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RESEARCH ARTICLE
Energy-efficient fog computing in Internet of Things based
on Routing Protocol for Low-Power and Lossy Network
with Contiki
Arun Kumar
1
| Sharad Sharma
1
| Nitin Goyal
2
| Sachin Kumar Gupta
3
|
Saru Kumari
4
| Sachin Kumar
5
1
Department of Electronics and
Communication Engineering, Maharishi
Markandeshwar (Deemed to be
University), Mullana, Haryana, India
2
Chitkara University Institute of
Engineering and Technology, Chitkara
University, Rajpura, Punjab, India
3
School of Electronics and
Communication Engineering, Shri Mata
Vaishno Devi University, Katra, India
4
Department of Mathematics, Chaudhary
Charan Singh University, Meerut, India
5
Department of Computer Science and
Engineering, Ajay Kumar Garg
Engineering College, Ghaziabad, India
Correspondence
Sachin Kumar Gupta, School of
Electronics and Communication
Engineering, Shri Mata Vaishno Devi
University, Katra, Jammu and Kashmir
182320, India.
Email: sachin.gupta@smvdu.ac.in
Summary
The traditional centralized cloud computing (CC) model faces a range of prob-
lems with the exponential growth of the Internet of Things (IoT) applications,
like high latency, reduced bandwidth, and network instability. Fog computing
(FC) takes the cloud closer to IoT computers to overcome these problems.
Rather than moving them to the cloud, the FC provides local IoT data
processing and storage on IoT computers. This paper focuses on the Routing
Protocol for Low-Power and Lossy Network (RPL), a universally routing proto-
col for a static environment that is used for reducing energy consumption,
delay, and packet loss with data aggregation at the border router using a fog
simulation model in Contiki Cooja. The objective function (OF) choice has an
impact on network topology, as each node selects a set of potential parents to
send to the destination. However, no systematic analysis of the effects of OF
behavior in the RPL environment has been undertaken. Here, three different
OFs, Objective Function Zero (OF0), Advanced Objective Function Zero
(AOF0), and Minimum Rank with Hysteresis Objective Function (MRHOF)
for RPL in the static environment for different node numbers, have been com-
pared. The findings demonstrate that altering all three OFs has a significant
impact on RPL. The energy consumption is reduced in the case of the AOF0 in
the fog node by 50.86%, which is less than the case of the OF0 and MRHOF
function. Extensive simulations show that AOF0 outperforms the existing OFs.
KEYWORDS
AOF0, cloud computing, Contiki, destination-oriented directed acyclic graph (DODAG),
edge computing, energy conservation, fog computing, IoT, RPL
1|INTRODUCTION
The emergence of Internet of Things (IoT) associates millions of computers and sensors, creating large quantities of
data within themselves. However, due to problems with latency, bandwidth, and power, computational models near
the edge is needed to measure and quantify the output.
1
Fog computing (FC) is an extended form of cloud computing
(CC), extending its features to the end-user level. The volume of mobile traffic demands stability and a broad regional
Received: 30 November 2020 Revised: 10 June 2021 Accepted: 2 November 2021
DOI: 10.1002/dac.5049
Int J Commun Syst. 2021;e5049. wileyonlinelibrary.com/journal/dac © 2021 John Wiley & Sons Ltd. 1of21
https://doi.org/10.1002/dac.5049
range that FC supports. In this paper, an innovative arrangement of IoT application engineering with fog is seen to
entrance numerous components of IoT at the application level. In this manner, the correspondence between IoT gad-
gets and IoT assets ought to be improved. By setting a cloud server in the middle of edge gadgets and cloud layers, it
can boost IoT benefits methodically. But it was the result of a need to tackle two problems, real-time operation, and
incoming data intervention, and resource constraints such as bandwidth and processing capacity, another aspect that
benefits FC. The improved computing capability that vendors integrate into their edge routers and switches allows this
upgrade to the data-path hierarchy. IoT is one of the technologies at the forefront that has the power to offer endless
advantages to our culture. The creation of the IoT is about to enter a point where several of the artifacts around us will
be able to interact with each other via the Internet with no interference by humans.
2
Included as fogging, FC spreads from a centralized cloud to the virtual edge layer for mobile devices, data, and ser-
vices. The fog networking architecture focuses on improving Internet backbone connectivity, deployment, and service
rather than primarily network gateway connectivity and swapping of those embedded in the Long-Term Evolution
(LTE) network. FC architecture is a strongly visualized computing system that offers hierarchical processing facilities
utilizing the edge application nodes. There is, though, a slight contrast between these two definitions. Fog and edge
computing also entail moving the communication and perception capacities down to the vicinity where the knowledge
originates. The main difference between the two systems is merely where computation and information management
are focused. Data from both devices, such as pumps, relays, motors, and sensors, are sent from the same sources or
physical objects. These two machines carry out physical activities in this world, for example, electric communications,
power transfer, swapping, or awareness of the events affecting them.
3
In recent years, the main approach has been to build the biggest data center system, defined as a cloud hosting net-
work (e.g., Google, AWS, IBM, and Microsoft Azure), which offers consolidated storage facilities. The cloud has been
supplied with all the details which produce a kind of knowledge that may, among others, be saved in some log files or
video files. Thus, the transition of data management, database resources, control structures, and data storage to the con-
solidated cloud became a common practice before the FC architecture.
4
After users entered the modern era, which is
recognized as the future of communication, we have connected billions of IoT devices. Over time, IoT applications have
also proliferated by connecting tangible objects and functional components with exponential development.
