NovaGenesis Applied to Information-Centric, Service-Defined, Trustable IoT/WSAN Control Plane and Spectrum Management

Abstract

We integrate, for the first time in the literature, the following ingredients to deal with emerging dynamic spectrum management (DSM) problem in heterogeneous wireless sensors and actuators networks (WSANs), Internet of things (IoT) and Wi-Fi: (i) named-based routing to provide provenance and location-independent access to control plane; (ii) temporary storage of control data for efficient and cohesive control dissemination, as well as asynchronous communication between software-controllers and devices; (iii) contract-based control to improve trust-ability of actions; (iv) service-defined configuration of wireless devices, approximating their configurations to real services needs. The work is implemented using NovaGenesis architecture and a proof-of-concept is evaluated in a real scenario, demonstrating our approach to automate radio frequency channel optimization in Wi-Fi and IEEE 802.15.4 networks in the 2.4 GHz bands. An integrated cognitive radio system provides the dual-mode best channel indications for novel DSM services in NovaGenesis. By reconfiguring Wi-Fi/IoT devices to best channels, the proposed solution more than doubles the network throughput, when compared to the case of mutual interference. Therefore, environments equipped with the proposal provide enhanced performance to their users.

Full text

sensors
Article
NovaGenesis Applied to Information-Centric,
Service-Defined, Trustable IoT/WSAN Control
Plane and Spectrum Management
Antônio Marcos Alberti 1,* , Marília Martins Bontempo 1, José Rodrigo dos Santos 1,
Arismar Cerqueira Sodré, Jr. 2and Rodrigo da Rosa Righi 3
1ICT Lab, Instituto Nacional de Telecomunicações—Inatel, Av. João de Camargo 510, Centro,
CEP 37540-000 Santa Rita do Sapucaí, Minas Gerais, Brazil; mariliamartins@gee.inatel.br (M.M.B.);
joserodrigo@gec.inatel.br (J.R.d.S.)
2Lab WOCA, Instituto Nacional de Telecomunicações—Inatel, Av. João de Camargo 510, Centro,
CEP 37540-000 Santa Rita do Sapucaí, Minas Gerais, Brazil; arismar@inatel.br
3Programa Interdisciplinar de Pós-Graduação em Computação Aplicada,
Universidade do Vale do Rio dos Sinos—Unisinos, Av. Unisinos 950, Bairro Cristo Rei,
CEP 93022-750 São Leopoldo, Rio Grande do Sul, Brazil; rrrighi@unisinos.br
*Correspondence: alberti@inatel.br; Tel.: +55-35-3471-9218
Received: 28 July 2018; Accepted: 1 September 2018; Published: 19 September 2018


Abstract:
We integrate, for the first time in the literature, the following ingredients to deal with
emerging dynamic spectrum management (DSM) problem in heterogeneous wireless sensors and
actuators networks (WSANs), Internet of things (IoT) and Wi-Fi: (i) named-based routing to provide
provenance and location-independent access to control plane; (ii) temporary storage of control data
for efficient and cohesive control dissemination, as well as asynchronous communication between
software-controllers and devices; (iii) contract-based control to improve trust-ability of actions;
(iv) service-defined configuration of wireless devices, approximating their configurations to real
services needs. The work is implemented using NovaGenesis architecture and a proof-of-concept
is evaluated in a real scenario, demonstrating our approach to automate radio frequency channel
optimization in Wi-Fi and IEEE 802.15.4 networks in the 2.4 GHz bands. An integrated cognitive
radio system provides the dual-mode best channel indications for novel DSM services in
NovaGenesis. By reconfiguring Wi-Fi/IoT devices to best channels, the proposed solution more
than doubles the network throughput, when compared to the case of mutual interference. Therefore,
environments equipped with the proposal provide enhanced performance to their users.
Keywords:
cognitive radio; future Internet; information-centric network; IoT; NovaGenesis;
service-oriented architecture; software-defined network; spectrum management
1. Introduction
The technology evolution has been based on diverse disruptive technologies, which now start
to converge towards deeply integrating physical and virtual worlds; a trend that has been called
digital transformation [
1
]. Disruptions are emerging in connectivity, controllability, virtualization,
expressiveness, content distribution and resources management. The Internet of Things (IoT) [
2
] is an
example of disruption that has been covering connectivity, expressiveness and resources management.
Expressiveness can be defined as the ability to express meaning [
3
]. Connected sensors provide all
kind of physical world data, generating context for services and applications. Connected actuators
reflect back to physical world decisions made at software level. Things’ representatives (smart objects)
act as proxies, representing physical world devices towards service level dynamic composition [4].
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Sensors 2018,18, 3160 2 of 35
Other important disruptions are related to the increasing role of software in information
architectures. Software-defined network (SDN) [
5
] and network function virtualization (NFV) [
6
]
have been revolutionizing telecommunications network design, including the fifth generation of
mobile communications (5G) [
7
,
8
]. SDN improves network programmability [
9
], enabling slicing
of the physical network devices. NFV supports virtualization of network functions that typically
are implemented in hardware. Placement and chaining of virtual network functions (VNFs) create
unprecedented levels of architectural flexibility. Multi-access edge computing (MEC) [
10
], fog and
cloud computing can be combined to address VNFs optimal placement. By combining VNFs
orchestration with SDN controllability, 5G architectures reflect service and application needs directly
in physical device configurations, creating a disruption in the way that physical resources are
managed. The work in 5G has been largely inspired/guided by previous work in future Internet (FI)
research [
11
,
12
]. Future Internet means any Internet-like network that could emerge in the future.
For instance, the outputs of FI public-private partnership (PPP) [
13
] have been fostering the 5G-PPP [
14
]
initiative in Europe.
Disruption is also present in content distribution: information-centric networking (ICN) [
15
–
19
]
replaces traditional host centric paradigm with efficient, coherent, secure and integral distribution of
named contents. Packets identify the desired content instead of destination hosts. What matters
are content names rather than locations. Services directly access contents/data by their names.
Expressiveness is increased with named-content forwarding/routing [
16
]. Network caching can also
be considered another disruption in this scope. ICN application to IoT management and control
is a promising approach [
20
], since ICN provides in-network storage of control/management data.
Access to the closest copy of a command is possible, as well as asynchronous interaction between
controllers and controlled devices. Self-verifying names (SVNes) [
21
] can be applied to control plane,
allowing provenance [22] and integrity check [23] in multi-controller networks.
The application of these disruptive technologies in smart environments will inevitably squeeze
into the limited radio frequency spectrum of unlicensed bands, while providing devices, things and
people connectivity. According to Haykin [
24
], the current static spectrum management policies are
responsible for its poor use, leaving few bands for unlicensed use, challenging the coexistence of
heterogeneous technologies in smart environments. Cognitive radio (CR) [
25
] can considerably increase
the efficiency of radio frequency spectrum by performing a dynamic spectrum management (DSM) [
24
].
The dynamic assignment of spectrum holes (or free frequency channels) in a fair and efficient manner
is a powerful tool to deal with congested frequency bands, reducing interference and improving
throughput. Therefore, integrated, trustable and secure DSM for IoT, Wi-Fi and other technologies
for unlicensed bands is extremely important for the current and future telecommunications networks.
Technological solutions for this context must take advantage of the disruptive technologies mentioned
above. To the best of our knowledge, the literature still does not present a unified combination
of ICN, SOA, SDN, ID/Loc splitting and cognitive radio in favor to improve DSM on IoT
environments [16–20,23,26–32].
In this article, we explore the benefits of integrating these disruptive technologies for
DSM of WSANs and IoT networks. We advance state-of-the-art by leveraging: (i) named-based
routing of control data to provide provenance and location-independent access to DSM controls;
(ii) temporary storage of control data for efficient and cohesive control dissemination and asynchronous
communication between software-controllers and controlled devices; (iii) contract-based control plane
to improve trust-ability of control actions; (iv) service-defined configuration of wireless devices,
approximating devices configurations to real services needs. These features bring fresh air to control
plane, confronting the problems of confidence lack, security, reliability, consistency, provenance and
integrity of control actions in WSANs and IoT networks [23].
Our approach for co-existence of wireless networks in the 2.4 GHz ISM band adopts a convergent
design space (presented in Table 1) that integrates IoT, ICN, FI and 5G ingredients. The work is performed
in the context of a FI/5G disruptive architecture called NovaGenesis (NG) [
3
,
4
,
33
]. NG project started

Sensors 2018,18, 3160 3 of 35
in 2008 and includes support for several architectural ingredients, typically addressed in a standalone
fashion. NG provides a complete architecture that covers ICN [
15
–
17
], SDN [
5
], service-oriented
architecture (SOA) [
34
], service-centric networking (SCN) [
35
], NFV [
6
,
31
], self-verifying naming [
3
,
16
,
21
],
distributed name resolution and identifier/locator (ID/Loc) splitting [36].
The article represents an extension of our previous work on cognitive radio for IoT [
33
], adding
Wi-Fi and IEEE 802.15.4 channel control based on energy detection. A proof-of-concept has been
experimentally performed, demonstrating throughput improvement after channel changing in a
mobile environment, in which Wi-Fi interferes in IEEE 802.15.4 and vice-versa. A contract-based,
named-data and software-defined approach is employed for IoT/Wi-Fi coexistence in the 2.4 GHz ISM
band for the first time. The focus is on the control plane rather than previous work [
33
] that was mainly
interested in ICN for data plane. To the best of our knowledge, this is the first effort for applying
ICN in the control plane. In summary, the article objective is threefold: (i) to advance DSM for the
co-existence of IoT IEEE 802.15.4 nodes and Wi-Fi access points in an ISM band; (ii) demonstration
of NovaGenesis viability in a real scenario for integrated IoT/Wi-Fi spectrum management of smart
places; (iii) demonstration of NG as an alternative to the current spectrum management architectures.
Our main contributions are the following:
•
Integration of aforementioned key ingredients usually separately found in IoT/FI/5G
architectures. As it will be demonstrated latter, this work goes deeply than our previous work [
33
]
when integrating the design dimensions, as reported in Table 1.
•
Name-based routing and self-verifying naming for provenance and integrity of the best channel
indications [
23
]. For the first time, name-based routing of the best channel indications is
demonstrated in laboratory. In contrast to other works [
20
,
22
,
26
,
27
,
37
–
39
], results have been
obtained in a field-trial experiments.
• “Semantic rich” orchestration of DSM services. NG provides naming support to foster trustable
exposition and discovery of spectrum sensing, optimization and reconfiguration services.
Dynamic composition is provided via publish/subscribe of name bindings.
•
Configuration of IoT/Wi-Fi devices accordingly to the service contracts. IoT and Wi-Fi devices
operate at channels defined by the umbrella of services contracts. This is more generic than
only configuring traffic flow tables as in traditional OpenFlow-based SDN [
5
]. In the current
SDN approaches, neither controllers are seen as services, nor contracts are established with
services to reflect their real needs beyond traffic flow configuration. We propose a service-defined
architecture (SDA), in which services and controllers establish dynamic contracts in the control
plane, making devices configuration a direct reflex of services needs. In other words, our SDN
model allows services to directly contract controllers to change physical device configurations.
•
Network caching of spectrum data (at control plane) to improve scalability and efficiency of
best channel indications for services. This solution allows asynchronous access to spectrum
data, as well as trustable sharing of spectrum indications among DSM services. This solution is
promising for scenarios with multiple spectrum sensors/best channel indicators and devices to
be controlled.
In summary, this work aims at providing significant bit rate increase in IEEE 802.15.4 and Wi-Fi
networks operating in unlicensed bands. The ICN-based proposal offers a spectrum management
solution for IoT/Wi-Fi, using a named data network to switch from the current TCP/IP technologies
to future Internet ones. The remainder of this manuscript is structured in other six sections.
Section 2presents a revision on the state-of-the-art in Section. In Section 3, we propose a cognitive radio
system for the integrated spectrum sensing in the 2.4 GHz band, including its hardware and firmware
modules. This cognitive radio system is latter integrated to NovaGenesis architecture. Section 4describes
the basics of NovaGenesis architecture. We also detail our main contribution: an extension to our
previous work [
33
] to offer a set of trustable, named-control-data and contract-based dynamic spectrum
management services for IoT/Wi-Fi devices. In Section 5, a real scenario is evaluated and experimental

Sensors 2018,18, 3160 4 of 35
results are analyzed. Section 6enumerates our proposal contributions, benefits and open issues for the
control plane of new generation WSANs and IoT. Finally, Section 7points our conclusions.
Table 1. Architecture design dimensions considered in this article.
Dimension Description
D1 Dynamic cognitive radio-based spectrum management in licensed/unlicensed spectrum bands.
D2 Secure exchange of IoT control data via trustable services.
D3 Name-based access and routing of control data (spectrum sensing), including network caching.
D4 Software-defined control and operation.
D5 Dynamic composition of control services based on semantic and context-awareness, including
complete service life-cycling.
D6 Improved support for architecture data and entities naming and name resolution.
D7 Identifier and locator splitting, meaning different names are used for identifying and locating data
and services.
D8 Contract-based operation of control plane services.
2. Related Work
Previous works related to the D1-D8 design dimensions are listed in Table 2. We have restricted our
selection to the state-of-the-art manuscripts related to next generation technologies of WSANs and IoT.
The hot topics and keywords presented in Table 1have been used to select the best fits. As can be seen
in Table 2, the majority of selected papers is typically focused on one or two dimensions. For instance,
articles that cover cognitive radio-based DSM (D1 and D2) do not cover emerging technologies like
ICN (D3 and D6), SDN (D4), SOA (D5 and D8), or identifier/locator splitting (D7). In this context,
our methodology was selecting papers that cover as many as possible of these design dimensions.

Sensors 2018,18, 3160 5 of 35
Table 2.
Related work on the next generation WSANs and IoT networks. Comparison with respect to: D1—Dynamic spectrum management with cognitive radio;
D2—Secure exchange of control data via trustable services; D3—Named-control-data access and routing; D4—Software-defined control and operation; D5—Dynamic
composition of control services; D6—Improved naming and name resolution for IoT; D7—Identifier/locator splitting for architecture entities; D8—Contract-based
control plane.
Dimensions
Previous Work D1 D2 D3 D4 D5 D6 D7 D8
Energy Harvesting Cognitive Radio Networking for IoT-enabled Smart Grid [40] x
A Secure IoT Management Architecture based on Information-Centric Networking [20] x x x x x
LASeR: Lightweight Authentication and Secured Routing for NDN IoT in Smart Cities [22] x x
Spectrum Management for Proactive Video Caching in Information-Centric Cognitive Radio Networks [26] x x x x
Spectrum-Availability based Routing for Cognitive Sensor Networks [41] x
A Case for ICN Usage in IoT Environments [27] x x
A Comparative Study of MobilityFirst and NDN based ICN-IoT Architectures [28] x x
A De-verticalizing Middleware for IoT Systems Based on Information Centric Networking Design [42] x x
A Distributed ICN-based IoT Network Architecture: An Ambient Assisted Living Application Case Study [43] x x x
A Robust and Lightweight Name Resolution Approach for IoT Data in ICN [44] x x
A Secure ICN-IoT Architecture [23] x x x
A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks [37] x x
A Cloud-Based Internet of Things Platform for Ambient Assisted Living [45] x
Coexistence of Wi-Fi and Heterogeneous Small Cell Networks Sharing Unlicensed Spectrum [46] x
Cognitive Radio-Enabled Internet of Vehicles: a Cooperative Spectrum Sensing and Allocation for Vehicular
Communication [47]x
Consumer Oriented IoT Data Discovery and Retrieval in Information Centric Networks [48] x x
CORAL-SDN: A Software-Defined Networking Solution for the Internet of Things [49] x
Cross-Technology Wireless Experimentation: Improving 802.11 and 802.15.4e Coexistence [50] x x
Development of Measurement Techniques and Tools for Coexistence Testing of Wireless Medical Devices [51] x x
Distributed Channel Allocation and Time Slot Optimization for Green Internet of Things [52] x
Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs [53] x
Efficient Methods of Radio Channel Access using Dynamic Spectrum Access that Influences SOA Services Realization –
Experimental Results [54]x
Energy-Efficient Channel Handoff for Sensor Network-Assisted Cognitive Radio Network [39] x
Experimental Study of Coexistence Issues Between IEEE 802.11b and IEEE 802.15.4 Wireless Networks [55] x
Adaptive Radio Channel Allocation for Supporting Coexistence of 802.15.4 and 802.11b [56] x x
The SDN Approach for the Aggregation/Disaggregation of Sensor Data [9] x
Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks [57] x
ISI: Integrate Sensor Networks to Internet with ICN [58] x x
Software-Defined Network Virtualization: An Architectural Framework for Integrating SDN and NFV for Service
Provisioning in Future Networks [59]x x x

Sensors 2018,18, 3160 6 of 35
Table 2. Cont.
Dimensions
Previous Work D1 D2 D3 D4 D5 D6 D7 D8
Networking Named Content [18] x x x
A Survey of Information-Centric Networking [19] x x x
A Survey of Information-Centric Networking Research [29] x x x
Named Data Networking [30] x x x
Efficient Proactive Caching for Supporting Seamless Mobility [60] x x
Efficient Information Lookup for the Internet of Things [61] x x
Cloud Computing for Global Name-Resolution in Information-Centric Networks [62] x x
Network of Information (NetInf) - An Information-centric Networking Architecture [17] x x x
XIA: Efficient Support for Evolvable Internetworking [16] x x x x
Prototyping the Recursive Internet Architecture: the IRATI Project Approach [31] xx xxxx
Developing information networking further: From PSIRP to PURSUIT [32] x x x