5
Data stor-
age themselves can get overwhelmed, so there's going to be a huge problem. Although the cloud platform is efficient, it
is not feasible to enable time-consuming or nonoperable processes if Internet connectivity is weak. Integrating IoT fog
into computing offers several IoT implementations with different benefits.
6
Fog makes it possible to reduce latency
between IoT devices in real time, particularly for IoT applications that are time sensitive. Also, fossil computing tech-
nology for large-scale sensor networks will be one of the main features, as the number of IoT sensors is soon going to
hit billions.
7
For particular IoT applications, FC may provide several advantages, as seen in Figure 1.
The definition of IoT must be made transparent. IoT is regarded as a transition time created in the world by con-
nected devices.
8
IoT network comprises digital machines linked to one another and installed in industrial surveillance,
defense, and domains such as automated energy meters, utilities, smart homes, and health care.
9
FC's integration into
IoT creates a new chance for Fog as a Service (FaaS), in which a provider forms a range of sensor nodes of fog through
their geographic location. Each fog node can calculate, network, and store locally.
10
FaaS would facilitate the distribu-
tion of services to consumers in new business models. Unlike clouds, operated largely by large organizations capable of
constructing and managing large data centers, FaaS can allow large and small enterprises to deliver and run private or
public computing, storage, and control systems at different levels serving the customer need of a range of people.
11
The
analysis discussed in this article presents the incorporation of FC with the IoT; this requires an analysis of the state-of-
the-art, functions, and advantages of the fog.
12
For performance evaluation of Routing Protocol for Low-Power and Lossy Network (RPL) border routing protocol
for FC-based IoT system by using some fixed number of nodes and transmission range in terms of end-to-end delay,
throughput, and hop count (HC) against size, RPL has been evaluated and analyzed for IoT-based systems. RPL routing
protocol has also been tested by moving the fog router to the center of a town, which is an important criterion for
achieving maximum network efficiency with efficient power analysis and distribution for varying transmission ranges.
The entire test was carried out on the Contiki OS using the Cooja Network Simulator. The destination-oriented directed
acyclic graph (DODAG) is built by RPL using OF. OF is also used to specify a node's rank, which is its distance from
the DODAG root node. RPL determines the overall topology by constructing DODAGs within instances, each of which
is linked to a specific objective function (OF). Our AOF0 was compared to MRHOF and OF0. Our AOF0 improves net-
work lifetime and energy balancing significantly, according to experimental results.
2of21 KUMAR ET AL.
1.1 |Problem statement
The OF is used by RPL to optimize or select the best route for nodes to reach their destination. The OF is used by the
user's or application's requirements. The standardized OFs (OF0 and MRHOF) choose the path in different ways, but
they both employ the same metric: HC and ETX, respectively. Although using these OFs has several benefits, such as
the optimum parent choice and routing table organization, it also has certain drawbacks. Indeed, a single metric
approach can improve some performance while deteriorating others, implying that the single measure method does not
meet all application needs. Here, a new RPL OF of AOF0 that is congestion aware and based on the Quality of Service
(QoS) is proposed. Extensive simulations show that AOF0 outperforms existing OFs in terms of satisfying real-time
applications under QoS and network congestion. RPL can avoid congestion using the proposed AOF0 while
maintaining network feasibility in terms of time and energy. This QCOF is implemented using linear programming to
maximize packet transmission rate based on priority while using the fewest DODAG roots possible.
1.2 |Motivation and contribution
Congestion avoidance to improve the QoS in RPL-based network such as IoT is an essential requirement. The OF in
RPL is in charge of finding routing paths, allowing you to choose the best route based on predefined criteria to improve
QoS and avoiding congestion in IoT networks. This route was chosen after meeting the recommended link metrics for
use in Low-Power and Lossy Networks (LLNs).
13,14
The OF OF0 in RPL, on the other hand, is based on a single metric,
which is rank (node positions). Despite the existing RPL extensions, RPL remains open in research and requires further
improvement, motivating us to create a new RPL extension known as AOF0 to overcome the limitations of the existing
RPL extensions. This paper's contribution can be summarized as follows:
•OFs OF0, AOF0, and MRHOF for RPL in the static environment for different node numbers have been verified.
AOF0 performs better in terms of reduced overhead, successful delay reduction, and less energy consumption.
•Investigating convergence of FC with numerous IoT applications and suggestions to overcome these issues.
•During the link estimation, RPL implementation issues in the Contiki system are tested and discovered ways to
reduce these unnecessary implementation issues.
FIGURE 1 FC supports many IoT applications
KUMAR ET AL.3of21
•Datagram Transport Layer Security (DTLS) is also proposed in the FC-based IoT scenario after verifying the improve-
ment in performance through test simulation.
1.3 |Scope of the study
RPL is the first mainstream LLN routing protocol in the IoT field with FC. However, there was little interest in deter-
mining its efficiency. Thus, the evaluation and understanding of RPL conduct are necessary to differentiate between
requirements and requirements, allowing it to be enhanced, in various situations and environments for future IoT sys-
tems with enhanced fog systems. Thus, the scope of this study is to show the performance of new routing algorithms
RPL in the proposed scenario for a successful power and delay reduction as well as providing less packet loss in the
IoT-based FC model.
The rest of the article is organized as follows: In Section 2, the literature survey is addressed based on CC and
FC. Section 3 highlights the system architecture based on the CC layer, fog layer, and physical layer. Section 4 talks
about the proposed methodology that presents the materials and methods employed. Further, the result and discussion
are discussed in Section 5. The article is eventually summarized in Section 6. Moreover, the outline of the paper has
been illustrated in Figure 2.