Sensors 2018,18, 3160 7 of 35
The emergence of IoT and its perspective of providing connectivity for a very large quantity
of devices [
63
] makes device/standard heterogeneity an important rule. Interoperability has been
provided at software level. New technology coexistence problems have emerged and become largely
studied. They include electromagnetic interference mitigation, dynamic spectrum management and
opportunistic spectrum access.
The authors of [
39
–
41
,
47
,
51
,
56
] propose cognitive radio implementations for IoT, addressing
channels keying with different techniques; not always related to the cognitive sensing technique,
but employing multiple access protocol, multi-hop and others. In [
40
], cognitive radios were employed
for IoT-enabled smart grids. The convergence of spectrum-aware communications and energy
harvesting is explored to: (i) deal with collisions and interference in ISM band; (ii) rectify RF signals to
feed IoT nodes. A ultra-low power unit perform channel sensing and switching in the nodes. In [
47
],
cognitive radio technology is applied for vehicular communication in a licensed band. Opportunistic
channels are allocated to vehicles as they move. The coexistence of medical wireless devices was
investigated in [
51
]. A framework for wireless devices coexistence testing based on energy detection
was proposed. Ding et al. [
52
] evaluated a distributed joint channel allocation and time slot access
control solution for IoT devices, concerned to optimizing residual nodes energy. In [
46
], the coexistence
of Wi-Fi and 4G in unlicensed spectrum was studied. Co-channel interference mitigation from small
cells to Wi-Fi is provided, whereas in in [
50
] coordination of heterogeneous wireless sensor network
technologies (Wi-Fi and IEEE 802.15.4) was investigated. The proposed WiSHFUL control framework
addresses interference mitigation (D1) using an protocol-independent unified programming interface
(UPI) for software-defined devices monitoring and configuration (D4).
In [
55
], authors reported an experimental performance evaluation of IEEE 802.11 and IEEE
802.15.4 standards, when sharing the same geographical space. In [
26
,
38
], SDRs were employed to
have a more flexible network with quality of service (QoS). References [
38
,
53
,
54
,
64
] are related to
SOA. Services designed to control the system operation and other functionalities aim to improve
its performance, like the spectrum sensing service proposed in [
53
]. In [
53
], IoT devices with
cognitive radio capabilities (D1) cooperatively optimize spectrum channels. Simulation and numerical
analyses were employed to demonstrate minimal interference with licensed mobile networks. In [
39
],
cognitive radio is employed for wireless sensor network energy-efficient channel handoff.
In [
18
], Jacobson et al. proposed content-centric networking (CCN) paradigm covering dimensions
D3, D6 and D7. In CCN, pending interest tables (PITs) handle interest packets forwarded upstream to
possible content sources in order to route back data packets to requesters. Zhang et al. [
30
] revisited
CCN and proposed an enhanced version called named data networking (NDN). NDN also follows the
same forwarding model, but proposes some improvements regarding delay. The surveys [
19
,
29
] cover
many proposals also related to D3, D6 and D7. Network of information (NetInf) is one example [
17
]
of a key component of the scalable and adaptive Internet solutions (SAIL) architecture. NetInf relays
in named data objects (NDOs) to give access to content by its name, i.e., data is located during
final transfer phase. Network caching is employed to store NDOs closely to the interested peers,
maximizing content distribution efficiency and robustness. The NetInf nodes offer three services:
(i) forwarding of requests for NODs to the closest caching/storage nodes; (ii) data transfer to the
requesters; (iii) a name resolution service (NRS) that can be used to locate possible content sources.
NetInf covers publishing, searching and subscription for NDOs. NetInf covers design dimensions
D3, D6 and D7, clearly demonstrating there is a gap related to the other design dimensions while
applying ICN paradigm. Still related to FI and ICN, the publish subscribe Internet technology
(PURSUIT) architecture employed a flat naming structure (D6) to support content subscriptions from
a publisher/subscriber rendezvous function at network nodes [
32
]. Publisher mobility is supported
by a topology manager that decouples IDs from locators (D7). PURSUIT can support the secure
exchange of control data, but there is no support for the dynamic composition of trustable services.
Katsaros et al. [
65
] studied data-oriented architecture (DONA) [
66
] and context-ubiquitous resolution
and delivery infrastructure for next generation services (CURLING) [
67
] route-by-name scalability
and performance for hierarchical networking caching. The effects of inter-domain topologies in ICN

Sensors 2018,18, 3160 8 of 35
performance have been evaluated, pointing this issue as important to be considered when extending
ICN for hierarchical domains. Another important aspect related to ICN is proactive caching of contents
accordingly to user mobility. Siris et al. [
68
] proposed an efficient mechanism to reduce delay when
mobile devices employ ICN paradigm, which is an important problem for the convergence of ICN,
IoT, 5G and cognitive radio. The proposed solution exploits mobility and congestion information to
improve cache storage performance.
Additionally, we selected studies that tackles ICN benefits for IoT. The authors of [
20
,
23
,
26
–
28
,
42
–
44
]
addressed the application of ICNs to IoT. In [
20
], content-centric network (CCN) was employed for
management of IoT networks. Name resolution (D6) and name-based, secure routing (D3) was used for
IoT devices key distribution, registration, discovery and command execution (D2 and D4). They have
also considered service level agreement (SLA) for management services (D8). CCN offers content
identifier/locator splitting (D8). In contrast, our work employs ICN for wireless spectrum management
(D1). Another difference is that we adopt service names for dynamic composition of IoT/Wi-Fi spectrum
management services (D5).
In [
22
], motes authentication (D2) and IoT data name-based routing (D3) were explored in the
context of the named-data network (NDN) architecture. Scalability and efficiency were evaluated using
ns-3 simulations. In [
27
], a simulation-based comparison of Internet protocol (IP) and CCN (D3 and
D6) for supporting IoT data plane was provided in terms of energy consumption and bandwidth
usage. Results indicated CCN consumes less energy and bandwidth than IP. In [
28
], two ICNs were
evaluated in the context of IoT: MobilityFirst [
69
] and NDN. The study covered devices discovery and
IoT data publish/subscribe. Two smart campus applications were evaluated with simulation.
In [
42
], an ICN-based middleware for IoT was proposed. The proposal covered IoT devices
naming, exposition and discovery. Name-based IoT data dissemination was evaluated in a real
scenario. Nour et al. [
43
] enhanced NDN architecture towards devices naming (D6), management (D2),
mobility (D7) and handoff. Simulation results contended on proposal’s scalability and efficiency.
Another paper related to ICN name and name resolution in IoT context was proposed by
Dong et al. [
44
], which covered devices attachment and failures. In [
23
], security of ICN-IoT (D2,
D3 and D6) was investigated. The work is related to the ICN research group (ICNRG) of Internet
research task force (IRTF), which investigate ICN application in the context of IoT. An ICN-IoT
middleware was proposed to provide service (D5 - partially), device and content (D3) discovery.
A secure naming service (D6) was also covered. In [
48
], authors extended NDN to reduce IoT data
latency. Adhatarao et al. [
58
], employed CCN-Lite for IoT nodes and gateways. The work addressed
naming in IoT, publish/subscribe communication model, security and mobility.
The convergence of ICN (D3 and D6) and CR (D1) has been explored in a very few works.
Si et al. [
26
] addressed efficient video distribution in this context. Mathematical modeling and
simulation were employed. Although the concern was to improve spectrum efficiency, the work
has no relation to D4.
Another convergence is spectrum management (D1) and SDN (D4). In [
37
], an enhanced
distributed channel access (EDCA) technique was integrated to an SDN controller. Access points
and mobile devices configured channels accordingly to a SDN controller. CORAL-SDN [
49
] was
embedded (D4) inside IoT nodes to offer low power wireless communication and media access control
in IEEE 802.15.4 2.4 GHz networks. A modified controller offered topology control, routing and flow
establishment, and data collection. Spectrum management was left out of the scope. In [
9
], Lin et al.
proposed a programmable P4 switch that provided aggregation/disaggregation of IoT data.
The integration of SDN and NFV is relevant for convergent architectures. Although independent,
the synergistic integration of SDN and NFV can take advantage of both disruptions simultaneously.
Duan et al. [
59
] proposed an integrative approach called software-defined network virtualization
(SDNV). This approach provided not only separation of data and control planes, but also decoupling
of service functions and infrastructure. A service-oriented control/management (D2, D4 and D5) plane
was provided to allow independent coordination of physical (infrastructure) and virtual (service-based)
resources. Duan et al. [
59
] contended that unified abstractions are required to expose heterogeneous

Sensors 2018,18, 3160 9 of 35
resources to software layer, e.g. information processing, networking and storage. However, the SDNV
proposal did not covered this issue. Coherence of coexisting software-controllers (control plane) is still
pointed as an open issue. Service-oriented proposals (D5) are also relevant for next generation IoT and
smart places. They are typically related to cloud or fog computing. In [
45
], a service-oriented platform
for IoT devices integration and behavior-aware coordination was proposed. The work employed the
devices profile for web services (DPWS) standard to support heterogeneous IoT devices, including
IEEE 802.15.4. Discovery and control services were developed (D5), but none related to coexistence of
RF signals. In [
57
], a SOA-based middleware for wireless sensor networks addressed services security
(D2 and D4), dynamic composition (D5), data aggregation and QoS challenges of smart environments.
In [
62
], a data oriented networking architecture (DONA) based on cloud computing was proposed.
It addresses cloud orchestration for ICN, covering the challenges behind IoT and its control plane.
A novel naming structure for identifying and locating IoT data and services (D7) is discussed in [
61
].
Different rules for controlling information distribution are proposed and organized into hierarchical
caches. A user that desires to obtain an information asks for the scope corresponding to a certain context,
i.e., it can access IoT information by their names. In [
60
] , an example of hierarchical network caching
(HNC) is provided, as well as a mechanism for mobility-based data search. These works address the
important issue of ICN-based hierarchical caching performance for IoT.
Regarding future Internet, the eXpressive Internet architecture (XIA) is a FI project funded by
the USA national science foundation (NSF) [
16
]. Its key concepts are: (i) employs global unique
identifiers for networking principals, i.e., administrative domains, hosts, services, and contents;
(ii) allows services to express intent via unique IDs; (iii) flexible addressing; (iv) iterative refinement
of forwarding; (v) intrinsic security. Self-certifying identifiers (XIDs) are calculated by hashing the
public cryptographic keys of domains, hosts and services or the entire binary pattern of named-data.
Still related to FI, recursive Internetwork architecture (RINA) is a clean slate architecture founded
on the interprocess communication (IPC) abstraction [
31
]. RINA adopted a distributed IPC facility
(DIF) to allow dynamic stack creation based on customized instances of recursive layers. In other
words, DIFs have the same interface and structure, independently of their level in the protocol stack.
IPCs can implement virtual network functions. RINA also has an inter-DIF directory (IDD) for name
resolution (D3 and D6) and ID/Loc splitting (D7). An error and flow control protocol (EFCP) provides
secure exchange of control data (D2). A common application connection establishment phase (CACEP)
is employed for establishing application connections (D8), allowing applications to discover (D5)
possible peer names. To the best of our knowledge, neither XIA, nor RINA have been applied for
cognitive radio and DSM yet.
In summary, related work individually deal with the challenges of next generation IoT and
wireless network demands, providing mathematical models and/or simulations, with a few real world
implementations, addressing specific design dimensions of Table 1. The majority of cognitive radio
proposals do not address other design dimensions. This means they do not form trustable networks
of control services, if possible implications in security. Moreover, they do not exchange control
data by their names, lacking on provenance and integrity check of control actions [
22
]. Name-based
routing is also unavailable, making control actions dependent on node’s addresses and locations.
Control plane is not software-defined, lacking in flexibility and evolvability. Control services are
not dynamically composed, difficulting extensibility and requiring frequently manual intervention.
Naming support is limited to the current Internet technologies, difficulting expressiveness [
16
,
45
].
Devices, services and data identifiers are coupled with locators, which creates identity loss and
traceability problems while moving. Control plane is not contract-based, meaning all control services
do not follow pre-established quality requirements. ICN proposals address some of these limitations,
but do not incorporate cognitive radio (or DSM) or SOA benefits. Suarez et al. [
20
], applied ICN for
IoT management, but have not covered cognitive radio or SOA. In summary, there is a gap in the
current researches to fully address design dimensions listed in Table 1to automatic switch wireless
devices’ channels (not only for Wi-Fi, but also for IEEE 802.15.4) using ICN, SOA, SDN and cognitive
radio, while optimizing network throughput.

Sensors 2018,18, 3160 10 of 35
3. Cognitive Radio System
This section contributes with a protocol-agnostic ISM band cognitive radio system to dynamically
determine the best channels in the Wi-Fi/IEEE 802.15.4 networks. It measures energy level for either
IEEE 802.11 and IEEE 802.15.4 over the 2.4 GHz frequency band. Its objective is to increase throughput
when IoT and legacy wireless network coexist. This CR system can interoperate with or without
NovaGenesis architecture, as will be described in Sections 4–6, respectively. This dual approach
enables us to compare both solutions, contrasting the novelties behind NovaGenesis design.
3.1. Hardware
The implemented system is composed by a sensing cell module and a wireless device network.
Figure 1shows the sensing cell hardware constitution, which uses an open source software defined
radio, called HackRF One
TM
to sense the frequency spectrum and a laptop with middleware to process
signals and perform SDR control. The HackRF One operates from 1 MHz to 6 GHz and has 20 MHz
bandwidth, which may be controlled through a middleware/software in any hardware with more at
least 1 GHz of processing clock. Our laptop has an Intel(R) Core
TM
i7 processor, with a 1.80 GHz clock
rate. The IoT network considered in this experiment operates in the IEEE 802.15.4 communication
standard. To evaluate the interference caused by other wireless devices, which usually coexist with
IoT nodes, an IEEE 802.11 network is additionally deployed. The IoT 802.15.4 network is constituted
by a SMARTRF06EBK as the network border router and Texas Instruments cc2650 motes. The Wi-Fi
network uses a TP-Link N750 OpenWRT router and two laptops communicating via TCP/IP socket to
create data plane traffic. In other words, our novel approach operates in the control plane, optimizing
coexistence of traditional technologies for IoT.
Figure 1. Hardware used to run the sensing cell module and the channel advisor (CA) firmware.
3.2. Firmware
For the sake of clarity, we divided firmware component in two parts: (i) the algorithm for the best
channel estimation; (ii) the employed spectrum sensing method.
3.2.1. The Best Channel Estimation Algorithm
The main script of the spectrum sensing system, named as Channel Advisor, which starts with
the indication of the link layer technology to be sensoried (Wi-Fi or IEEE 802.15.4) coming from
NovaGenesis, when it is employed or automatically (without NG benefits) when it is not present.
The message is received through a zero-em-queue (ZMQ) socket (TCP/IP) and indicates whether the
spectrum sensing service should be ON/OFF; when it is ON, the wireless communication standard

Sensors 2018,18, 3160 11 of 35
being sensoried. As it will be described latter in Section 4.9.1, a few characters (“ON 802.15.4”) are
send by NovaGenesis spectrum sensing service (SSS) to turn ON channel indications and inform the
frequency bands of the technology to be scanned.
Figure 2describes the employed GNU Radio sensing algorithm flowchart. Once the main script
receives the first NG message, it initiates the firmware to control the SDR. The firmware interacts with
a GNU Radio instance, which is a set of open source tools for SDR implementations. We developed a
GNU Radio algorithm (implemented in a set of communication blocks) that transform the sensed data
stream into a 1024 positions vector. In the next step, the vector is converted to the frequency domain
through a FFT block. Later, the module of the generated data stream is calculated and converted from
the voltage scale to the energy scale.
Figure 2. The GNU Radio sensing algorithm flowchart.
Figure 3illustrates the flowchart of the Channel Advisor firmware. It is important to mention
since the HackRF One has a bandwidth of 20 MHz and the 802.11 channels have 22 MHz, the GNU
Radio sensing flowchart is repeated two times for each 802.11 channel. Therefore, the script can sense
11 MHz each time and all the channels are covered. Sensing the 802.15.4 standard is not a problem,
since its channels have a 5 MHz bandwidth. The energy samples of each channel are stored into binary
files. After sensing all channels, the main script calculates the channel energy and chooses the channel
with less energy collected from other coexisting devices. Posterior, the Channel Advisor inform the
best channel to NovaGenesis, as will be described in Section 4.
Version September 1, 2018 submitted to Sensors 10 of 34
11 MHz each time and all the channels are covered. Sensing the 802.15.4 standard is not a problem,339
since its channels have a 5 MHz bandwidth. The energy samples of each channel are stored into340
binary files. After sensing all channels, the main script calculates the channel energy and chooses341
the channel with less energy collected from other coexisting devices. Posterior, the Channel Advisor342
inform the best channel to NovaGenesis, as will be described in Section 4.343
3.2.2. Spectrum Sensing Method344
In cognitive radio, a typical hypothesis test represents the spectrum sensing [70]:345
(H0: Primary signal is absent
H1: Primary signal is present, (1)
where H0denotes there is no primary user in a particular frequency band, whereas H1represents346
that a specific band is occupied by a licensed user.347
One can measure the spectrum sensing performance through the probability of detection, PD,348
and probability of false alarm; PFA.PDand PFA are described as [70]:349
PD:Pr(H1|H1) = Pr(T>γ|H1)
PFA :Pr(H1|H0) = Pr(T>γ|H0),(2)
where Pr is the probability of an event; T is the test statistic of the spectrum sensing method and γ350
is the decision threshold. When T is above γ, the channel is taken as occupied, otherwise the channel351
is assumed to be free. Each spectrum sensing technique in a cognitive radio is able to calculate the352
test statistic T, as demonstrated by 2.353
Figure 3. The best channel advisor complete flowchart.
There are many techniques to set the test statistic T, including the matched filter, cyclostationary,354
energy detection and others. Considering the known methods, the energy detection is the only355
approach that does not demand total or partial knowledge of the signals to be detected, not even356
Figure 3. The best channel advisor complete flowchart.

Sensors 2018,18, 3160 12 of 35
3.2.2. Spectrum Sensing Method
In cognitive radio, a typical hypothesis test represents the spectrum sensing [70]:
(H0: Primary signal is absent
H1: Primary signal is present, (1)
where
H0
denotes there is no primary user in a particular frequency band, whereas
H1
represents that
a specific band is occupied by a licensed user.
One can measure the spectrum sensing performance through the probability of detection,
PD
,
and probability of false alarm; PFA.PDand PFA are described as [70]:
PD:Pr(H1|H1) = Pr(T>γ|H1)
PFA :Pr(H1|H0) = Pr(T>γ|H0),(2)
where Pr is the probability of an event; T is the test statistic of the spectrum sensing method and
γ
is
the decision threshold. When T is above
γ
, the channel is taken as occupied, otherwise the channel is
assumed to be free. Each spectrum sensing technique in a cognitive radio is able to calculate the test
statistic T, as demonstrated by Equation (2).
There are many techniques to set the test statistic T, including the matched filter, cyclostationary,
energy detection and others. Considering the known methods, the energy detection is the only
approach that does not demand total or partial knowledge of the signals to be detected, not even about
the communication channel [
71
]. This condition is fundamental to sense an IoT devices scenario with
different and unknown standards. For this reason, the energy detection method has been chosen as the
spectrum sensing technique to be implemented in our system.
In the energy detection method, the test statistic for N received samples is calculated by [72]:
T=1
N
N
∑
n=1X(n)2(3)
The sum of the measured samples potency module, according to 3, is performed as illustrated in
Figure 2. Since the noise in a real environment is dynamic, estimation errors could lower the technique
performance if the threshold decision
γ
is wrong. Besides that, with the huge amount of IoT connected
devices, it becomes possible that in a certain moment all sensed standard channels are occupied.
This work evaluates the benefits in a scenario of less interfered channel of a certain standard (Wi-Fi or
IEEE 802.15.4). Therefore, a threshold is not calculated, but the channel with less measured energy is
determined. This criterion is used for the execution of the flowchart as shown in Figure 3. Once the
system establishes the channel with less measured energy, it switches the network to the operation
at the new channel and the process can be repeated every time other channel offers less interference,
respecting the minimal difference calibrated to cause the exchange.
4. NovaGenesis
NovaGenesis (NG) [
3
,
33
] is an alternative information architecture that covers data processing,
exchanging and storage. The project has idealized an alternative architecture for the current Internet
and has been demonstrated viable for IoT [
4
] and cognitive radio [
33
]. NG has been evaluated as
an alternative to domain name service (DNS) [
3
], named-data routing and network caching [
73
]
(for efficient content distribution) and software-defined networks [
74
]. Due to its service-oriented
design (SOD) with support for virtualization, software-defined networking and virtual network
functions, it can be seen as a 5G architecture as well. In this context, NovaGenesis is a “clean slate”
architecture that cohesively integrates many novel ingredients at its core, including exposition of
physical things to software, programmable networks (SDN and NFV), service-defined architecture
(SDA), ICN, SOA, SCN, IoT, SON, among others.

Sensors 2018,18, 3160 13 of 35
4.1. Naming and Hierarchical Name Resolution
NovaGenesis allows for unlimited namespaces and provides distributed/hierarchical name
binding resolution. Namespaces can cover from natural language names (NLNes) up to self-verifying
names (SVNes). For instance, a SVN can be calculated from an IoT hardware serial number,
i.e., SVN = MD5(12080180972B) = b09d3a2e3aead0308403ecb0d6c6f9a4, in which MD5 is a hash
function. Moreover, NG supports devices NLNes, such as NLN = Mote 1, which are important for
human operators.
Besides flexible naming, NG allows name bindings (NBs) to represent entities relationships by
means of semantic operators, such “equivalent”, “is contained” or “contains”. For instance, a name
bind
<
Mote 1, b09d3a2e3aead0308403ecb0d6c6f9a4
>
stores that one existence have two equivalent
names, i.e., homonyms. However, a “contains” NB can represent that some entity inhabits another one.
For example, the NB
<
Domain 1, Mote 1
>
represents that the domain named Domain 1 contains a
Mote 1. NG allows also name bindings to be associated with the contents, i.e., data objects that are
distributedly stored in the network. In this case, a name “Sample1.json” can be associated with a
binary file containing a certain measure.
4.2. Hierarchical Network Caching
NovaGenesis adopts temporary caches in every domain to improve named-content distribution.
This approach is founded in ICN, in which contents are accessed by their names and popular
information is made available to future access in local cache. NG name resolution approach is integrated
to a network caching to form a name resolution and network caching service (NRNCS). By integrating
name resolution to content caching, NG facilitates content delivery by their names.
The current implementation of NRNCS adopts a publish/subscribe communication model,
in which services publish and/or subscribe name bindings (NBs) and associated contents (if any)
from the local domain cache. Therefore, NRNCS provides a rendezvous point analogous to message
queuing telemetry transport (MQTT) brokers.
Table 3. NovaGenesis concepts [3].
Concept Description
Name Symbols that denote an existence in natural language.
Identifier An unique name that unambiguously identify an existence in a certain scope.
Locator A name that denotes a certain position or point of attachment in a certain space,
giving notation of distance to other points in the same space.
Name Binding (NB) An entity that link names.
Process An instance of a computer program running in an operating system that has
Blocks and Actions internally.
Block An internal component of a Process that contains many Actions.
Action An internal component of a Block that implements its functioning.
Message The protocol data unit (PDU) for NovaGenesis information exchange.
CommandLine Each command line describes an Action to be executed at the destination and
its parameters.
Service The same than a Process.
Hash Table (HT) An instance (Block) that implements a hash table data structure.
Gateway (GW) A Block responsible to exchange messages inside a process.
Proxy/Gateway (PG) A Block responsible to exchange messages externally a process.
Hash Table Service (HTS) A distributed hash table build with HT Blocks.
Generic Indirection Resolution
Service (GIRS) Responsible to select the proper HTS to store name bindings and content.
Publish/Subscribe Service (PSS) Responsible for the rendezvous of publishers and subscribers.
Proxy/Gateway/Controller
Service (PGCS)
Encapsulation of messages for link layer transport, representative of things and
software-controller of their configurations.

Sensors 2018,18, 3160 14 of 35
NRNCS is implemented by three services: (i) publish/subscribe service (PSS) to which services
can publish or subscribe NBs/contents; (ii) generic indirection resolution service (GIRS) that selects
appropriate hash tables to store named data; (iii) a hash table service (HTS), which in fact stores NBs
and associated contents. Every NG domain must have at least one instance of each of these core
services. However, elasticity is provided by increasing the numbers of PSS/GIRS/HTS. To facilitate
understanding, a summary of NG terminology is shown on Table 3.
PSS forwards NG messages carrying name bindings and content for network caching. For this
purpose, it exposes an application programming interface (API), which offers the following primitives:
(i) NB and content publishing without notification of other services; (ii) NB and content publishing with
notification of interested services; (iii) NB and content subscription; (iv) NB and content subscription
with notification of publisher; (v) delivering of NB and content subscribed; (vi) revoking of published
NB and content, if any. In summary, PSS offers an API distributed over the network, allowing services
discovery and access based on naming.
4.3. Entities Life-Cycling: From Equipment, Operating Systems, Services up to Information
NG adopts SOA premises to perform everything-as-a-service (XaaS) dynamic composition.
Event protocol implementations in software are seen as services that establish contract to other services
(peer protocol implementations or applications). NG provides a rich service life-cycle with resources
(capabilities) exposition, discovery of possible peers and contents, contracting offers, negotiation and
SLA installation. Due to its unique naming and name resolution approach, NG enables name-based
dynamic service composition. In other words, NG integrates service-centric networking (SCN) and
ICN in an unique design. Not only access to information is name-based, but also access to services.
In fact, access to all entities is name-based as well as content routing and delivering. All these NG
features provide a service-defined, trustable IoT spectrum management approach.
4.4. Message Encapsulation over Link Layer
Although NG can be applied to link layer, the current implementation does not cover this feature.
As a consequence, NovaGenesis messages must be adapted for contemporary link layer technologies,
such as Ethernet, Wi-Fi, ZigBee, LoRa, etc. For this aim, a gateway service was designed. In the current
implementation, a convergent proxy/gateway/controller service (PGCS) was developed to represent
(as a proxy) ordinary things (and IoT nodes) inside NG service ecosystem. PGCS provides gateway
(protocol translation) functions to IoT protocols, such as ZigBee, IEEE 802.15.4, LoRa. Additionally to
message encapsulation, NG model requires a service to represent things at software layer. Currently,
this idea is named smart object. For this purpose, PGCS includes an internal proxy component which
represents things in NG life-cycle.
4.5. Layered Model
Even though NG allows for flexible protocol development and layering, the current implementation
can be represented in a protocol stack as illustrated in Figure 4. The model presents two computers
hosting NG. Each NovaGenesis service has several internal components to implement their functions.
These components are called blocks, which can be classified in two types: common and specialized.
The following blocks inhabit all services: gateway (GW) and hash table (HT). GW provides: (i) inter
block communication (IBC) inside a service; (ii) inter service communication (ISC) in an operating
system (OS); and (iii) event-driven dispatching of messages and callback of service actions. Inter
OS/host communication is done by a specialized block called proxy/gateway (PG), which implements
a convergence layer to provide message encapsulation, fragmentation and reassembly. PGCS, PSS,
GIRS and HTS offer a software implementation of the NovaGenesis layer. Finally, in the upper level of
the architecture an application layer takes advantage of all core services.

Sensors 2018,18, 3160 15 of 35
Server
PSS
Gateway
(GW)
Hash
Table
(HT)
Pub/Sub
(PS)
HTS
Gateway
(GW)
Hash
Table
(HT)
D. Hash
Table
(HT)
GIRS
Gateway
(GW)
Hash
Table
(HT)
Ind.
Resol.
(IR)
PGCS
Gateway
(GW)
Hash
Table
(HT)
Proxy/
Gateway
(PG)
Raw socket
Shared Memory
PGCS
\
Gateway
(GW)
Hash
Table
(HT)
Proxy/
Gateway
(PG)
Raw socket
Shm
App
Gateway
(GW)
Hash
Table
(HT)
Core
OS
Host 2
App
Gateway
(GW)
Hash
Table
(HT)
Core
Ethernet
NRNCS - NAME RESOLUTION AND
NETWORK CACHE SERVICE
•UNLIMITED NAMESPACES
•NATURAL LANGUAGE AND SELF-VERIFYING
NAME BINDINGS
•HIERARCHICAL NAME RESOLUTION
•CONTENT CACHING
PROXY/GATEWAY/
CONTROLLER SERVICE
•THINGS’
REPRESENTATIVES
•PROTOCOL
TRANSLATION
•MESSAGE
ENCAPSULATION
•SOFTWARE-CONTROL
OF THINGS
•CONVERGENCE LAYER
SHARED MEMORY
FOR INTRA OS
COMMUNICATION
RAW SOCKET FOR INTER
HOST COMMUNICATION
1
2
3
4
5
6
8
9
12
13
11,14
7,10
16
15
NovaGenesis Layer Application
Layer
Convergence
Layer
Link
Layer
OS
Figure 4.
A layered model of the NovaGenesis architecture in a local domain. The convergence layer
adapts NG messages to be transported in current link layer technologies. The NG layer comprehends
core NG protocols that support application layer via a PSS application programming interface (API).
NovaGenesis dynamic stack enables customization of the protocols required for a certain application.
Therefore, overhead of unnecessary protocols can be avoided. The convergence layer provides message
encapsulation, fragmentation and reassembly. The NG layer provides name-based message forwarding,
content delivery and storage. We have already embedded a simplified version of this stack for IoT [
4
].
4.6. Software-Defined Networking Model
PGCS can also control things, self-configuring them as required, based on dynamic established
contracts (Figure 5). PGCS was envisioned as a controller service, similar to a SDN controller, but with
responsibilities larger than frame forwarding control. PGCS provides programmability of connected
things, sensors, actuators and switches. It can change general configurations in represented devices.
In an OpenFlow-based SDN, software-controllers employ OF protocol to configure flow tables in
compatible switches. OF protocol is employed in the south bound, between controller and switches.
In the north bound, REST is typically employed, e.g., in OpenDaylight and open network operating
system (ONOS). Figure 5provides a comparison between OF and NovaGenesis SDN models.
NG does not limit the scope of programmability to flow tables. It employs services life-cycling
to dynamically compose control plane via service contracts. NG applies the same protocols at north
and south bounds. PGCS represents things to “sell” their capacities via NG named-based, semantic
rich service orchestration. PGCS configures things accordingly to high level service and application
needs, creating a service-define architecture (SDA) [
4
]. When applied for IoT spectrum management,
PGCS can represent IoT nodes to sell their measurement capabilities to control applications, such as a
RMS instance. In addition, it can change the RF channel being used by the IoT node to communicate.
PGCS can also change flow tables in a switch or access points in a similar way to OpenFlow, but using
NG publish/subscribe communication model. It can also interoperate with OF controllers [74].

Sensors 2018,18, 3160 16 of 35
Linux
Control
Layer
Application
Layer
Link
Layer
Cloud
Orchestration
SDN
Applications
SDN Controller
OPENFLOW SDN
Server
OF/SSL/TCP/IP
REST/TCP/IP
Linux NG SDN
CONTROLLED DEVICES
PGCS
RMS
Other
Applications
NG/ETHERNET
NG/ETHERNET
Server
DYNAMIC
CONTRACTS
Figure 5.
Comparison of OpenFlow-based SDN with NovaGenesis service-defined architecture (SDA).
4.7. Publishing Content to the Local Domain Temporary Cache
Figure 4demonstrates the path required to publish a NG message from an application to the
NRNCS. In Transaction 1, a Host 2 application publishes a message to the PSS. The local gateway
determines the message is destined to another host (using a host identifier in the message header).
The App GW forwards the message to the local PGCS GW via Transaction 2. The PGCS GW
also determines the message destination is Host 1 and forwards the message to its PG for inter
OS message exchange (Trans. 3). The PG block employs a raw socket to encapsulate NG over
Ethernet (Trans. 4). Then, Host 1 PGCS PG reads the message from raw socket (Trans. 5). Using PSS
identifier (also contained in message header), the PGCS GW forwards the message to the PSS instance
(Trans. 6 and 7). The message goes to PS block, where a GIRS instance is selected to continue storage
(Trans. 8-10). The message arrives at the GIRS GW and is forwarded to the IR block (Trans. 12). The IR
block selects the proper HTS instance to store the content and forwards the message to HTS GW
(Trans. 13–15). The message arrives at the HTS HT block (Trans. 16), where its content is store in Host
1 Linux file system. NRNCS always involve message forwarding from PSS to GIRS and from GIRS and
HTS. A content subscription follows the same path up to the HTS. Content delivery is done by HTS
directly to the subscriber service.
4.8. Message Format
NovaGenesis messages are divided into two portions: command lines (that call Actions in the
receiving side) and payload. The command lines are in ASCII format, employing a novel script
language to define control commands and their parameters. The payload is streamed from a file
system archive. To separate command lines and payload a blank line is employed. The structure of
each command line is as follows:
ng -command –alternative version [ <n type E1 E2 E3 E4 ... En >]
in which
-command identifies the Action that will be called at the destination.
-alternative identifies possible alternatives to the command line, allowing Actions customization.
version identifies the version of the command line being executed.
[ ] contains one or more vectorial arguments.
n indicates the number of elements in the vector.
type contains the type employed in the vector elements.
E1 E2 E3 E4 ... En are the vectors’ elements, containing parameters for the command line.
Figure 6gives an example of a NovaGenesis exposition message used by a service to publish
its name bindings to other services. The first command line with the command ng -m –cl is a