2|LITERATURE SURVEY
This section discusses papers that deal with the FC convergence of IoT. Provided that fog computation is only a new
area of study, this computational model does not have any clear solutions. In this section, review over the work
addressing FC integration with IoT in different applications is conducted.
Oueis et al.
15
have incorporated FC into the load-balancing process to increase network performance for users. It
does, however, lengthen the network convergence time. Alharbi et al.
16
have discussed the determinants of technology
use in Saudi Healthcare Organizations, as well as the synthesis of IoT and heterogeneous frameworks, which prompt a
match or blending issue for the utility in the healthcare sector, but the framework does not address security issues.
Mongiello et al.
17
discussed run-time architectural simulation for future Internet implementations to maintain the IoT
security protection parameter. However, the network overhead has not been reflected in this work. Sanjeevi et al.
18
discussed ontology that allowed the Internet to deter post-harvest losses from smart agriculture, and they defined a
numerical cloud arrange model and the significantly related boundaries for clarifying the deferral in haze engineering
figuring and correspondence. But security issues are not resolved by the framework.
FIGURE 2 The outline of the paper
4of21 KUMAR ET AL.
Gomathi et al.
19
discussed open access to services and maintenance of smart city stuff for user-centered future Inter-
net and concentrated anxious center (fog) and core (cloud) collaboration and participation. Their involvement increases
energy consumption and an end-to-end delay. It does, however, lengthen the network convergence time. Sarkar and
Misra
20
performed numerical simulations of the FC method and investigated the latency and energy consumption of
the program within the IoT. The proposed technique improves the life of the network, while it increases the overhead.
Thus, Ningning et al.
21
suggested an FC approach that would turn physical nodes into virtual machine nodes at differ-
ent speeds. Both the minimal approach and the max–min strategy are found in multiple automated process scheduling
systems as criteria for calculating the success of other scheduling techniques discussed. He et al.
22
discussed the role of
the principle of entity virtualization in developing personalized IoT-based educational apps for end-users, but security
issues are not resolved by the framework.
Mishra et al.
23
discussed research centered on Contiki OS using the Cooja simulator, which measures wireless sen-
sor node temperature and light with the help of IoT. In the global cloud-centric environment, Gubbi et al.
24
have intro-
duced IoT. They discussed different technologies in the future that drive IoT research. Anneka has been used to
interacting between private clouds and public clouds. Premsankar et al.
25
funded ambitious IoT edge computing tech-
nologies to improve immersive apps. The findings demonstrate the significance of edge computing in raising the latency
of applications in their experimental assessment. Ning and Hu
26
also made use of increasingly evolving technology,
such as fog, cloud, and IoT, to implement instruction in universities. Two frameworks were suggested for the future of
IoT, that is, creation, classification, and further design of IoT devices for a modern education program. However, the
network overhead is not reflected in this work.
Yang et al.
27
have established an overall energy output model involving utilizing loops, calculation, and
offloading in homogenous fog grids. The research focused only on overall energy and did not recognize energy
transfers through both the fog and the terminal layers. In multiuser fog radio access networks (F-RANs), Pang
et al.
28
proposed a latency-driven cooperative task computing that characterizes the communication–computing rela-
tionship through multiple F-RAN nodes. However, the security issues are not resolved by the framework. Ogawa
et al.
29
presented a use case regarding RPL and Collection Tree Protocol (CTP) energy usage indicators and
proposed measurements for a variety of cases. Utilizing both RPL and CTP, the developers did not find the robust
routing protocol to be successful.
Felici-Castell et al.
30
concentrated on examining the different methods for collecting knowledge from multiple data
sources. Experimental assessment of trade-offs between “submit all”and “local buffer”approaches took account of
power consumption, lifespan, performance, and reliability. No attention was given to the efficiency of the sink
node(s). Elleuchi et al.
31
suggested a routing scheme for the security protocol that expands the RPL to the Internet.
The symmetric key distribution between the AES128 (Advanced Encryption Standard) and SHA256 (Secure Hash
Algorithm) communications nodes relies on identification-based cryptography to calculate message authentication
codes. Boualam and Ezzouhairi
32
discussed the proposed work that was tested by implementing the Cooja simulator
with Contiki OS. The results of the simulation show that, relative to the default RPL, the proposed work offers a
higher packet delivery ratio (PDR), lower latency, and greater throughput but security issues are not resolved by the
framework.
Bhandari and Cho
33
suggested a new FO aimed at enhancing the consistency of chosen routes by utilizing both
composite measures and fuzzy logic. The findings obtained indicate that, in terms of reliability, energy usage, and
packet distribution, our solution performs better than the typical RPL. Mahmud et al.
34
presented a taxonomy of
FC. They researched the configuration of the fog server, network modules, and different fog processing metrics. They
have been studying the virtualization of network services to gain the flexibility of network service distribution. Li
et al.
35
developed a virtual fog device that could be used to test and execute objective investigations. To reduce the
wired-free front carriage costs of the cloud system, Peng et al.
36
suggested an economic spectral efficiency (ESE) frame-
work, and they articulate the power consumption while transmitting data. Also, they introduced a simulated fog system
that could be used to validate and perform quantitative research in the future. Raj and Kumar
37
concentrated in this
paper on the data protection problems found in CC. They discovered a viable solution and a proprietary cloud realm in
addition to this, but the security issue was not resolved by the framework. Garg et al.