Sensors 2018,18, 3160 17 of 35
forwarding/routing command line [
75
], which contains the control information required to forward
the message to a certain destination. It contains two tuples of four values that identifiers respectively,
source and destination self-verifying names (SVNes). The argument
[<
4 s 0BD95286 ED12F3ED
342DD4C5 B8101939
>]
contains a tuple that names the source of the message and the argument
[<
4 s
0BD95286 ED12F3ED 449B0B0C 6FDF0A76
>]
provides the names of the destination. The command
line ng -p –b is used to publish name bindings, therefore exposing keywords related to the service
exposing its features, e.g., the key 19656CF3 is the hash of the word “Wi-Fi”. The last command line
contains the hash of the previous command lines for integrity check purpose.
Version September 1, 2018 submitted to Sensors 15 of 34
exchange (Trans. 3). The PG block employs a raw socket to encapsulate NG over Ethernet (Trans. 4).477
Then, Host 1 PGCS PG reads the message from raw socket (Trans. 5). Using PSS identifier (also478
contained in message header), the PGCS GW forwards the message to the PSS instance (Trans. 6 and479
7). The message goes to PS block, where a GIRS instance is selected to continue storage (Trans. 8-10).480
The message arrives at the GIRS GW and is forwarded to the IR block (Trans. 12). The IR block selects481
the proper HTS instance to store the content and forwards the message to HTS GW (Trans. 13-15).482
The message arrives at the HTS HT block (Trans. 16), where its content is store in Host 1 Linux file483
system. NRNCS always involve message forwarding from PSS to GIRS and from GIRS and HTS. A484
content subscription follows the same path up to the HTS. Content delivery is done by HTS directly485
to the subscriber service.486
4.8. Message Format487
NovaGenesis messages are divided into two portions: command lines (that call Actions in the488
receiving side) and payload. The command lines are in ASCII format, employing a novel script489
language to define control commands and their parameters. The payload is streamed from a file490
system archive. To separate command lines and payload a blank line is employed. The structure491
of each command line is as follows:492
493
ng -command –alternative version [ <n type E1 E2 E3 E4 ... En >]494
495
in which496
-command identifies the Action that will be called at the destination.497
–alternative identifies possible alternatives to the command line, allowing Actions customization.498
version identifies the version of the command line being executed.499
[ ] contains one or more vectorial arguments.500
nindicates the number of elements in the vector.501
type contains the type employed in the vector elements.502
E1 E2 E3 E4 ... En are the vectors’ elements, containing parameters for the command line.503
504
Figure 6gives an example of a NovaGenesis exposition message used by a service to publish505
its name bindings to other services. The first command line with the command ng -m –cl is a506
forwarding/routing command line [75], which contains the control information required to forward507
the message to a certain destination. It contains two tuples of four values that identifiers respectively,508
source and destination self-verifying names (SVNes). The argument [<4s0BD95286 ED12F3ED509
342DD4C5B8101939 >]contains a tuple that names the source of the message and the argument [510
<4s0BD95286 ED12F3ED 449B0B0C6F DF0A76 >]provides the names of the destination. The511
command line ng -p –b is used to publish name bindings, therefore exposing keywords related to512
the service exposing its features, e.g. the key 19656CF3 is the hash of the word “Wi-Fi”. The last513
command line contains the hash of the previous command lines for integrity check purpose.514
ng -m--cl 0.1 [<1s ... ><4s0BD95286 ED12F3ED 342DD4C5 B8101939 >
<4s0BD95286 ED12F3ED 449B0B0C 6FDF0A76 >]
...
ng -p--b0.1 [<1s2><1s19656CF3 ><1s342DD4C5 >]
ng -p--b0.1 [<1s1><1s19656CF3 ><1s Wi-Fi >]
...
ng -scn --seq 0.1 [<1s78A8DC70 >]
Figure 6. Example of a NovaGenesis exposition message. In the current prototype, NovaGenesis
messages carry command lines with one or more arguments. The messages are textual, to facilitate
development. Future versions will include source encoding to reduce overhead. In the current form,
messages can be manually entered by a user.
Figure 6.
Example of a NG exposition message. In the current prototype, NG messages carry command
lines with one or more arguments. The messages are textual, to facilitate development. Future versions
will include source encoding to reduce overhead.
4.9. Dynamic Spectrum Management with NovaGenesis
NovaGenesis allows spectrum sensing, management and control as a service. The service-oriented
spectrum management components are implemented as services in NG environment. They represent
spectrum sensing hardware to enable dynamic composition with a spectrum manager. Services also
represent the Wi-Fi access points and IEEE 802.15.4 nodes, allowing software-defined configuration
of wireless channels. NG enables contract-based spectrum management, integrating all components
required for dynamic spectrum access (DAS). It aims to create a dynamic spectrum market, in which
opportunistic usage is allowed and monetized accordingly to established contracts or SLAs.
4.9.1. Spectrum Sensing Service (SSS)
In this paper, we extended a previous spectrum sensing service (SSS) development [
33
] to expose
IEEE 802.11 and IEEE 802.15.4 best channel indications to a resource management service (RMS).
As illustrated in Figure 7, measurements performed in HackRF One
TM
hardware are transferred
to a GNU Radio instance. The channel advisor (CA) controls spectrum energy measurement and
connects to a Core block of SSS via zero-em-queue (ZMQ) request/response socket. In addition,
SSS also publishes keywords (“SSS”, “Spectrum”,“Sensing”, “IEEE 802.15.4” and “Wi-Fi”) to other NG
spectrum management services in a local domain via PSS. Posterior, SSS searchers for a RMS instance.
When SSS discovers a RMS, it sends a service offer to RMS describing the details behind best channel
indications that will be performed.
4.9.2. Access Point Service (APS)
APS is novel service to represent and control Wi-Fi access points inside NovaGenesis.
APS publishes keywords (“APS”, “Access”, “Point”, “Controller” and “Wi-Fi”) to other NG spectrum
management services to enable dynamic composition. It also searchers for RMS instances and sends
a service offer to discovered ones, describing details of the Wi-Fi APs it is representing. A service
acceptance message will be sent by the RMS in case of contract establishment. Then, APS can exchange
channels in Wi-Fi APs as requested by RMS. For this aim, APS Core block sends a control message to
the AP via SSH. APs need to run OpenWRT OS.
4.9.3. Resource Management Service (RMS)
RMS implements a name-based, contract-oriented, self-organizing wireless spectrum management
approach in a local NG domain. RMS mediates the relationship among NG spectrum management

Sensors 2018,18, 3160 18 of 35
services (a.k.a SSS, APS and PGCS). Communication among them is TCP/IP independent. When a SSS
discovers a RMS, it proposes a service offer with a description of the channel indication functionality
that will be provided by CA software (Section 3). RMS accepts the contract and starts being notified of
the SSSes’ publications which inform: (i) the wireless technology that is being sensed (Wi-Fi or IEEE
802.15.4); (ii) the best channel to be used by local devices. Therefore, RMS becomes aware of the radio
spectrum situation (a property called situation awareness) in the industrial, scientific and medical
(ISM) bands. RMS can then decide if it accepts latest indication subscribed from SSS or to keep the
current configuration. If it decides to change Wi-Fi/802.15.4 channel configurations, it publishes a
control message to one or more local domain APS/PGCS, asking them to change channel. PGCS is
used in case of IEEE 802.15.4.
HackRF One
USB
Spectrum Sensing
USB
802.15.4 motes
Border router
Channel
Advisor
IEEE 802.15.4
IEEE 802.11
GNU
Radio
Host 3
Linux
ZMQ
TUNSLIP
APS
PGCS
Gateway
(GW)
Hash
Table
(HT)
Proxy/
Gateway
(PG)
Raw socket
Gateway
(GW)
Hash
Table
(HT)
Core
Sh. Memory
RMS
Gateway
(GW)
Hash
Table
(HT)
Core
SSS
Gateway
(GW)
Hash
Table
(HT)
Core
ZMQ
IEEE 802.11
Core
Access Point
Wi-Fi
Communicating devices
TUNSLIP
SSH
Host 2
HTS
PSS
Gateway
(GW)
Hash
Table
(HT)
Pub/Sub
(PS)
GIRS
Gateway
(GW)
Hash
Table
(HT)
Indirec.
Resol.
(IR)
Linux
Host 1
Gateway
(GW)
Hash
Table
(HT)
D. Hash
Table
(HT)
NRNCS - NAME RESOLUTION AND
NETWORK CACHE SERVICE
PGCS
Gateway
(GW)
Hash
Table
(HT)
Proxy/
Gateway
(PG)
IEEE 802.11
Shared Memory
Raw socket
Figure 7.
Joint IoT and Wi-Fi spectrum management with NovaGenesis. The Host 1 is running NG
core services, while Host 2 is running NG spectrum management services, including spectrum sensing
service (SSS), access point service (APS) and resource management service (RMS). SSS provides best
channel indications to RMS based on HackRF One
TM
measures. RMS manages channel indications
and selects best channels for Wi-Fi and IEEE 802.15.4 devices. APS configures the best channel to be
used in Wi-Fi access points. Observe that PGCS at Host 2 has a core block, which implements service
offering and acceptance from RMS. Host 3 is running Channel Advisor (CA) and GNU Radio.
4.9.4. Proxy/Gateway/Controller Service (PGCS)
When an IoT node needs to change the frequency channel, e.g., a IEEE 802.15.4 node, a contract to
its representative PGCS is required. In this way, PGCS exposes its keywords and sends a service offer
to RMS. The offer exposes PGCS IoT node control feature, which can be done via Tunslip software as
illustrated in Figure 7. Future work will enable IEEE 802.15.4 channel exchange without using TCP/IP.
An embedded proxy/gateway service (EPGS) is being adapted [
3
] to run inside IoT nodes and accept
NovaGenesis messages directly, without TCP/IP.

Sensors 2018,18, 3160 19 of 35
4.10. Dynamic Composition of Spectrum Management Services
Figure 8provides a sequence diagram of the actions implemented to dynamically manage 2.4 GHz
ISM spectrum with NovaGenesis. The life-cycle comprehends five steps: (i) service names/keywords
exposition; (ii) service discovery; (iii) service contracting; (iv) best channel indications; (v) device
channel adjustment.
Host 3
HackRF
One
Host 2
Exposition
Host 1
PGCS
PGCS
APS
Discovery
Contracting
NRNCS
RMS
SSS
Channel
Advisor
GNU
Radio
Access
Point(s)
Tags
CC 2650
1b
1c
1d
2b
2c
1a
2a
3a
3c
3d
2d
Best Channel Indication
4b
4c
Changing
Channel(s)
5c
5a, 5b
5d
4a
Channel
Sensing
Energy
Estimation
CA indicates the best channel to be used for Wi-Fi or IEEE
802.15.4. SSS publishes the indication for RMS.
PGCS offers its ability to change IEEE
802.15.4 channels at IoT Nodes.
Accepts the service
offers.
RMS subscribes the indication.
RMS evaluates indications and
change channels if required.
APS implements Wi-Fi channel change.
PGCS implements IEEE 802.15.4 channel change.
Spectrum sensing service
expose its names.
Spectrum sensing
service searches for
RMS names.
3b
SSS offers its best channel
indication ability.
RMS subscribes the offers.
Figure 8.
Life-cycle of the dynamic spectrum management approach with NovaGenesis. Five steps
are shown: service exposition, service discovery, contracting (dynamic composition), best channel
indication and channel changing (Wi-Fi and IEEE 802.15.4). Each vertical line represents the control
plane actions related to a certain component. Control actions (Transactions) are named as
nc
, in which
n
is related to the life cycle step being performed and
c
is a sequence number introduced to facilitate
understanding. For instance, 1ais a control plane message send by PGCS to NRNCS.

Sensors 2018,18, 3160 20 of 35
4.10.1. Service Names/Keywords Exposition
In Transaction 1a, PGCS from Host 2 publishes to NRNCS a set of self-verifying name bindings,
e.g., among Host ID, OS ID, Process ID and internal component IDs. In addition, SVNes are bound
to natural language names (keywords) to express PGCS role in spectrum management solution
(e.g., “Proxy”, “Gateway”, “Controller” and “IoT”). These NBs are stored in the HTS distributed
system. Transactions 1b, 1c and 1d carry, respectively, name bindings of APS, RMS and SSS instances.
SSS keywords are “Spectrum”, “Sensing”, “Service”, “Wi-Fi” and “IoT”, while RMS are “Manager”,
“IoT”, “RMS” and “Management”. Other keywords can be used, in any language. These transactions
are forwarded by local Host 2’s PGCS to the Host 1’s PGCS, in which NRNCS is running. In the
end of this exposition phase, all name binding (graph of names) required to self-organize spectrum
management services are stored in the NRNCS.
4.10.2. Service Discovery
In the service discovery step, services subscribe NBs can identify possible peer services for
the solution. In Transaction 2a, PGCS subscribes the keywords “Manager”, “IoT”, “RMS” and
“Management” in order to discover a RMS instance. APS and SSS also employ the same keywords,
while searching for RMS (Transactions 2b and 2d). In the opposite direction (2c), RMS tries to discover
PGCS, APS and SSS instances that can be employed to control devices. NRNCS delivers NBs resulting
from these searches to each one of the querying services. This service discovery procedure is distributed,
periodic and recursive. Not all the Transaction taken are shown in Figure 8for the sake of simplicity.
4.10.3. Service Contracting
In Figure 8, two service contracting actions are illustrated. In the first one, PGCS publishes a
service offer to a RMS instance that it discovers. In this offer (3a), PGCS informs its ability to exchange
IEEE 802.15.4 channel at IoT sensor tags it represents. In the second one (3b), SSS offers to RMS the
ability to indicate best channels to be used not only in Wi-Fi ISM band, but also in IEEE 802.15.4. In a
third action (not shown in Figure 8), APS offers to RMS its ability to exchange Wi-Fi channels at access
points. All offers are forwarded to RMS, which coordinates all actions in a domain.
RMS is notified by NRNCS about the three service offers commented above and subscribe them
(3c). After receiving the service offers (in a .txt file), RMS analyzes its contents. In Transaction 3d,
RMS accepts the service offer by publishing a service acceptance .txt file to NRNCS and notifying
PGCS about it. PGCS subscribes this service acceptance object and stores it locally. The other service
contracts are established in a similar way.
4.10.4. Best Channel Indication
After establishing all contracts, services can perform dynamic spectrum management via
named-content message exchanging. In Transaction 4a, SSS queries channel advisor software about the
best channel to be used in a domain. Queries can be applied for Wi-Fi or IEEE 802.15.4. CA determines
the best option based on a GNU Radio instance connected to a HackRF One
TM
software-defined
radio. In Transaction 4b, SSS publishes a best channel indication to the local domain cache (NRNCS),
which notifies RMS about this publication. RMS subscribes the best channel indication and decides if a
channel exchange is necessary.
4.10.5. Devices Channel Adjustment
The last step is to change devices channels according to the need. RMS can change one or
more access point channels or IoT node channels. In the case of changing IEEE 802.15.4 channels,
RMS publishes a channel change control message to the PGCS (5c). PGCS is notified about this
publication and subscribes it, implementing a properly channel change in the IoT Sensor Tags. In the
contrary (Wi-Fi channel changing), RMS publishes a control message to the local domain APS (5d).
APS subscribes the control message and implements a channel change in the AP(s).

Sensors 2018,18, 3160 21 of 35
5. Experimental Results and Analysis
Our architecture can be employed to any kind of wireless communication protocol, since SDR senses
all RF signals present over a desired frequency bandwidth. To evaluate the system efficiency for an IoT
scenario, we choose two standards that might be commonly seen in the same ambient: (i) IEEE 802.15.4,
composed by fourteen IoT motes sharing the geographical space; (ii) IEEE 802.11 (Wi-Fi) devices.
5.1. Testing Methodology
All measurements were executed in an open soccer field, apart from other 2.4 GHz interference
signals not considered as part of the experiment. At data plane, the Wi-Fi network has been composed
by one router (an access point or AP) and two Wi-Fi end devices. The AP is a TP-Link N750
TM
with OpenWRT, covering features like operation bandwidth control, potency control and channel
management, all configured by software. The Wi-Fi end devices were a laptop and a mobile phone,
both connected through a TCP socket generating traffic, continuously. The 802.15.4 network was
composed by one border router Smart RF06TM and fourteen Texas Instruments cc2650TM motes.
Version September 1, 2018 submitted to Sensors 21 of 34
(a) Cognitive radio system scenario without NovaGenesis.
(b) Cognitive radio system scenario interoperating with NovaGenesis.
Figure 9. Experimental scenarios close to a soccer field to avoid interference to other devices outside
experiment.
5.2.2. Evaluation of IEEE 802.11 Interference in the IEEE 802.15.4 Operation667
It is possible to evaluate the interference of the Wi-Fi network operation when geographically668
close to an IEEE 802.15.4 network. Without the Wi-Fi AP interference, the IEEE 802.15.4 CoAP669
throughput was 1120 bps. After the Wi-Fi simultaneously operation at the channel 7, the 802.15.4670
CoAP throughput decreased to 520 bps. The Channel Advisor automatically switched the 802.15.4671
operation from the channel 18 to the 21. The new CoAP throughput was 1270 bps, a value close to672
the non-interference condition.673
5.3. Results with NovaGenesis Control Plane674
The second scenario consists of running the Channel Advisor to optimize wireless network675
device channels through NovaGenesis in order to automatically software-control their operation676
under the umbrella of SLAs. The thesis we want to proof is NovaGenesis will perform similarly to677
the Channel Advisor, but including the key design dimensions of Table 1. The experimental scenario678
Version September 1, 2018 submitted to Sensors 21 of 34
(a) Cognitive radio system scenario without NovaGenesis.
(b) Cognitive radio system scenario interoperating with NovaGenesis.
Figure 9. Experimental scenarios close to a soccer field to avoid interference to other devices outside
experiment.
5.2.2. Evaluation of IEEE 802.11 Interference in the IEEE 802.15.4 Operation667
It is possible to evaluate the interference of the Wi-Fi network operation when geographically668
close to an IEEE 802.15.4 network. Without the Wi-Fi AP interference, the IEEE 802.15.4 CoAP669
throughput was 1120 bps. After the Wi-Fi simultaneously operation at the channel 7, the 802.15.4670
CoAP throughput decreased to 520 bps. The Channel Advisor automatically switched the 802.15.4671
operation from the channel 18 to the 21. The new CoAP throughput was 1270 bps, a value close to672
the non-interference condition.673
5.3. Results with NovaGenesis Control Plane674
The second scenario consists of running the Channel Advisor to optimize wireless network675
device channels through NovaGenesis in order to automatically software-control their operation676
under the umbrella of SLAs. The thesis we want to proof is NovaGenesis will perform similarly to677
the Channel Advisor, but including the key design dimensions of Table 1. The experimental scenario678
Figure 9.
Experimental scenarios close to a soccer field to avoid interference to other devices outside
experiment. (a) Scenario without NG; (b) Cognitive radio system scenario interoperating with NG.

Sensors 2018,18, 3160 22 of 35
All IEEE 802.15.4 devices run a Contiki [
76
] operational system (OS) and communicate one
another via a low power wireless personal area network (6LoWPAN) with constrained application
protocol (CoAP) [
77
]. 6LoWPAN devices are typically battery-powered and have low processing
capability. CoAP is an application protocol for constrained devices, since it is lightweight and low
power consuming. Furthermore, Contiki is an event-driven embedded OS for IoT nodes that allow
dynamic loading and unloading of individual programs and services. The 802.15.4 motes generate
traffic at the data plane, continuously publishing temperature measurements to an IEEE 802.15.4 border
router. The described scenario is depicted in Figure 9. We will first present and discuss Figure 9a
scenario (in Section 5.2), in which manual coordination is applied, instead of NovaGenesis architecture.
NG coordination is presented in Section 5.3.
In a worst interference situation, the Wi-Fi AP operates in the channel 7, which has central
frequency of 2.442 GHz. The 802.15.4 network operates, at the same time, at the default channel 18,
which has central frequency of 2.440 GHz. Those channels were chosen to ensure the interference of the
signals transmitted, since they have coincident channel frequencies. Figure 10 presents the frequency
of each channel for 802.11 and 802.15.4. The 802.11 AP transmits at 22 dBm, while the 802.15.4 motes
transmits at 5 dBm.
Version September 1, 2018 submitted to Sensors 22 of 34
Figure 10. 2.4 GHz ISM frequency bands of the 802.15.4 and 802.11 standards.
Figure 11. Variation in IEEE 802.11 throughput as new 802.15.4 motes are added to the network.
with NovaGenesis is connected to a Sensing Cell through ZMQ socket as illustrated in Figure 7. In679
this way, the Sensing Cell can be geographically distant from the NG Core. In the next Subsections,680
we will follow Figure 8steps, proofing NG concept for DSM.681
5.3.1. Exposition and Discovery of DSM Services682
Figure 12 contains a partial reproduction of the log of an APS exposition message published to683
RMS via NRNCS. The command line ng -m –cl is used for forwarding/routing [75]. The command684
line ng -p –b is used to publish name bindings, therefore exposing APS keywords to RMS, e.g. the key685
19656CF3 is the hash of the word “Wi-Fi”. This log is a print of Transaction 1b in Figure 8. Besides686
APS, SSS, RMS and PGCS expose their names. In the discovery process, RMS subscribes keywords687
from the other DSM services. Figure 13 depicts a partial log of a NRNCS response to a RMS search688
(Trans. 2c in Fig. 8). Name bindings inform about possible peers that will try to establish service689
contracts in the next step of DSM self-organizing approach. The command line ng -d –b is employed690
to deliver name bindings.691
5.3.2. Dynamic Contracting of DSM Services692
Figure 14 reproduces APS service offer to RMS. The pub/notify command line (ng -p –notify)693
contains the name binding <1 s A613BB6A >< 1 s Service_Offer_1127995407.txt >, which links the694
SVN A613BB6Ato the .txt file containing the service offer. After the blank line, the features of the695
Figure 10. 2.4 GHz ISM frequency bands of the 802.15.4 and 802.11 standards.
5.2. Results for Cognitive Radio System
The first test scenario consists of running the Channel Advisor to indicate the best network
channels, without using the NovaGenesis to automatically coordinate all devices. We check the
influence of 802.15.4 in the 802.11 operation and opposite situation.
5.2.1. Evaluation of IEEE 802.15.4 Interference in the IEEE 802.11 Operation
This scenario, as illustrated in Figure 9a, consists of evaluating the IEEE 802.11 throughput
while IEEE 802.15.4 causes interference. To estimate the motes interference in the data plane of IEEE
802.11 standard, TCP throughput was evaluated when a new mote came into the network. Figure 11
reports the throughput decrease for each new mote connected to the IEEE 802.15.4 network, when IEEE
802.15.4 operates at the channel 18 and IEEE 802.11 at the channel 7. Figure 11 was measured in a
scenario without the NovaGenesis control plane. The reported results indicate fast dropping of IEEE
802.11 throughput when IoT network size increases. This result justifies all the efforts being made to
increase and dynamically manage ISM band allocations. Changing IoT nodes to other RF channels
is an effective way to deal with the increasing of IoT motes in a network. However, there are limits
to what can be done with channel switching, which indicates that the radio technologies themselves
(physical and link layers) need to evolve to support the high congestions of spectrum usage in the
2.4 GHz ISM band.

Sensors 2018,18, 3160 23 of 35
We turned ON the Channel Advisor to sense IEEE 802.11 channels, verifying the best one to enter
into operation. The throughput of the IEEE 802.11 was 6.1 Mbps with the interference of fourteen
802.15.4 motes. The Channel Advisor recommended operation at the channel 11. After switching
the operation of the IEEE 802.11 router to the channel 11, the new IEEE 802.11 TCP throughput
was 34.86 Mbps, i.e., an increase of 4.7 times in the throughput, by making the rate next to the
non-interference condition.
Figure 11. Variation in IEEE 802.11 throughput as new 802.15.4 motes are added to the network.
5.2.2. Evaluation of IEEE 802.11 Interference in the IEEE 802.15.4 Operation
It is possible to evaluate the interference of the Wi-Fi network operation when geographically
close to an IEEE 802.15.4 network. Without the Wi-Fi AP interference, the IEEE 802.15.4 CoAP
throughput was 1120 bps. After the Wi-Fi simultaneously operation at the channel 7, the 802.15.4 CoAP
throughput decreased to 520 bps. The Channel Advisor automatically switched the 802.15.4 operation
from the channel 18 to the 21. The new CoAP throughput was 1270 bps, a value close to the
non-interference condition.
5.3. Results with NovaGenesis Control Plane
The second scenario consists of running the Channel Advisor to optimize wireless network
device channels through NovaGenesis in order to automatically software-control their operation under
the umbrella of SLAs. The thesis we want to proof is NovaGenesis will perform similarly to the
Channel Advisor, but including the key design dimensions of Table 1. The experimental scenario with
NovaGenesis is connected to a Sensing Cell through ZMQ socket as illustrated in Figure 7. In this way,
the Sensing Cell can be geographically distant from the NG Core. In the next Subsections, we will
follow Figure 8steps, proofing NG concept for DSM.
5.3.1. Exposition and Discovery of DSM Services
Figure 12 contains a partial reproduction of the log of an APS exposition message published to
RMS via NRNCS. The command line ng -m –cl is used for forwarding/routing [
75
]. The command
line ng -p –b is used to publish name bindings, therefore exposing APS keywords to RMS, e.g., the key
19656CF3 is the hash of the word “Wi-Fi”. This log is a print of Transaction 1b in Figure 8. Besides APS,
SSS, RMS and PGCS expose their names. In the discovery process, RMS subscribes keywords from the
other DSM services. Figure 13 depicts a partial log of a NRNCS response to a RMS search (Trans. 2c in
Figure 8). Name bindings inform about possible peers that will try to establish service contracts in
the next step of DSM self-organizing approach. The command line ng -d –b is employed to deliver
name bindings.

Sensors 2018,18, 3160 24 of 35
Version September 1, 2018 submitted to Sensors 23 of 34
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 382058FF AFE05993 >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--b0.1 [<1s2><1s07A06043 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s07A06043 ><1s APS >]
ng -p--b0.1 [<1s2><1s D051B60B ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D051B60B ><1s Core >]
ng -p--b0.1 [<1s2><1s D95F19E2 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D95F19E2 ><1s Access >]
ng -p--b0.1 [<1s2><1s EB4C062C ><1s382058FF >]
ng -p--b0.1 [<1s1><1s EB4C062C ><1s Point >]
ng -p--b0.1 [<1s2><1s9E6CCE05 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s9E6CCE05 ><1s Wireless >]
ng -p--b0.1 [<1s2><1s423FCDDC ><1s382058FF >]
ng -p--b0.1 [<1s1><1s423FCDDC ><1s Controller >]
ng -p--b0.1 [<1s2><1s D5546D53 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D5546D53 ><1sProxy >]
ng -p--b0.1 [<1s2><1s9110AE5C ><1s382058FF >]
...
ng -p--b0.1 [<1s1><1s19656CF3 ><1s Wi-Fi >]
ng -p--b0.1 [<1s2><1s38EED305 ><1s382058FF >]
...
ng -sr --b0.1 [<1s2><1s4E5CD8C0 ><1s A1328D4B >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s29>]
ng -scn --seq 0.1 [<1s B276057A >]
Figure 12. Log of APS exposition to enable RMS discovery of this access point proxy/control service.
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 013F4745 434E7270 >
<4s43188DBC A1328D4B 603FE845 C8D4702F >]
ng -d--b0.1 [<1s2><1s4E5CD8C0 ><1s A1328D4B >]
ng -d--b0.1 [<1s2><1s D051B60B ><6s382058FF C8D4702F 603FE845 E93185DE BC6F9917 AFE05993 >]
ng -d--b0.1 [<1s2><1s9E6CCE05 ><1s382058FF >]
ng -d--b0.1 [<1s2><1s9110AE5C ><2s382058FF BC6F9917 >]
ng -d--b0.1 [<1s2><1s19656CF3 ><3s382058FF 603FE845 BC6F9917 >]
ng -d--b0.1 [<1s2><1s07A06043 ><1s382058FF >]
...
ng -d--b0.1 [<1s9><1s5A73AB32 ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -scn --ack 0.1 [<2s D464C64C F02CC001 >]
ng -scn --ack 0.1 [<2s5649CA63 DAECBA4E >]
Figure 13. Log of the response from HTS to a RMS query about possible DSM peers.
access point TL-WDR4300TM are detailed to RMS. The ng -info –payload informs the name of the .txt696
file to be stored at APS input/output (IO) folder in Linux operating system (OS). Figure 15 depicts697
SSS service offer to RMS, as illustrated in Fig. 8Transaction 3b. The SSS01.sensing_bw informs the698
Channel Advisor sensing bandwidth. All messages have an integrity check field (ng -scn –seq 0.1 [ <699
1 s DF0BFE88 >]) which allows to determine whether messages have been tampered with in transit700
or not. In all service offer cases, a service acceptance object is published by RMS and notified to701
APS/SSS/PGCS to inform RMS accordance. Both logs demonstrate NovaGenesis ability to represent702
other systems inside its environment, enabling proxying of any kind of legacy services/applications.703
5.3.3. The Best Wi-Fi Channel Indication and Changing704
After the contract establishment, the DSM services can start operating as designed. Figure 16705
reproduces the log of a SSS message sent to RMS indicating the best channel to be used to reduce706
interference with IEEE 802.15.4 sensor tags (Fig. 8Transaction 4b). RMS publishes the same indication707
to APS, which decides a channel change is required or not. Posterior, APS subscribes another file708
published with the same information and performs the change using SSH to connect to TL-WDR4300709
OpenWRT, as illustrated in Figure 7. We measured the time required by the APS to subscribe a control710
file (SSSFile_0.txt) from NRNCS temporary cache as 8.666 ms.711
Figure 12. Log of APS exposition to enable RMS discovery of this access point proxy/control service.
Version September 1, 2018 submitted to Sensors 23 of 34
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 382058FF AFE05993 >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--b0.1 [<1s2><1s07A06043 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s07A06043 ><1s APS >]
ng -p--b0.1 [<1s2><1s D051B60B ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D051B60B ><1s Core >]
ng -p--b0.1 [<1s2><1s D95F19E2 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D95F19E2 ><1s Access >]
ng -p--b0.1 [<1s2><1s EB4C062C ><1s382058FF >]
ng -p--b0.1 [<1s1><1s EB4C062C ><1s Point >]
ng -p--b0.1 [<1s2><1s9E6CCE05 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s9E6CCE05 ><1s Wireless >]
ng -p--b0.1 [<1s2><1s423FCDDC ><1s382058FF >]
ng -p--b0.1 [<1s1><1s423FCDDC ><1s Controller >]
ng -p--b0.1 [<1s2><1s D5546D53 ><1s382058FF >]
ng -p--b0.1 [<1s1><1s D5546D53 ><1sProxy >]
ng -p--b0.1 [<1s2><1s9110AE5C ><1s382058FF >]
...
ng -p--b0.1 [<1s1><1s19656CF3 ><1s Wi-Fi >]
ng -p--b0.1 [<1s2><1s38EED305 ><1s382058FF >]
...
ng -sr --b0.1 [<1s2><1s4E5CD8C0 ><1s A1328D4B >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s29>]
ng -scn --seq 0.1 [<1s B276057A >]
Figure 12. Log of APS exposition to enable RMS discovery of this access point proxy/control service.
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 013F4745 434E7270 >
<4s43188DBC A1328D4B 603FE845 C8D4702F >]
ng -d--b0.1 [<1s2><1s4E5CD8C0 ><1s A1328D4B >]
ng -d--b0.1 [<1s2><1s D051B60B ><6s382058FF C8D4702F 603FE845 E93185DE BC6F9917 AFE05993 >]
ng -d--b0.1 [<1s2><1s9E6CCE05 ><1s382058FF >]
ng -d--b0.1 [<1s2><1s9110AE5C ><2s382058FF BC6F9917 >]
ng -d--b0.1 [<1s2><1s19656CF3 ><3s382058FF 603FE845 BC6F9917 >]
ng -d--b0.1 [<1s2><1s07A06043 ><1s382058FF >]
...
ng -d--b0.1 [<1s9><1s5A73AB32 ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -scn --ack 0.1 [<2s D464C64C F02CC001 >]
ng -scn --ack 0.1 [<2s5649CA63 DAECBA4E >]
Figure 13. Log of the response from HTS to a RMS query about possible DSM peers.
access point TL-WDR4300TM are detailed to RMS. The ng -info –payload informs the name of the .txt696
file to be stored at APS input/output (IO) folder in Linux operating system (OS). Figure 15 depicts697
SSS service offer to RMS, as illustrated in Fig. 8Transaction 3b. The SSS01.sensing_bw informs the698
Channel Advisor sensing bandwidth. All messages have an integrity check field (ng -scn –seq 0.1 [ <699
1 s DF0BFE88 >]) which allows to determine whether messages have been tampered with in transit700
or not. In all service offer cases, a service acceptance object is published by RMS and notified to701
APS/SSS/PGCS to inform RMS accordance. Both logs demonstrate NovaGenesis ability to represent702
other systems inside its environment, enabling proxying of any kind of legacy services/applications.703
5.3.3. The Best Wi-Fi Channel Indication and Changing704
After the contract establishment, the DSM services can start operating as designed. Figure 16705
reproduces the log of a SSS message sent to RMS indicating the best channel to be used to reduce706
interference with IEEE 802.15.4 sensor tags (Fig. 8Transaction 4b). RMS publishes the same indication707
to APS, which decides a channel change is required or not. Posterior, APS subscribes another file708
published with the same information and performs the change using SSH to connect to TL-WDR4300709
OpenWRT, as illustrated in Figure 7. We measured the time required by the APS to subscribe a control710
file (SSSFile_0.txt) from NRNCS temporary cache as 8.666 ms.711
Figure 13. Log of the response from HTS to a RMS query about possible DSM peers.
5.3.2. Dynamic Contracting of DSM Services
Figure 14 reproduces APS service offer to RMS. The pub/notify command line (ng -p –notify)
contains the name binding
<
1 s A613BB6A
><
1 s Service_Offer_1127995407.txt
>
, which links the
SVN A613BB6A to the .txt file containing the service offer. After the blank line, the features of the
access point TL-WDR4300
TM
are detailed to RMS. The ng -info –payload informs the name of the .txt
file to be stored at APS input/output (IO) folder in Linux operating system (OS). Figure 15 depicts
SSS service offer to RMS, as illustrated in Figure 8Transaction 3b. The SSS01.sensing_bw informs
the Channel Advisor sensing bandwidth. All messages have an integrity check field (ng -scn –seq
0.1 [
<
1 s DF0BFE88
>
]) which allows to determine whether messages have been tampered with in
transit or not. In all service offer cases, a service acceptance object is published by RMS and notified to
APS/SSS/PGCS to inform RMS accordance. Both logs demonstrate NovaGenesis ability to represent
other systems inside its environment, enabling proxying of any kind of legacy services/applications.