38
suggested two metrics of study
assessment that are effective citation and better understanding of influence factor.
Zaminkar et al.
39
proposed an effective hybrid method, which includes encryption, as an efficient approach for
resolving the RPL protocol concerns, ensuring that the devices are securely connected. Giannakoulias
40
addressed regu-
latory ramifications, compliance with laws and requirements, new attack vectors arising from virtualization technology
flaws, problems of data privacy such as encryption and access permissions, and security tests to be carried out on
KUMAR ET AL.5of21
TABLE 1 A description of the research works related to FC convergence with the IoT
Reference Year Focus Privacy Security Reliability Performance Energy
Oueis et al.
15
2015 Discussed incorporated FC into the load-
balancing process to increase network
performance for users.
✓✓✓
Alharbi et al.
16
2016 Proposed technology use in Saudi
Healthcare Organizations
determinants.
✓✓
Sanjeevi et al.
18
2020 Discussed ontology allowed the Internet
to deter post-harvest losses from smart
agriculture.
✓✓
Ningning
et al.
21
2016 Suggested an FC approach that would
turn physical nodes into virtual
machine nodes at different speeds.
✓✓
He et al.
22
2003 Discussed the role of the principle of
entity virtualization in developing
personalized IoT-based educational
apps for end-users.
✓✓
Mishra et al.
23
2017 Discussed research cantered on and
Contiki OS using the Cooja simulator,
which measures wireless sensor node
temperature and light with the help of
IoT.
✓
Gubbi et al.
24
2013 Discussed different technologies in the
future that drive IoT research and
Anneka has been used to interact
between private clouds and public
clouds.
✓✓ ✓
Premsankar
et al.
25
2018 Discussed ambitious IoT technologies
edge computing to improve immersive
apps.
✓✓
Yang et al.
27
2018 Analyzed overall energy output model
involving utilizing loops, calculation,
and offloading in homogenous fog
grids.
✓
Pang et al.
28
2017 Proposed a latency-driven cooperative
task computing that characterizes the
communication–computing
relationship through multiple F-RAN
nodes.
✓✓ ✓
Mahmud
et al.
34
2018 Presented taxonomy of FC. ✓✓
Peng et al.
36
2017 Provided an ESE framework and they
articulate the power consumption
while transmitting data.
✓✓
Raj and
Kumar
37
2020 Discussed CC security strategies and
issues.
✓
Garg et al.
38
2020 Suggested two metrics of study
assessment that are effective citation
and effective better understanding
influence factor.
✓
Zaminkar
et al.
39
2021 Proposed an effective hybrid method,
which includes encryption, as an
efficient approach for resolving the
✓✓
6of21 KUMAR ET AL.
networks before their move to the cloud. Lamaazi and Benamar
41
proposed a new OF that was based on a combination
of metrics and fuzzy logic (OF-EC). The proposed OF-EC considers both the link and node metrics, namely, the
expected transmission count (ETX), HC, and energy consumption, to overcome the limitations of using a single metric
(EC). But still, the security of IoT nodes is not covered in this paper by the author. Yu et al.
42
presented the logic analy-
sis scheme for Burrows–Abadi–Needham. The suggested scheme can withstand multiple attacks and provide safe
shared authentication and anonymity by using secret criteria and biometrics.
Lamaazi and Benamar
43
proposed a new method for evaluating RPL effectiveness based on the OF and trickle
algorithm. They compared the RPL-EC (RPL-based combined ETX and energy consumption) to the RPL-FL (RPL
based on the flexible trickle algorithm). But that work was only limited to the energy consumption approach, not pro-
vide high security. Chen et al.
44
addressed the lower energy offloading scheme. The goal is to reduce energy consump-
tion when computational activities are performed within the optimal overhead and delay of energy. It takes a
thorough look at the components of the fog node's energy usage, including the local computing, distributing, and
waiting for state energy consumption. Computational results show that the planned form of offloading is superior to
local computer systems and total energy consumption and completion time offload schemes. Onwuegbuzie et al.
45
investigated the performance of RPL concerning its two OFs: OF0 and MRHOF. MRHOF function performs well;
however, the network overhead has not been reflected in this work. Table 1 summarizes the difference between the
proposed method and related works to FC convergence with the RPL, underlining the contribution of each
manuscript.
Table 1 summarizes the related work with the following parameters like privacy, security, energy, performance, and
reliability. However, the related research work comparison shows that limited research work has security and privacy
features, and some has features related to energy, performance, and reliability. Therefore, our goal in this paper is to
introduce the DTLS protocol in a fog network with higher security and an OF that not only extends the life of the net-
work but also includes a simple method for lowering network overhead.
TABLE 1 (Continued)
Reference Year Focus Privacy Security Reliability Performance Energy
RPL protocol concerns, ensuring that
the devices are securely connected.
Giannakoulias
40
2019 Addressed regulatory ramifications,
compliance with laws, and
requirements.
✓
Lamaazi and
Benamar
41
2018 Proposed a new OF that was based on a
combination of metrics and fuzzy logic
(OF-EC).
✓✓ ✓
Yu et al.
42
2019 Presented the logic analysis scheme for
Burrows–Abadi–Needham.
✓
Lamaazi and
Benamar
43
2019 Proposed a new method for evaluating
RPL effectiveness based on the
objective function (OF) and trickle
algorithm.
✓✓ ✓
Chen et al.
44
2019 Addressed the lower energy offloading
scheme.