Sensors 2018,18, 3160 25 of 35
Version September 1, 2018 submitted to Sensors 24 of 34
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 382058FF AFE05993 >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--notify 0.1 [<1s18><1s A613BB6A ><1s Service_Offer_1127995407.txt >
<5s pub 43188DBC A1328D4B 603FE845 C8D4702F >]
ng -info --payload 0.1 [<1s Service_Offer_1127995407.txt >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s AFE05993 >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s382058FF >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s A1328D4B >]
ng -p--b0.1 [<1s9><1s A613BB6A ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s67>]
ng -scn --seq 0.1 [<1s DF0BFE88 >]
ng -sr --b0.1 [<1s17><1s APS01 > < 13 s Negotiation_Type Dual_Band a_Flag b_Flag g_Flag n_Flag
Number_of_LAN_Ports Number_of_WAN_Ports LAN_Port_Bit_Rates WAN_Port_Bit_Rates Device_Type
Device_Supplier Device_Model >]
ng -sr --b0.1 [<1s17><1s APS01.Negotiation_Type ><1s Rigid >]
ng -sr --b0.1 [<1s17><1s APS01.Dual_Band ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.a_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.b_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.g_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.n_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.Number_of_LAN_Ports ><1s4 > ]
ng -sr --b0.1 [<1s17><1s APS01.Number_of_WAN_Ports ><1s1 > ]
ng -sr --b0.1 [<1s17><1s APS01.LAN_Port_Bit_Rates ><1s10/100/1000 > ]
ng -sr --b0.1 [<1s17><1s APS01.WAN_Port_Bit_Rates ><1s10/100/1000 > ]
ng -sr --b0.1 [<1s17><1s APS01.Device_Type ><1sAP>]
ng -sr --b0.1 [<1s17><1s APS01.Device_Supplier ><1s TPLink >]
ng -sr --b0.1 [<1s17><1s APS01.Device_Model ><1s TL-WDR4300 >]
Figure 14. Log of APS service offer to RMS.
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B BC6F9917 E93185DE >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--notify 0.1 [<1s18><1s16896E81 ><1s Service_Offer_1072251125.txt >
<5s pub 43188DBC A1328D4B 603FE845 C8D4702F >]
ng -info --payload 0.1 [<1s Service_Offer_1072251125.txt >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s E93185DE >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s BC6F9917 >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s A1328D4B >]
ng -p--b0.1 [<1s9><1s16896E81 ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s49>]
ng -scn --seq 0.1 [<1s7EA31CDC >]
ng -sr --b0.1 [<1s17><1s SSS01.sensing_bw ><1s11000000 > ]
Figure 15. Log of SSS service offer to RMS.
5.3.4. The Best IEEE 802.15.4 Channel Indication and Changing712
SSS log changes when indication is related to IEEE 802.15.4 bandwidth. Instead of < 3 s 802.11713
Channel 11 >], SSS publishes < 3 s 802.15.4 Channel 23 >], for instance. The remaining fields of Figure714
16 are the same. In this case, RMS publishes to PGCS in order to change IoT nodes channels, including715
border router‘. Differently from APS, PGCS employs Tunslip to send CoAP commands directly to the716
border router and sensor tags. We have also measured the time required by the PGCS to subscribe a717
control file (PGCSFile_1.txt) from NRNCS temporary cache as 9.106 ms.718
5.3.5. Evaluation of IEEE 802.15.4 Interference in the IEEE 802.11 Operation719
This subsection reports the application of NG DSM for switching Wi-Fi AP channels. The720
throughput operating at channel 7 and interfered by a 802.15.4 network was 6.1 Mbps. As illustrated721
in Figure 8, SSS requests the Channel Advisor to sense IEEE 802.11 spectrum. After a best channel722
indication and RMS evaluation, APS has changed the Wi-Fi router to the channel 11. The same723
indication was automatically took by Channel Advisor without the use of NG architecture, for724
comparison purpose. After the channel change, the new TCP/IP/Wi-Fi throughput was 36.08 Mbps,725
a value close to the non-interference condition and similar to the Channel Advisor result.726
Figure 14. Log of APS service offer to RMS.
Version September 1, 2018 submitted to Sensors 24 of 34
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B 382058FF AFE05993 >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--notify 0.1 [<1s18><1s A613BB6A ><1s Service_Offer_1127995407.txt >
<5s pub 43188DBC A1328D4B 603FE845 C8D4702F >]
ng -info --payload 0.1 [<1s Service_Offer_1127995407.txt >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s AFE05993 >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s382058FF >]
ng -p--b0.1 [<1s2><1s A613BB6A ><1s A1328D4B >]
ng -p--b0.1 [<1s9><1s A613BB6A ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s67>]
ng -scn --seq 0.1 [<1s DF0BFE88 >]
ng -sr --b0.1 [<1s17><1s APS01 > < 13 s Negotiation_Type Dual_Band a_Flag b_Flag g_Flag n_Flag
Number_of_LAN_Ports Number_of_WAN_Ports LAN_Port_Bit_Rates WAN_Port_Bit_Rates Device_Type
Device_Supplier Device_Model >]
ng -sr --b0.1 [<1s17><1s APS01.Negotiation_Type ><1s Rigid >]
ng -sr --b0.1 [<1s17><1s APS01.Dual_Band ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.a_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.b_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.g_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.n_Flag ><1s Yes >]
ng -sr --b0.1 [<1s17><1s APS01.Number_of_LAN_Ports ><1s4 > ]
ng -sr --b0.1 [<1s17><1s APS01.Number_of_WAN_Ports ><1s1 > ]
ng -sr --b0.1 [<1s17><1s APS01.LAN_Port_Bit_Rates ><1s10/100/1000 > ]
ng -sr --b0.1 [<1s17><1s APS01.WAN_Port_Bit_Rates ><1s10/100/1000 > ]
ng -sr --b0.1 [<1s17><1s APS01.Device_Type ><1sAP>]
ng -sr --b0.1 [<1s17><1s APS01.Device_Supplier ><1s TPLink >]
ng -sr --b0.1 [<1s17><1s APS01.Device_Model ><1s TL-WDR4300 >]
Figure 14. Log of APS service offer to RMS.
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B BC6F9917 E93185DE >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--notify 0.1 [<1s18><1s16896E81 ><1s Service_Offer_1072251125.txt >
<5s pub 43188DBC A1328D4B 603FE845 C8D4702F >]
ng -info --payload 0.1 [<1s Service_Offer_1072251125.txt >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s E93185DE >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s BC6F9917 >]
ng -p--b0.1 [<1s2><1s16896E81 ><1s A1328D4B >]
ng -p--b0.1 [<1s9><1s16896E81 ><1s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s49>]
ng -scn --seq 0.1 [<1s7EA31CDC >]
ng -sr --b0.1 [<1s17><1s SSS01.sensing_bw ><1s11000000 > ]
Figure 15. Log of SSS service offer to RMS.
5.3.4. The Best IEEE 802.15.4 Channel Indication and Changing712
SSS log changes when indication is related to IEEE 802.15.4 bandwidth. Instead of < 3 s 802.11713
Channel 11 >], SSS publishes < 3 s 802.15.4 Channel 23 >], for instance. The remaining fields of Figure714
16 are the same. In this case, RMS publishes to PGCS in order to change IoT nodes channels, including715
border router‘. Differently from APS, PGCS employs Tunslip to send CoAP commands directly to the716
border router and sensor tags. We have also measured the time required by the PGCS to subscribe a717
control file (PGCSFile_1.txt) from NRNCS temporary cache as 9.106 ms.718
5.3.5. Evaluation of IEEE 802.15.4 Interference in the IEEE 802.11 Operation719
This subsection reports the application of NG DSM for switching Wi-Fi AP channels. The720
throughput operating at channel 7 and interfered by a 802.15.4 network was 6.1 Mbps. As illustrated721
in Figure 8, SSS requests the Channel Advisor to sense IEEE 802.11 spectrum. After a best channel722
indication and RMS evaluation, APS has changed the Wi-Fi router to the channel 11. The same723
indication was automatically took by Channel Advisor without the use of NG architecture, for724
comparison purpose. After the channel change, the new TCP/IP/Wi-Fi throughput was 36.08 Mbps,725
a value close to the non-interference condition and similar to the Channel Advisor result.726
Figure 15. Log of SSS service offer to RMS.
5.3.3. The Best Wi-Fi Channel Indication and Changing
After the contract establishment, the DSM services can start operating as designed. Figure 16
reproduces the log of a SSS message sent to RMS indicating the best channel to be used to reduce
interference with IEEE 802.15.4 sensor tags (Figure 8Transaction 4b). RMS publishes the same indication
to APS, which decides a channel change is required or not. Posterior, APS subscribes another file
published with the same information and performs the change using SSH to connect to TL-WDR4300
OpenWRT, as illustrated in Figure 7. We measured the time required by the APS to subscribe a control
file (SSSFile_0.txt) from NRNCS temporary cache as 8.666 ms.
Version September 1, 2018 submitted to Sensors 25 of 34
ng -m--cl 0.1 [<1s15B239D1 ><4s43188DBC A1328D4B BC6F9917 E93185DE >
<4s43188DBC A1328D4B 128AF13D BD122284 >]
ng -p--notify 0.1 [<1s18><1s29133805 > < 1 s SSSFile_0.txt >
<5s pub 43188DBC A1328D4B 603FE845 C8D4702F >]
ng -info --payload 0.1 [<1s SSSFile_0.txt >]
ng -p--b0.1 [<1s2><1s29133805 > < 1 s E93185DE >]
ng -p--b0.1 [<1s2><1s29133805 > < 1 s BC6F9917 >]
ng -p--b0.1 [<1s2><1s29133805 > < 1 s A1328D4B >]
ng -p--b0.1 [<1s9><1s29133805 > < 1 s43188DBC >]
ng -message --type 0.1 [<1s1>]
ng -message --seq 0.1 [<1s51>]
ng -scn --seq 0.1 [<1s D67E8E03 >]
ng -sr --b0.1 [< 1 s17><1s SSS01.best_channel ><3s802.11 Channel 11>]
ng -sr --b0.1 [< 1 s17><1s SSS01.best_channel ><2s Counter 0>]
Figure 16. Log of SSS indicating for RMS the best channel to configure Wi-Fi access point in the region.
Figure 17. Capture of a NG message from RMS to APS using WiresharkTM.
In Figure 17, we reproduce a WiresharkTM capture of a NG message from RMS to APS requiring727
a channel change to channel 3. The control command is in the payload area of the message (latest728
bytes). For this test, the frame Type was configured to 0x1234, which is unknown for Wireshark. The729
mean round trip time (RTT) for subscribing such command messages at APS is depicted in Figure 18.730
Figure 16.
Log of SSS indicating for RMS the best channel to configure Wi-Fi access point in the region.

Sensors 2018,18, 3160 26 of 35
5.3.4. The Best IEEE 802.15.4 Channel Indication and Changing
SSS log changes when indication is related to IEEE 802.15.4 bandwidth. Instead of < 3 s 802.11
Channel 11
>
], SSS publishes < 3 s 802.15.4 Channel 23
>
], for instance. The remaining fields of
Figure 16 are the same. In this case, RMS publishes to PGCS in order to change IoT nodes channels,
including border router‘. Differently from APS, PGCS employs Tunslip to send CoAP commands
directly to the border router and sensor tags. We have also measured the time required by the PGCS to
subscribe a control file (PGCSFile_1.txt) from NRNCS temporary cache as 9.106 ms.
5.3.5. Evaluation of IEEE 802.15.4 Interference in the IEEE 802.11 Operation
This subsection reports the application of NG DSM for switching Wi-Fi AP channels.
The throughput operating at channel 7 and interfered by a 802.15.4 network was 6.1 Mbps. As illustrated
in Figure 8, SSS requests the Channel Advisor to sense IEEE 802.11 spectrum. After a best channel
indication and RMS evaluation, APS has changed the Wi-Fi router to the channel 11. The same indication
was automatically took by Channel Advisor without the use of NG architecture, for comparison purpose.
After the channel change, the new TCP/IP/Wi-Fi throughput was 36.08 Mbps, a value close to the
non-interference condition and similar to the Channel Advisor result.
Figure 17. Capture of a NG message from RMS to APS using WiresharkTM.

Sensors 2018,18, 3160 27 of 35
In Figure 17, we reproduce a Wireshark
TM
capture of a NG message from RMS to APS requiring a
channel change to channel 3. The control command is in the payload area of the message (latest bytes).
For this test, the frame Type was configured to 0
×
1234, which is unknown for Wireshark. The mean
round trip time (RTT) for subscribing such command messages at APS is depicted in Figure 18.
Measurements started at time instant 10452.86 s and remained up to time instant 12056.72 s,
i.e., 26.73 min. A total of 13 measurements were taken. The mean subscription RTT was 14.62 ms.
The error bars present the worst and best results inside a 95% confidence interval, respectively.
In contrast, Figure 19 reports mean RTT of IEEE 802.15.4 channel change messages subscriptions
from PSS to Host 2 PGCS. Measurements started at time instant 13444.28 s and finished at 15450.32 s,
i.e., 33.43 min. The mean subscription RTT was 13.05 ms. These delays demonstrate NG can be
considered as an alternative architecture to deal with DSM of Wi-Fi/IoT in the unlicensed bands. A few
milliseconds RTT look promising for control plane message subscription in a LAN environment.
0.005
0.01
0.015
0.02
0.025
10400
10600
10800
11000
11200
11400
11600
11800
12000
Subscription round trip delay (seconds)
Time (seconds)
Subscription of Channel Change Command at APS
sub
Figure 18.
Mean round trip time of APS subscriptions for Wi-Fi channel changing command from PSS.
Wi-Fi network delay between Host 1 and Host 2 is included twice.
0.005
0.01
0.015
0.02
0.025
13500
14000
14500
15000
15500
Subscription round trip delay (seconds)
Time (seconds)
Subscription of Channel Change Command at PGCS
sub
Figure 19.
Mean round trip time of PGCS subscriptions for IEEE 802.15.4 channel changing command
from PSS. Wi-Fi network delay between Host 1 and Host 2 is included twice.
5.3.6. Evaluation of IEEE 802.11 Interference in the IEEE 802.15.4 Operation
The last test was focused on evaluating the benefits of our NovaGenesis-based approach to the
IEEE 802.15.4 operation when interfered by a Wi-Fi network. The CoAP 802.15.4 throughput was
520 bps under 802.11 interference. PGCS has changed the 802.15.4 network to the channel 23, a channel
free of Wi-Fi interference. The new 802.15.4 throughput increased to 1730 bps, a value even better
than the obtained in the non-interference condition. Table 4summarizes the results obtained for all

Sensors 2018,18, 3160 28 of 35
performed tests. Significant throughput enhancements were obtained not only for Wi-Fi, but also
for IoT nodes. Results for NovaGenesis are similar to the ones obtained with the Channel Advisor.
This proves our NovaGenesis-based control plane represents an alternative to the status quo DSM
technologies, which have limited support for the eight design dimension proposed in Table 1.
The obtained results proof-the-concept of our ICN-based, service-defined (APS and PGCS),
named-data (PSS, GIRS and HTS) trustable IoT/Wi-Fi (service offers and acceptances) spectrum
management approach (APS, RMS and SSS) for future wireless networks. NovaGenesis automates DSM
for heterogeneous wireless networks, with delay of control packets exchanging in milliseconds range.
Table 4. Throughput results before and after changing the channel.
Description Before After Throughput Gain
802.15.4 interference in 802.11 without NG 6.1 Mbps 34.86 Mbps 471%
802.11 interference in 802.15.4 without NG 520 bps 1270 bps 144%
802.11 interference in 802.15.4 with NG 6.1 Mbps 36.08 Mbps 491%
802.15.4 interference in 802.11 with NG 520 bps 1730 bps 233%
6. Discussion on Benefits and Open Challenges
This section returns to the design dimensions presented in Table 1, giving a summary of
NovaGenesis benefits to the problem of trustable unlicensed spectrum management for IoT/Wi-Fi.
Moreover, it points out open challenges for future developments. Table 5summarizes our contributions
for the control plane of new generation WSANs and IoT:
•
D1—Our approach could sense any kind of wireless communication protocol in the HackRF
One SDR operating bandwidth. It provides integrated IoT (IEEE 802.15.4) and Wi-Fi cognitive
radio-based DSM. Our NG-based solution enable to increase network throughput and reduce
interference of Wi-Fi access points in IoT nodes.
•
D2—Secure exchange of spectrum sensing data via trustable DSM services. The novel
security mechanisms proposed by NG, namely self-verifying naming, secure name resolution,
trust network formation, contract-based operation and services reputation allows enhanced
security at smart places. These mechanisms improve traditional security for IoT/FI/CR/5G,
since it takes advantage of social behavior of devices and services.
•
D3—NovaGenesis made possible name-based access and routing of spectrum sensing data,
including network caching for efficient, distributed and coherent software-control (control plane)
of smart environments.
•
D4—Integration of software-defined control and operation [
37
,
49
]. The current SDN model
(based on the OpenFlow protocol) is limited to configure forwarding tables at link layer switches.
NG allowed broader configuration and management of physical devices via their software
representatives (e.g., DSM services). Therefore, NG extends software-defined paradigm towards
exposing hardware capabilities to spectrum management services, enabling “richer” orchestration
of resources.
•
D5—NovaGenesis includes support for the dynamic composition of control plane services based
on semantic and context-awareness. It provides mechanisms for the complete service life-cycling.
Quality of service (QoS) can be measured from the established contracts, enabling estimation of
services reputation. Services with low quality can loose its contracts, losing reputation. QoS and
reputation will be subject of future works. Dynamic composition is available for network data,
control and management planes.
•
D6—NovaGenesis provides increased expressiveness [
16
,
45
], when compared to current
information architectures. NovaGenesis is not the unique architecture concerned to improve
protocols expressiveness, XIA [
16
] is also an example that deals with this issue. However, to the
best of our knowledge, we have first applied XIA for IoT in 2017 [78].

Sensors 2018,18, 3160 29 of 35
•
D7—IoT/Wi-Fi devices, spectrum data and spectrum management services are accessed by
self-verifying identifiers, enabling ID-consistent mobility and guaranteeing provenance of
spectrum data [21,22].
•
D8—NovaGenesis provides contract-based operation of spectrum management services.
The control of IoT/Wi-Fi unlicensed band channels is service-defined, contract-based
and trustable.
This paper extended Alberti et al. [
33
] scenario to deal with the dual-mode (Wi-Fi/IEEE 802.15.4)
best channel indications from a Channel Advisor middleware. We plan to extend this DSM approach to
other next generation WSANs, such as LoRa, Sigfox, NB-IoT, etc. Operation in licensed band is also an
open issue. Furthermore, this article extended NovaGenesis control plane for CR/IoT/Wi-Fi. However,
NG data plane is limited to Ethernet and Wi-Fi. We have been developing extensions for LoRa, ZigBee,
passive-optical networks and IEEE 802.15.4. In other words, we have been developing a NovaGenesis
over X (NGoX) adaptation layer, which will enable PGCS to interoperate with embedded NG versions,
such as the embedded proxy/gateway service (EPGS) proposed in [
4
]. In this context, embedding
NovaGenesis at GNU Radio, IoT nodes and access points are still in its infancy. We plan to introduce
NovaGenesis in all devices of Figure 7, eliminating SSH, ZMQ, Tunslip and CoAP need. We indeed
envision the need to improve spectrum sensing to a cooperative approach with NovaGenesis.
There is an inherent complexity behind the integration of so many ingredients, such as IoT,
ICN, SDN/NFV, SOA, cognitive radio, dynamic spectrum management, etc. In this context, an open
challenge is how to model the complexity behind existent/novel architectures in order to enable fair
comparison. NovaGenesis brings to the core many ingredients usually found at the world wide web of
the current Internet. For instance, distribute hash table (DHT) is typically implemented over TCP/IP.
NovaGenesis implements DHT directly over link layer protocols, in the core. The same applies for
MQTT, an IoT publish/subscribe approach implemented over TCP/IP. NovaGenesis implements
pub/sub in the core, allowing any application to take advantage of this model. Therefore, to be fair,
the same features should be present in both stacks being compared. It means we need to consider the
overhead of application protocols of the Internet in the evaluation.
Another point is performance evaluation of ICN caches. Fair comparisons require complete
scenarios, in which data/control plane information is transferred not to an unique client, but to many
in a coherent and efficient way. It is in this type of situation that the new proposals are promising,
taking advantage of ICN caches for coherent distributed deliveries. In summary, the evaluation of
system performance should consider functionalities that are being covered, including aspects related
to distributed coordination, data delivery and coherence of control plane. Methodologies for these
aims provide interesting research opportunities and are topics for future research.
We also have plan to improve NovaGenesis performance by: (i) adding source coding to NG
messages; (ii) exploring load balancing and multi-path routing; (iii) elasticity of NovaGenesis services;
(iv) refinement of the prototype; (v) employ different hash code sizes to reduce overhead in NG
messages; (vi) implement hierarchical multi-domain/level name resolution, network caching and
name-based routing. These improvements promise a better performance when compared to the current
solutions. For this reason, they will be the target of future work.
Scalability of route-by-name approaches with hierarchical networking caching is another
important aspect to be evaluated in ICN-based approaches [
65
]. Moreover, our solution will require
an evaluation regarding mobile nodes, such as the one performed by Siris et al. [
68
]. Reproducibility
of experiments is another open issue, as well as to experiment with larger numbers of IoT nodes,
Wi-Fi APs and spectrum sensing cells. Recent work on experimental facilities can help on improving
such issues. Another future work is to prepare NovaGenesis scenarios to be tested in larger testbeds
in Europe, USA and Brazil. A challenge is how to port NovaGenesis services to different hardware
available in these experimental facilities.

Sensors 2018,18, 3160 30 of 35
Table 5.
NovaGenesis DSM for IoT/Wi-Fi. D1— Dynamic spectrum management with cognitive radio; D2—Secure exchange of control data via trustable services;
D3—Named-control-data access and routing; D4—Software-defined control and operation; D5—Dynamic composition of control services; D6—Improved naming and
name resolution for IoT; D7—Identifier/locator splitting for architecture entities; D8—Contract-based control plane.
Approach Taken Benefits for Smart Environments Contributions to State-Of-The-Art
D1
Protocol-agnostic best channel indication based on the
radio frequency energy of operational channels.
Exposition of spectrum sensing and channel control
services in IoT and Wi-Fi.
Programmability [9], improved
expressiveness [16–18,45], flexibility and cohesive
integration to IoT.
ISM band spectrum sensing and best channel indication as a service.
Dual mode (Wi-Fi/IEEE 802.15.4) operation.
D2
Asynchronous and distributed access to control data
using self-verifying names [21] and name-based
forwarding, routing and delivery of spectrum
control data.
Coherence of control actions, security (integrity) of
control messages [
23
], provenance of control data [
22
].
All these features are determined in terms of control
file SVNes.
First application of ICN paradigms to control and management of
DSM in IoT/Wi-Fi.
D3
Access to control files is given by name bindings
published in NRNCS. Representatives of controlled
devices (PGCS and APS) are notified and subscribe
about control files. Queries follow a path to the NRNCS
instance. Control files are delivered by HTS directly to
PGCS and APS.
In-network name-based coordination of services [20],
in-network caching of control files [20],
asynchronous/coherent IoT/Wi-Fi command
execution, name-based security [21], efficiency of
control dissemination [58], unbounded
namespaces [58].
A convergent ICN, CR and SOA approach for WSANs and IoT
control plane. Suarez et al. [20] apply ICN for IoT management,
including registration and discovery of devices, command execution
and retrieval of measured data. Besides these features, our work
integrates ICN with SOA and CR, advancing life cycle of
control services.
D4
An alternative to OpenFlow SDN is employed to chance
configurations at Wi-Fi access points and IEEE 802.15.4
sensor tags. This alternative is generic, flexible and
adequate to support command execution on
IoT/Wi-Fi devices.
Flexibility, self-configuring, improved controllability
and management, support for dynamic QoS [37,49].
An alternative to SDN/NFV for IoT. CORAL-SDN embeds a
programmable data plane at IoT nodes [37,49]. It also leverages a
modified controller to support IoT nodes topology control, routing
and flow establishment, as well as data collection. However,
CORAL-SDN neither covers DSM, nor employs ICN at the
control plane.
D5
To apply SOA principles for IoT/WSAN control plane.
DSM and IoT services can expose their features, search
for partners and form trust networks based on a service
level agreement.
Context-awareness, contract-based operation,
integration of heterogeneous devices and
middlewares, self-organization and coordinated
orchestration [45].
In [45], discovery and control services are developed, but none
related to coexistence of RF signals. An alternative to the IP-based
WSN SOA architecture proposed in [79]
D6
Support for spectrum data, control and services naming
and name resolution via the hierarchical,
distributed, NRNCS.
The improved expressiveness allows DSM/IoT
services to express their keywords, names (natural
language and self-verified) and service offers to
possible peers.
Besides ICN and SCN provided by XIA [16], NG employs SOA and
contract-based operation. In addition, XIA has not been applied for
cognitive radio yet.
D7
To decouple entities identifiers (IDs) from locators
(Locs), enabling direct entities access via IDs,
independently of their locations (LOCs).
Mobility without identity loss [36]. Perennial
identification of data, devices and services.
In [20], ID/Loc splitting is provided to IoT management. Our work
provides a generic ID/Loc splitting approach to all architectural
entities, including devices and services.
D8
A novel service-defined approach to allow exposing
best channel indication (or spectrum sensing) features
to DSM services.
Trust-ability, security, reputation of control services.
An ecosystem of trustable services for IoT/WSAN control plane.
Suarez et al. [
20
] also provides ICN-based SLAs. A difference is that
NG binds contract-names to service names, improving security.

Sensors 2018,18, 3160 31 of 35
For networks larger than a small campus network, more instances of spectrum sensors, best
channel indicators and channel changing services will be required. The hardware part of our DSM
solution can be scaled by adding more devices to the deployment. The software part of the our proposal
can be scaled by employing elasticity of edge computing as proposed by Righi et al. [
80
]. Moreover,
previous results obtained for NovaGenesis service-oriented, self-organizing, dynamic composable
distributed architecture [
4
,
11
,
33
] indicate that it can deal with new software instances as the number of
physical devices increases. However, a large scale experiment with hundreds/thousands of nodes is a
quite challenging research task, which is being addressed by the authors as future works.
7. Conclusions
This paper presented, for the first time, a successful application of a future Internet architecture
for the integrated (IoT/Wi-Fi) dynamic spectrum management of wireless devices (access points and
sensor nodes) in a smart campus scenario, which can be extended to a larger scale. We have extended
previous NovaGenesis services [
33
] to provide ISM unlicensed band best channel indications not only
for IEEE 802.11, but also for IEEE 802.15.4 standard.
A spectrum sensing service was extended to expose dual-mode (Wi-Fi/IoT) best channel
indication feature to other NovaGenesis dynamic spectrum management services. SSS interoperates
with Channel Advisor, which has a GNU Radio implementation for determining energy level at
Wi-Fi/IoT unlicensed bandwidths. A resource management service was extended to deal with this
dual mode operation. A novel APS was developed to represent Wi-Fi APs towards establishing control
plane contracts to a RMS instance. Existing PGCS was extended to represent IEEE 802.15.4 Texas
Instruments cc2650 sensor tags and border routers. PGCS establishes control plane contracts to a
RMS instance, making necessary protocol translations and encapsulations to change IoT nodes radio
frequency channels.
In summary, our approach enables coordination of any group of devices operating with different
wireless communication standards. The overall architecture was idealized, implemented and tested
in a real scenario to proof two main hypotheses: (i) the Channel Advisor improves throughput
when acting automatically in a specific wireless network standard; (ii) NovaGenesis obtains the same
results, but automating DSM in a contract-based way for any group of wireless network standards.
Both hypotheses were tested in open field and validated. Channel Advisor operating separated
in one only network showed results that improved the 802.11 throughput in 4.71 times and the
802.15.4 throughput in 1.44 times. When checking the efficient of NovaGenesis acting automatically in
both networks, the throughput improved in 802.11 was 4.91 times compared to the interfered rate and
the 802.15.4 improvement was 2.33 times.
These results demonstrate NovaGenesis-based control plane can be seen as an alternative to
the status quo DSM technologies, which have limited support for the previously reported design
dimensions. The control packets subscription delays remained limited to a few milliseconds in the
evaluated scenario, which are promising values for real applications. Future works include continuing
extending this solution towards: (i) other next generation sensor and wireless networks, such as LoRa,
Sigfox, NB-IoT, etc; (ii) integration with licensed bands for IoT; (iii) develop a NovaGenesis over X
(NGoX) adaptation layer; (iv) embedding NovaGenesis at GNU Radio, IoT nodes and access points;
(v) improve spectrum sensing to a cooperative approach with NovaGenesis; (vi) increase scalability
and reproducibility of experiments.
Author Contributions:
A.M.A is the designer and developer of NovaGenesis and wrote the majority of the
paper. M.M.B is the designer and developer of the cognitive radio system. A.M.A and M.M.B provided previous
work discussion. M.M.B and J.R.d.S conceived, designed and performed the experiments. A.M.A and M.M.B
analyzed the data and wrote the results section. J.R.d.S organized the equipment, materials, real scenario
arrangements. A.C.S.J and R.d.R.R contributed with the paper proposal, structuring, language, detailed review,
scientific contributions and conclusion evaluation. A.C.S.J and R.d.R.R contributed to NovaGenesis and cognitive
radio system design.

Sensors 2018,18, 3160 32 of 35
Funding:
This research was funded by Finep with resources from Funttel, Grant No. 01.14.0231.00, under the
Radiocommunication Reference Center (Centro de Referência em Radiocomunicações—CRR) project of the
National Institute of Telecommunications (Instituto Nacional de Telecomunicações—Inatel), Brazil.
Acknowledgments:
Authors also thank the financial support from CNPq (Grant No. 457501/2014-6), CAPES,
MCTIC, CGI and FAPEMIG.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Ebert, C.; Duarte, C.H.C. Digital Transformation. IEEE Softw. 2018,35, 16–21. [CrossRef]
2.
Ashton, K. That ’Internet of Things’ Thing. Available online: https://www.rfidjournal.com/articles/view?
4986 (accessed on 10 September 2018).
3.
Alberti, A.M.; Casaroli, M.A.F.; Singh, D.; da Rosa Righi, R. Naming and name resolution in the future
internet: Introducing the NovaGenesis approach. Future Gener. Comput. Syst. 2017,67, 163–179. [CrossRef]
4.
Alberti, A.M.; Scarpioni, G.D.; Magalhaes, V.J.; Cerqueira, S.A.; Rodrigues, J.J.P.C.; Righi, R.D.R. Advancing
NovaGenesis Architecture Towards Future Internet of Things. IEEE Internet Things J. 2017. [CrossRef]
5.
McKeown, N.; Anderson, T.; Balakrishnan, H.; Parulkar, G.; Peterson, L.; Rexford, J.; Shenker, S.; Turner, J.
OpenFlow: Enabling Innovation in Campus Networks. SIGCOMM Comput. Commun. Rev.
2008
,38, 69–74.
[CrossRef]
6. Sun, S.; Kadoch, M.; Gong, L.; Rong, B. Integrating network function virtualization with SDR and SDN for
4G/5G networks. IEEE Netw. 2015,29, 54–59. [CrossRef]
7.
Gardikis, G.; Costicoglou, S.; Koumaras, H.; Sakkas, C.; Kourtis, A.; Arnal, F.; Contreras, L.M.; Gutierrez, P.A.;
Guta, M. NFV applicability and use cases in satellite networks. In Proceedings of the In 2016 European
Conference on Networks and Communications (EuCNC), Athens, Greece, 27–30 June 2016; pp. 47–51.
8.
Mueck, M.; Strinati, E.C.; Kim, I.G.; Clemente, A.; Dore, J.B.; Domenico, A.D.; Kim, T.; Choi, T.; Chung, H.K.;
Destino, G.; et al. 5G CHAMPION—Rolling out 5G in 2018. In Proceedings of the IEEE Globecom Workshops
(GC Wkshps), Washington, DC, USA, 4–8 December 2016; pp. 1–6.
9.
Lin, Y.B.; Wang, S.Y.; Huang, C.C.; Wu, C.M. The SDN Approach for the Aggregation/Disaggregation of
Sensor Data. Sensors 2018,18, 2025. [CrossRef] [PubMed]
10.
Taleb, T.; Samdanis, K.; Mada, B.; Flinck, H.; Dutta, S.; Sabella, D. On multi-access edge computing: A survey
of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutor.
2017
,
19, 1657–1681. [CrossRef]
11.
Alberti, A.M. A conceptual-driven survey on future internet requirements, technologies, and challenges.
J. Braz. Comput. Soc. 2013,19, 291–311. [CrossRef]
12. Partridge, C. Helping a Future Internet Architecture Mature. SIGCOMM Comput. Commun. Rev. 2013,44, 50–52.
[CrossRef]
13.
FI-PPP. Future Internet—Public-Private Partnership. Available online: https://ec.europa.eu/digital-single-
market/en/news/future-internet-public-private-partnership-fi-ppp-%E2%80%93-community-building-
webinar (accessed on 6 September 2018).
14.
5G-PPP. 5G—Public-Private Parnership. Available online: https://www.crunchbase.com/organization/5g-
infrastructure-public-private-partnership (accessed on 6 September 2018).
15.
Ahlgren, B.; Aranda, P.; Chemouil, P.; Oueslati, S.; Correia, L.; Karl, H.; Söllner, M.; Welin, A. Content, connectivity,
and cloud: Ingredients for the network of the future. Commun. Mag. IEEE 2011,49, 62–70. [CrossRef]
16.
Han, D.; Anand, A.; Dogar, F.; Li, B.; Lim, H.; Machado, M.; Mukundan, A.; Wu, W.; Akella, A.; Andersen, D.G.; et al.
XIA: Efficient Support for Evolvable Internetworking. In Proceedings of the 9th USENIX Conf. on Networked
Systems Design and Implementation. USENIX, 2012, NSDI’12, San Jose, CA, USA, 25–27 April 2012; p. 23.
17.
Dannewitz, C.; Kutscher, D.; Ohlman, B.; Farrell, S.; Ahlgren, B.; Karl, H. Network of Information
(NetInf)—An Information-centric Networking Architecture. Comput. Commun.
2013
,36, 721–735. [CrossRef]
18.
Jacobson, V.; Smetters, D.K.; Thornton, J.D.; Plass, M.F.; Briggs, N.H.; Braynard, R.L. Networking Named
Content. In Proceedings of the 5th International Conference on Emerging Networking Experiments and
Technologies, Rome, Italy, 1–4 December 2009; pp. 1–12.
19.
Ahlgren, B.; Dannewitz, C.; Imbrenda, C.; Kutscher, D.; Ohlman, B. A survey of information-centric
networking. IEEE Commun. Mag. 2012,50. [CrossRef]