✓✓
Onwuegbuzie
et al.
45
2019 Investigated the performance of RPL
concerning its two OFs; OF0 and
MRHOF.
✓✓
Our proposed
work
Energy-efficient fog computing in
Internet of Things based on Routing
Protocol for Low-Power and Lossy
Network with Contiki
✓✓ ✓ ✓ ✓
KUMAR ET AL.7of21
3|SYSTEM ARCHITECTURE
FC architecture enables fog nodes, cloud, and IoT to be moved dynamically through processing, networking, and stor-
age services. Fog interfaces, however, may allow the versatility and complex movement of devices, storage, and control
roles between these various organizations, to communicate with the server, other fogs, and staff or users. This has facili-
tated well-located FC consumer evaluation and has also allowed accurate and successful control of QoS. FC acts as a
cloud-to-end gateway that enables end-users to access data, storage, and network resources. The nodes of fog are
referred to as such units. It can be mounted anywhere with a network connection. Figure 3, based upon the optimiza-
tion of QoS parameters and the proposed model, illustrates the smart city technology architecture of the new plat-
form.
46
The function of the different layers is explained in this figure.
3.1 |Cloud layer
At the cloud layer in heterogeneous IoT, the compilation and execution of information obtained from other layers
depends on the CC layer. Cloud storage with a wide variety of heterogeneous IoT will accommodate the enormous
amount of knowledge immediately in a specific way. This is feasible since cloud systems have the power to monitor
hierarchical computation. Cloud services often have the potential to make choices depending on the knowledge
received, in addition to computing space. Also, cloud providers will take swift action on some essential heterogeneous
IoT systems based on emergency event-aware frameworks.
3.2 |Fog layer
This layer is managed by the logical fog manager who analyzes and classifies the request according to the specificity of
the moment. The fog handler is the key component for optimizing the QoS and usage of the tools on fog layers.
FIGURE 3 The system architecture of IoT with FC
8of21 KUMAR ET AL.
3.3 |Sensor or physical layer
Specific sensors in IoT's physical layer gather data from various locations, and the cloud resources are then supplied for
decision making. A large number of sensors are mounted in a particular region, and topology is generated for transmit-
ting these data. There are sink nodes, sensor nodes, and control nodes in a typical network. A lower node collects sen-
sor node data and transforms it into a multihop communication model.
The FC architecture facilitates a dynamic transition to the fog node, the cloud, and the IoT continuum
47
for
transmission, networking, and stocking services. However, fog interfaces to connect to the cloud, other fogs, and
information or users must make mobility simpler with this different computing, storage, or control roles and com-
plicated transfer. The FC end-user appraisal was well placed, and QoS management was professional and success-
ful. Promoting server and cloud to fog communication with back-to-back facilities from the fog to the cloud
interface should be made mandatory. The data and services that can be distributed through the fog and cloud
must be sought. The exactness and accuracy of these details and data will determine how the fog or cloud
responds. Fog nodes need to have the features of pool resources to facilitate processing with each other. For
example, all used fog nodes will share their data storage, computation, and processing resources with one or more
user apps using a priority node functionality scheme. Also, several fog nodes will work with each other's backup
resources.
48
FC offers a globally distributed IoT device structure with differential recognition in a resource-effi-
cient, user-friendly environment. Fog services simply provide IoT access via fog to IoT interface or fog to the user
interface. The building blocks of the cloud are considered to be FC, and the FC features can be defined as
follows:
•FC promotes knowledge of locations in which fog nodes can be installed in various locations. Moreover, because the
fog is closer to the end devices when processing the end device data, it provides lower latency.
•There are large-scale networks of sensors that track the world around them.
•The fog offers distributed tools of computation and storage that can work with such large-scale end devices.
•Fog components can communicate with various domains and through different service providers and work with
them.
•Fog nodes or end devices are built of various types by various manufacturers to be deployed depending on the
platforms.
4|PROPOSED METHODOLOGY
Contiki is a broadly utilized IoT-empowered working framework that is free and open source. Contiki is a low-power
IoT computer operating system. In Table 2, the simulation parameters are given.
To uphold and incorporate higher degrees of security, there is a need to actualize IPv6 for IoT-based FC scenarios,
with dynamic crossover cryptography in key age and verification. The IPv6based methodology can be empowered by
being completely sure about calculations that are not powerless against capture. Border Router 1, working as an FC
node (highlighted in green color), helps in connecting one network to another (see Figure 4). FC node is used in this
case to route data between an RPL network and an external network. So far, only the RPL network has been developed.
The situation in which the RPL network (having 15 sensor motes highlighted in yellow color) is attached to an external
network now needs to be simulated. The IoT convention based on IPv6 is RPL. For 6LoWPAN, it is used. So, for effi-
cient power distribution using IoT technologies and a 6LoWPAN network, we evaluated the RPL routing protocol in
real time for a small area using an FC node as the 6LoWPAN gateway with the help of a powerful simulator. Contiki
Cooja.
The system was tested in a real-time power distribution network situation with varying transmission ranges and
node counts. IPsec protection is guided by the IPv6 display information, which allows IPv6 group authorization and
payload encryption through extension headers methods. However, IPsec is not updated; it must be arranged and used
with a trade key in the security. It operates with DODAG dynamic output and has unidirectional and bidirectional cor-
respondence. It has specific occurrences with higher growth, with restricted behavior.
KUMAR ET AL.9of21
4.1 |DA process
RPL empowers each node in the system to select whether to submit packages up to their root or down to their child
nodes. With the growing usage of IoT in various fields, it is necessary to figure out IoT's protection pieces by ensuring
that the assured packages are directed to avoid interruptions and ensuring that all communications can be completely
assured. In the wake of the directing convention, we will talk about how information is collected in the system with a
mathematical model (see Figure 5). The sum of data packets received at node jover time tis known as reinforcement
signal (RS).
TABLE 2 Simulation parameters
Parameter Value
Operating system Contiki 2.7
Simulator Cooja
Computer RAM 8 GB, i5 processor
Transmission range 50 m
Interference range 55 m
Initial energy 1500 mA
Simulation time 110 min
Routing protocol RPL
Number of nodes 16
Node type Sky mote
Topology Random
MAC layer 802.15.4
Network area 300 m 300 m
FIGURE 4 Cooja simulator software setup with RPL network with 6LoWPAN gateways (FC node)
10 of 21 KUMAR ET AL.
Rateipj¼PAN þPAM :ð1Þ
PAN is used to represent throughout the routing the number of data packets that the previous nodes aggregated, and
Rateipjis the rate of input packets. PAM is used to represent throughout the routing the number of packets of data that
are not aggregated from previous nodes.
RS ¼11
Rateipj
:ð2Þ
If S> Threshold δ, then node jis rewarded. Otherwise, the node is given a penalty. If a reward is received by the
node, PA
gg
(probability of aggregated by IoT nodes) varies as
PAgg ¼PAgg þα1PAgg
,ð3Þ
where αis the action of automata, and if a bonus is received by the node, PA
gg
will be as
PAgg ¼1β1RðÞðÞPAgg:ð4Þ
Here, penalty coefficient is β(input set of automata), the compensation is the penalty ratio, and the effect of the
coefficients on the total number of (plain or aggregated) packets obtained in node jover the time tis
NAPackj¼Xi¼1degree NPKi,ð5Þ
where NAPackjis the number of aggregated data packets by node jand NPK iis the number of aggregated data packets in
ifor node j, and the impact of coefficients (reward/penalty T)isas
T¼11
NAPj
:ð6Þ
The aggregation of data is an integral wireless routing method for the processing of data from different network
sources. It aggregates data from the sensor, which eliminates the redundancy of the data and decreases the transmitted
data. Thus, it saves the strength of the IoT sensor nodes. Data aggregation (DA) clubs the data of the sensor and
FIGURE 5 Data aggregation flow with a mathematical model
KUMAR ET AL.11 of 21
removes the noise from different sources. Finally, for data transmission, it produces precise data. Basic DA work (MAX,
MIN, AVE, and so forth.) can be utilized to join information things, identifying with a similar occasion
(e.g., temperature readings of some sensor nodes). Thus, DA procedure is very useful for our proposed scenario to
improve results during the simulation (see Figure 6).
4.2 |AOF0 implementation
To choose the most feasible path, AOF0 uses a combination of routing metrics and constraints, including real-time
constraints, energy constraints, packet priority, and congestion metrics. In RPL, each node chooses its preferred
parent using the OF, which is then used to transfer data to the DODAG root. As a result, each node uses AOF0
to find its way to the DODAG root. For constructing DODAG, RPL has control message types: (1) DODAG
Information Object (DIO) used to create a path from upward routing; (2) Destination Advertisement Object (DAO)
used to create a path from downward routing, also to propagate destination information to the upward nodes;
(3) DODAG Information Solicitation (DIS) used to solicit or request a DIO from the RPL node, also to search
neighborhood; and (4) Destination Advertisement Object Acknowledgment (DAO-ACK) in response to unicast
DAO message.
Let Nbe the set of sensor nodes and nibe a sensor node from N, which has Mj
s, a set of new periodic messages to
send through channel jover a link, and MOj
sj(resp. Mj
r), a set of old periodic messages that are transmitted (resp.
received) through channel j; node nihas higher priority (Pi) than node nk; and a sensor node has a set of channels C
over one link. The OF of AOF0 is to maximize packet transmission rate according to their priority, which is subjected
to five constraints:
Maximize XjCj
j¼1XMj
s
i¼1αi,jPi:ð7Þ
For data, the sum αi,jits coefficients must equal 1 to ensure that the transmission is not repeated (Equation 12).
1. For each channel between node niand its parent nj, the channel's utilization over their link F
ni
,jmust not exceed
the predefined threshold.
Subject to njFni,j,¥cjC:
XjMj
sj
i¼1
αi,jWCTT i,j
Ti
þXjMOj
sj
i¼1
WCTT i,j
Ti
þXjMj
sj
k¼1
WCRTk,j
Tk
þ≤Threshold, ð8Þ
FIGURE 6 Max function data aggregation process
12 of 21 KUMAR ET AL.
where WCRTk,jis the worst-case reception time of message kfrom node nk (response transmission time of message
ifrom node ni) over channel j,Ti(response Ti) refers to the period of message k(response message i), and WCTTi,jis
the worst-case transmission time of message iover channel j.
2. For each node, the output data (transmitting data) must be greater than the input data (receiving data).
XjMj
rj
k¼1
WCRTk,j
Tk
≤XMj
s
jj
i¼1
αi,jWCTT i,j
Ti
þXMOj
s
jj
i¼1
WCTT i,j
Ti
:ð9Þ
3. For each channel, the consumed energy must not exceed the available energy for a node.
XjCj
j¼1Ejt1,t2
½ðÞ<CBt1
ðÞþEHt1,t2
½ðÞ,ð10Þ
where Ejt1,t2
½ðÞis the consumed energy by channel jin the time interval EHt1,t2
½ðÞ.CBt1
ðÞis the remaining energy in
the battery at t1, and EHt1,t2
½ðÞis the harvested energy.