Sensors 2018,18, 3160 33 of 35
20.
Suarez, J.; Quevedo, J.; Vidal, I.; Corujo, D.; Garcia-Reinoso, J.; Aguiar, R.L. A Secure IoT Management
Architecture Based on Information-Centric Networking. J. Netw. Comput. Appl.
2016
,63, 190–204. [CrossRef]
21.
Ghodsi, A.; Koponen, T.; Rajahalme, J.; Sarolahti, P.; Shenker, S. Naming in Content-oriented Architectures.
In Proceedings of the ACM SIGCOMM Workshop on Information-centric Networking, Toronto, ON, Canada,
15–19 August 2011; pp. 1–6.
22.
Mick, T.; Tourani, R.; Misra, S. LASeR: Lightweight Authentication and Secured Routing for NDN IoT in
Smart Cities. IEEE Internet Things J. 2018,5, 755–764. [CrossRef]
23.
Sicari, S.; Rizzardi, A.; Grieco, L.A.; Coen-Porisini, A. A secure ICN-IoT architecture. In Proceedings of
the 2017 IEEE International Conference on Communications Workshops (ICC Workshops), Paris, France,
21–25 May 2017; pp. 259–264.
24.
Khozeimeh, F.; Haykin, S. Dynamic spectrum management for cognitive radio: An overview. Wirel. Commun.
Mob. Comput. 2009,9, 1447–1459. [CrossRef]
25.
Mitola, J.; Maguire, G.Q. Cognitive radio: Making software radios more personal. IEEE Pers. Commun.
1999
,
6, 13–18. [CrossRef]
26.
Si, P.; Yue, H.; Zhang, Y.; Fang, Y. Spectrum Management for Proactive Video Caching in Information-Centric
Cognitive Radio Networks. IEEE J. Sel. Areas Commun. 2016,34, 2247–2259. [CrossRef]
27.
Quevedo, J.; Corujo, D.; Aguiar, R. A case for ICN usage in IoT environments. In Proceedings of the 2014 IEEE
Global Communications Conference (GLOBECOM), Austin, TX, USA, 8–12 December 2014; pp. 2770–2775.
28.
Li, S.; Zhang, Y.; Raychaudhuri, D.; Ravindran, R. A comparative study of MobilityFirst and NDN based
ICN-IoT architectures. In Proceedings of the 2014 10th International Conference on Heterogeneous Networking
for Quality, Reliability, Security and Robustness (QShine), Rhodes, Greece, 18–20 August 2014; pp. 158–163.
29.
Xylomenos, G.; Ververidis, C.N.; Siris, V.A.; Fotiou, N.; Tsilopoulos, C.; Vasilakos, X.; Katsaros, K.V.; Polyzos, G.C.
A survey of information-centric networking research. IEEE Commun. Surv. Tutor.
2014
,16, 1024–1049. [CrossRef]
30.
Zhang, L.; Afanasyev, A.; Burke, J.; Jacobson, V.; Crowley, P.; Papadopoulos, C.; Wang, L.; Zhang, B.
Named data networking. ACM SIGCOMM Comput. Commun. Rev. 2014,44, 66–73. [CrossRef]
31.
Vrijders, S.; Staessens, D.; Colle, D.; Salvestrini, F.; Grasa, E.; Tarzan, M.; Bergesio, L. Prototyping the
recursive internet architecture: The IRATI project approach. IEEE Netw. 2014,28, 20–25. [CrossRef]
32.
Fotiou, N.; Nikander, P.; Trossen, D.; Polyzos, G.C. Developing information networking further: From PSIRP to
PURSUIT. In Broadband Communications, Networks, and Systems; Springer: New York, NY, USA, 2012; pp. 1–13.
33.
Alberti, A.M.; Mazzer, D.; Bontempo, M.M.; de Oliveira, L.H.; da Rosa Righi, R.; Sodré, A.C., Jr.
Cognitive radio in the context of internet of things using a novel future internet architecture called
NovaGenesis. Comput. Electr. Eng. 2017,57, 147–161. [CrossRef]
34.
Sun, L.; Li, Y.; Memon, R.A. An open IoT framework based on microservices architecture. China Commun.
2017,14, 154–162. [CrossRef]
35.
Nordström, E.; Shue, D.; Gopalan, P.; Kiefer, R.; Arye, M.; Ko, S.; Rexford, J.; Freedman, M.J.
Serval: An End-Host Stack for Service-Centric Networking. In Proceedings of the 9th USENIX Conference
on Networked Systems Design and Implementation. USENIX, 2012, NSDI’12, San Jose, CA, USA,
25–27 April 2012; pp. 85–98.
36.
Ramirez, W.; Masip-Bruin, X.; Yannuzzi, M.; Serral-Gracia, R.; Martinez, A.; Siddiqui, M. A survey and
taxonomy of ID/Locator Split Architectures. Comput. Netw. 2014,60, 13–33. [CrossRef]
37.
Manzanares-Lopez, P.; Malgosa-Sanahuja, J.; Muñoz-Gea, J.P. A Software-Defined Networking Framework
to Provide Dynamic QoS Management in IEEE 802.11 Networks. Sensors
2018
,18, 2247. [CrossRef] [PubMed]
38.
Lin, Z.; Tao, D.; Wang, Z. Dynamic Construction Scheme for Virtualization Security Service in Software-
Defined Networks. Sensors 2017,17, 920. [CrossRef] [PubMed]
39.
Usman, M.; Khan, M.S.; Vu-Van, H.; Insoo, K. Energy-Efficient Channel Handoff for SensorNetwork-Assisted
Cognitive Radio Network. Sensors 2015,15, 18012–18039. [CrossRef] [PubMed]
40.
Ozger, M.; Cetinkaya, O.; Akan, O.B. Energy Harvesting Cognitive Radio Networking for IoT-enabled Smart
Grid. Mob. Netw. Appl. 2017,23, 956–966. [CrossRef]
41.
Zhang, L.; Cai, Z.; Li, P.; Wang, L.; Wang, X. Spectrum-Availability Based Routing for Cognitive Sensor
Networks. IEEE Access 2017,5, 4448–4457. [CrossRef]
42.
Piro, G.; Ribezzo, G.; Grieco, L.A.; Blefari-Melazzi, N. A De-verticalizing Middleware for IoT Systems Based
on Information Centric Networking Design. In Proceedings of the International Tyrrhenian Workshop on
Digital Communication, Mondello, Italy, 18–20 September 2017; pp. 197–211.

Sensors 2018,18, 3160 34 of 35
43.
Nour, B.; Sharif, K.; Li, F.; Moungla, H. A Distributed ICN-Based IoT Network Architecture: An Ambient
Assisted Living Application Case Study. In Proceedings of the GLOBECOM 2017–2017 IEEE Global
Communications Conference, Singapore, 4–8 December 2017; pp. 1–6.
44.
Dong, L.; Wang, G. A robust and lightweight name resolution approach for IoT data in ICN. In Proceedings
of the 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy,
4–7 July 2017; pp. 61–65.
45.
Cubo, J.; Nieto, A.; Pimentel, E. A Cloud-Based Internet of Things Platform for Ambient Assisted Living.
Sensors 2014,14, 14070–14105. [CrossRef] [PubMed]
46.
Zhang, H.; Chu, X.; Guo, W.; Wang, S. Coexistence of Wi-Fi and heterogeneous small cell networks sharing
unlicensed spectrum. IEEE Commun. Mag. 2015,53, 158–164. [CrossRef]
47.
Eze, J.; Zhang, S.; Liu, E.; Eze, E. Cognitive radio-enabled Internet of Vehicles: A cooperative spectrum
sensing and allocation for vehicular communication. IET Netw. 2018,7, 190–199. [CrossRef]
48.
Dong, L.; Wang, G. Consumer oriented IoT data discovery and retrieval in information centric networks.
In Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile
Radio Communications (PIMRC), Montreal, QC, Canada, 8–13 October 2017; pp. 1–7.
49.
Theodorou, T.; Mamatas, L. CORAL-SDN: A software-defined networking solution for the Internet of Things.
In Proceedings of the 2017 IEEE Conference on Network Function Virtualization and Software Defined
Networks (NFV-SDN), Berlin, Germany, 6–8 November 2017; pp. 1–2.
50.
Ruckebusch, P.; Bauwens, J.; Jooris, B.; Giannoulis, S.; Poorter, E.D.; Moerman, I.; Garlisi, D.; Gallo, P.;
Tinnirello, I. Cross-technology wireless experimentation: Improving 802.11 and 802.15.4e coexistence.
In Proceedings of the 2016 IEEE 17th International Symposium on A World of Wireless, Mobile and
Multimedia Networks (WoWMoM), Coimbra, Portugal, 21–24 June 2016; pp. 1–3.
51.
Balid, W.; Kalaa, M.O.A.; Rajab, S.; Tafish, H.; Refai, H.H. Development of measurement techniques and tools
for coexistence testing of wireless medical devices. In Proceedings of the 2016 IEEE Wireless Communications
and Networking Conference (WCNC), Doha, Qatar, 3–6 April 2016; pp. 449–454.
52.
Ding, K.; Zhao, H.; Hu, X.; Wei, J. Distributed Channel Allocation and Time Slot Optimization for Green
Internet of Things. Sensors 2017,17, 2479. [CrossRef] [PubMed]
53.
Moon, B. Dynamic Spectrum Access for Internet of Things Service in Cognitive Radio-Enabled LPWANs.
Sensors 2017,17, 2818. [CrossRef] [PubMed]
54.
Sliwa, J.; Matyszkiel, R.; Jach, J. Efficient Methods of Radio Channel Access Using Dynamic Spectrum Access
That Influences SOA Services Realization—Experimental Results. In Proceedings of the 2015 IEEE 81st
Vehicular Technology Conference (VTC Spring), Glasgow, UK, 11–14 May 2015; pp. 1–5.
55.
Angrisani, L.; Bertocco, M.; Fortin, D.; Sona, A. Experimental study of coexistence issues between IEEE
802.11 b and IEEE 802.15. 4 wireless networks. IEEE Trans. Instrum. Meas. 2008,57, 1514–1523. [CrossRef]
56.
Won, C.; Youn, J.; Ali, H.; Sharif, H.; Deogun, J. Adaptive radio channel allocation for supporting coexistence
of 802.15.4 and 802.11b. In Proceedings of the IEEE Vehicular Technology Conference, Dallas, TX, USA,
28 September 2005; Volume 62, p. 2522.
57.
Alshinina, R.; Elleithy, K. Performance and Challenges of Service-Oriented Architecture for Wireless Sensor
Networks. Sensors 2017,17, 536. [CrossRef] [PubMed]
58.
Adhatarao, S.S.; Arumaithurai, M.; Kutscher, D.; Fu, X. ISI: Integrate Sensor Networks to Internet With ICN.
IEEE Internet Things J. 2018,5, 491–499. [CrossRef]
59.
Duan, Q.; Ansari, N.; Toy, M. Software-defined network virtualization: An architectural framework for
integrating SDN and NFV for service provisioning in future networks. IEEE Netw.
2016
,30, 10–16. [CrossRef]
60.
Siris, V.A.; Vasilakos, X.; Polyzos, G.C. Efficient proactive caching for supporting seamless mobility.
arXiv 2014, arXiv:1404.4754.
61.
Marias, G.F.; Fotiou, N.; Polyzos, G.C. Efficient information lookup for the Internet of Things. In Proceedings
of the 2012 IEEE International Symposium on World of Wireless, Mobile and Multimedia Networks
(WoWMoM), San Francisco, CA, USA, 25–28 June 2012; pp. 1–6.
62.
Vasilakos, X.; Katsaros, K.; Xylomenos, G. Cloud computing for global name-resolution in information-centric
networks. In Proceedings of the 2012 Second Symposium on Network Cloud Computing and Applications
(NCCA), London, UK, 3–4 December 2012; pp. 88–94.
63.
Miorandi, D.; Sicari, S.; De Pellegrini, F.; Chlamtac, I. Internet of things: Vision, applications and research
challenges. Ad Hoc Netw. 2012,10, 1497–1516. [CrossRef]

Sensors 2018,18, 3160 35 of 35
64.
Yue, H.; Guo, L.; Li, R.; Asaeda, H.; Fang, Y. DataClouds: Enabling Community-Based Data-Centric Services
Over the Internet of Things. IEEE Internet Things J. 2014,1, 472–482. [CrossRef]
65.
Katsaros, K.V.; Vasilakos, X.; Okwii, T.; Xylomenos, G.; Pavlou, G.; Polyzos, G.C. On the Inter-domain
Scalability of Route-by-name Information-Centric Network Architectures. In Proceedings of the 2015 IFIP
Networking Conference (IFIP Networking), Toulouse, France, 20–22 May 2015; pp. 1–9.
66.
Koponen, T.; Chawla, M.; Chun, B.G.; Ermolinskiy, A.; Kim, K.H.; Shenker, S.; Stoica, I. A Data-oriented
(and Beyond) Network Architecture. SIGCOMM Comput. Commun. Rev. 2007,37, 181–192. [CrossRef]
67.
Chai, W.K.; Wang, N.; Psaras, I.; Pavlou, G.; Wang, C.; de Blas, G.G.; Ramon-Salguero, F.J.; Liang, L.;
Spirou, S.; Beben, A.; Hadjioannou, E. Curling: Content-ubiquitous resolution and delivery infrastructure
for next-generation services. IEEE Commun. Mag. 2011,49, 112–120. [CrossRef]
68.
Siris, V.A.; Vasilakos, X.; Polyzos, G.C. Efficient proactive caching for supporting seamless mobility.
In Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks,
Sydney, Australia, 19 June 2014; pp. 1–6.
69.
Li, J.; Shvartzshnaider, Y.; Francisco, J.A.; Martin, R.P.; Nagaraja, K.; Raychaudhuri, D. Delivering Internet-
of-Things Services in MobilityFirst Future Internet Architecture. In Proceedings of the 2012 3rd IEEE
International Conference on the Internet of Things, Wuxi, China, 24–26 October 2012; pp. 31–38.
70.
Yates, R.D. Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 2nd ed.;
Yates, R.D., Goodman, D.J., Eds.; John Wiley & Sons: New York, NY, USA, 1984.
71. Urkowitz, H. Energy detection of unknown deterministic signals. Proc. IEEE 1967,55, 523–531. [CrossRef]
72.
Nadler, B.; Penna, F.; Garello, R. Performance of eigenvalue-based signal detectors with known and
unknown noise level. In Proceedings of the 2011 IEEE International Conference on Communications (ICC),
Kyoto, Japan, 5–9 June 2011; pp. 1–5.
73.
Alberti, A.M.; do Rosário, E.C.; Cassiano, G.; dos Santos, J.R.; D’Ávila, V.H.; Carneiro, J.R. Performance evaluation
of NovaGenesis information-centric network. In Proceedings of the 2017 2nd International Multidisciplinary
Conference on Computer and Energy Science (SpliTech), Split, Croatia, 12–14 July 2017; pp. 1–6.
74.
Alberti, A.; de O Fernandes, V.; Casaroli, M.; de Oliveira, L.; Pedroso, F.; Singh, D. A NovaGenesis proxy/
gateway/controller for OpenFlow software defined networks. In Proceedings of the 2014 10th International
Conference on Network and Service Management (CNSM), Rio de Janeiro, Brazil, 17–21 November 2014;
pp. 394–399.
75.
Alberti, A.M.; dos Santos, J.R.; Morais, E.; Magalhães, V.; Santos, R. Forwarding/Routing with Dual Names:
The NovaGenesis Approach. In Proceedings of the IX Workshop de Pesquisa Experimental da Internet do
Futuro (WPEIF-SBRC 2018), Campos do Jordão, Brazil, 6–10 May 2018.
76.
Dunkels, A.; Gronvall, B.; Voigt, T. Contiki-a lightweight and flexible operating system for tiny networked
sensors. In Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks,
Tampa, FL, USA, 16–18 November 2004; pp. 455–462.
77.
Kim, C.M.; Kang, H.W.; Choi, S.I.; Koh, S.J. Implementation of CoAP/6LoWPAN over BLE Networks for IoT
Services. J. Broadcast Eng. 2016,21, 298–306. [CrossRef]
78.
dos Santos Chaib, R.P.; Alberti, A.M. A Simple Solution for IoT Experimentation in the Context of Future
Internet Architectures. In Proceedings of the VIII Workshop de Pesquisa Experimental da Internet do Futuro
(WPEIF - SBRC 2017), Belem, Brazil, 15–19 May 2017; pp. 27–30.
79.
Gustafsson, J.; Kyusakov, R.; Mäkitaavola, H.; Delsing, J. Application of Service Oriented Architecture for
Sensors and Actuators in District Heating Substations. Sensors
2014
,14, 15553–15572. [CrossRef] [PubMed]
80.
da Rosa Righi, R.; Rodrigues, V.F.; da Costa, C.A.; Galante, G.; de Bona, L.C.E.; Ferreto, T. AutoElastic:
Automatic Resource Elasticity for High Performance Applications in the Cloud. IEEE Transa. Cloud Comput.
2016,4, 6–19. [CrossRef]
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