4. For data, the coefficient value must be between one and zero to determine whether or not the data will be sent.
¥miMs,0≤αi,j≤1:ð11Þ
5. For data, the sum αi,jits coefficients must equal 1.
¥miMs,XC
jj
j¼1αi,j¼1:ð12Þ
Thus, in the proposed AOF0, the optimal feasible path is chosen after verifying the following constraints:
•meets real-time constraints (time feasibility),
•meets energy constraints (energy feasibility),
•respects link capacity and total input/output data (congestion aware), and
•sends data according to their priority.
5|RESULT AND DISCUSSION
We consider a directed acyclic graph (DAG) with up to 100 nodes that are randomly distributed to form a connected
network to evaluate the impact of the proposed RPL's OF AOF0 on QoS and congestion. Five hundred new random
packets must be sent to the DODAG root by these nodes. We compared AOF0 to the OFs OF0 and MRHOF, which are
simulated in Contiki, an open-source IoT operating system, to show how AOF0 can provide good performance
concerning real-time application requirements. Then, we assess five performance indicators while ensuring that the
network is both time and energy efficient. To start with, we will build a stand-alone network with no connectivity to
outside networks. The next tutorial will show how to add Internet connectivity with a border router. RPL
fundamentally perceives our mesh network as a tree topology, called a DAG or DODAG. The network is instantiated
(constructed) by the tree's root (DAG or DODAG root). RPL supports different directions of traffic; first, upward
routing: from any node to a root; second, downward routing: from the root to any node; and third, any-to-any routing:
where traffic flows between arbitrary pairs of nodes in the DODAG by routing upwards to their closest common
ancestor (or the root in nonstoring mode) in the DODAG and then down to the destination node. An event can take
place everywhere in the field of the sensor.
49
KUMAR ET AL.13 of 21
The sink node with the lowest PDR is then reduced in the proposed optimization process in its sub-DODAG
measurements when the mean span of the event zone nodes approaches one half of the full sub-DODAG duration.
In this case, the sink nodes reduced their sub-DODAG size at a large distance from the current sink node; thus,
raising the hop distance from this new sink would usually result in improved network efficiency. PDR is known
only when there are no mobility disks inside the network, as described in the network optimization algorithm and
seen in the network Algorithm 1. Sink nodes are preparing to reduce their network resources dependent on PDR.
Because the PDR of close sub-DODAG sink nodes is given for each sink, the lower PDR value lowers its network
size below the application threshold defined. This decision is expected to decrease PDR in general if the nodes can-
not successfully send their data back to the destination. The network topology in an FC network is complex. There
will be new nodes joining and leaving the network. The problem of confidence is one of the most urgent issues of
FC. The level of confidence that an entity can behave satisfactorily is referred to as trust. RPL gives only rudimen-
tary protection against routing and other kinds of threats. Due to the resource limitations of fog nodes, heavyweight
cryptographic algorithms cannot be used to achieve safe communication. To protect fog-IoT networks, a lightweight
mechanism is required.
Further, RPL is connected to UDP and optionally to DTLS, offering a high degree of security in communications.
Based on established Internet standards, particularly the DTLS protocol, we are implementing the first fully
implemented two-way authentication protection framework for IoT security and privacy. The proposed protection
framework is based on the most commonly used cryptography of the public key Rivest–Shamir–Adleman (RSA) and
operates on top of regular stacks of low-power communication. We assume that current implementations, engineering
techniques, and security infrastructure can be reused by relying on a proven standard, allowing for easy security
uptake. RPL sets out four protection modes: (1) NoSec, with DTLS, deactivated; (2) PreShared Key, with activated
DTLS; (3) Raw Public Key, with activated DTLS; and (4) Certificate, with compatible DTLS. Confirmable (CON) is the
durability of signing a letter. A confirmable message is retransmitted by law, and exponential back-off between broad-
casts, to the receiver, sends the message acknowledgment (ACK) with the same message ID (in that example is
0x7d34) from the appropriate endpoint (see Figure 7). If a receiver cannot process a confirmable signal at all (i.e., not
even able to provide an acceptable answer to an error), it replies with a reset message (RST) instead of a greeting.
Figure 7 shows the authentication feature between the client and server to protect the server against Denial-of-Service
(DoS) attacks.
FIGURE 7 Reliable message transmission
14 of 21 KUMAR ET AL.
FIGURE 8 Comparison of energy consumption with the proposed method
In Algorithm 1, C
pkt
counter is the cumulative amount of packets the sink node receives. The cumulative number of
packets received by source nodes was C
pkt
received. Min PDR N is the lowest PDR between the two sinks. Max Rank
Rest is the Total Level Rest. NCI denotes network counter indicator.
LLN is a network that restricts all routers and their interconnections. LLN routers operate with restrictions such as
power, resources, memory, and their interconnections. LLN operates from a few hundred to 1000 routers with a high
error rate, low data rate, and volatility. LLN also has traffic flows such as P2P, P2M, and M2P.
50
To function indepen-
dently from the routing goal across a wide variety of LLN programs, RPL packet processing and forwarding. This goal
minimizes energy and latency. RPL is based on a distance vector, a constructive protocol that produces a DAG that is
used to exchange data between nodes. To measure the node rank, the AOF0 is used to compute the right path; it works
on a combination of metrics and constraints. In RPL, OF is used to pick the optimal path in a network towards the
DODAG root.
The optimization seen in this work is to increase the overall network performance and extend the network's life-
time. IoT nodes typically use batteries; the energy supply of these nodes is thus an important element in their continu-
ous life and operation in the network environment. In this regard, the energy consumption of the network is measured
in a way, namely, real energy consumption, and the estimation method for the energy consumption rate is modeled on
a millijoule scale is
Energy mJðÞ¼
Tx19:5mAþRx21:8mAþCPU 1:8mAþLMP 0:0545Þ3V
4096 8:ð13Þ
The energy consumption (Equation 13) is determined using the AOF0 on the fog node to boost network efficiency
and is also used in the proposed scenario. Figure 8 shows the new system with various cycles for data generation versus
the per-bit energy usage of the fogging process. The proposed method's AOF0 energy consumption is slightly better
than the OF0 and MRHOF functions. When the packet frequency is high, the data rate is strong, and the AOF0 fog
node does not drop several packets, resulting in low energy usage per bit as opposed to lower packet generation periods.
Consequently, the energy usage is smaller than the conventional system.
It can be seen that conventional device schemes' packet drop ratio is greater than the current solution since latency
becomes more severe and the amount of packets falling becomes higher. Figure 9 shows the delay from end to end for
both methods. This latency is high as sky motes or nodes use very low service cycles in the simulation. Therefore, nodes
use their sleep mostly compared with the active node. Both methods are very late. The delay of conventional methods
is noticeable in the first place, similar to the propagated method. The data are constantly sent to a sink node; thus,
KUMAR ET AL.15 of 21
congestion grows steadily and continues. This raises the latency in traditional techniques. After low PDRs have been
observed in every sink node, network synchronization begins to optimize the network. The speed of the transmission
phase decreases with network setup in the network. Although the end-to-end latency of the AOF0 stabilizes and
becomes significantly better than the OF0 and MRHOF with continuous data collection.
FIGURE 9 Delay count comparison with the proposed method
FIGURE 10 Average power consumption versus number of hop
16 of 21 KUMAR ET AL.
Hop is an IoT networking term that refers to the number of nodes a packet (a portion of data) passes through from
its source to its destination, as shown in Figure 10. Sometimes, a hop is counted when a packet passes through other
nodes on a network. This is not always the case, and it depends on what role those devices play on the network and
how they are configured. The traditional method does not depend on any routing metrics for data-path selection,
FIGURE 11 Average transmit duty cycle versus number of hop
FIGURE 12 Average power consumption in the motes
KUMAR ET AL.17 of 21
whereas the proposed method selects the path with RPL routing based on minimum node rank value. So the power
consumption for the OF0 and MRHOF is higher than the AOF0.
Task period implies a percentage of the time that a node is filled. With less overhead routing and retransmission of
connection layers in our proposed function AOF0, RPL decreases the duty cycle relative to the conventional approach
as seen in Figure 11. If a single node transmits 2-time units per 10-time units on a line, this node has a 20% duty cycle.
But if we regard channels as well, things become a bit more complicated. If we have a system that transmits instead of
one on three networks, each specific channel is always occupied per 10-time units for 2-time units (so 20%). However,
the system now transmits every 10-time units to 6-time units, granting it a 60% duty cycle. In the proposed work,
16 nodes are used to track the amount of energy consumed, with the network becoming denser as more energy is con-
sumed. This increase in power consumption is due to the node sending more transmissions. Figure 12 shows the total
absolute power consumption over time, with the transceiver power accounting for the majority of the total power con-
sumption and the other power sources being minor. As a result, we ignored the low-power mode and CPU consumption
in our research. Then, we concentrate on calculating the percent radio on time, which is a measure of energy consump-
tion. The higher the network traffic, the higher the energy consumption, and vice versa. There are a vast number of
parameters that need to be looked at during the IoT simulation, including low-power management (LPM), central
processing unit (CPU), radio listens, and radio transmit. The findings are compatible with the above-described graph,
and the low-power mode is in honesty mode. Furthermore, the parameter for radio listening is also consistent. For the
FIGURE 13 Temperature evaluation in Celsius at the motes
18 of 21 KUMAR ET AL.
processing computation, the LPM, CPU, radio listens and the radio transmits are separate parameters, and these param-
eters are utilised to calculate the average power of our network motes.
Power mWðÞ¼
Energy mJ
ðÞ
Time sðÞ :ð14Þ
To contribute, the level of power consumption (in mW/h units) in the proposed network motes shall be determined
by Equation 14. With the network graph tab in the Cooja collect view, the researcher can view the dynamic topology
and sensor node connections. The calculation of the temperature in Celsius of all motes is shown in Figure 13.
To provide a clear understanding, a comparative research description is specified in the form of Table 3 between
conventional approaches and the proposed approach.
6|CONCLUSION
This paper integrates IoT with FC and suggests an IoT-based FC model. The quality tests have been carried out using
Cooja on Contiki. The paper concludes that the proposed scenario with FC is highly beneficial because packets from
similar directions are added to the network root using learning automats, and data exchange rates are used to manage
time consumption. As a result, this strategy culminated in better network graph creation and more efficient load
balancing inside the network in the proposed simulation. In this paper, we propose a new AOF0 for selecting the nodes'
preferred parent. Energy consumption, ETX, and NOCS metrics were used in our OF. Furthermore, the weights of met-
rics in our AOF0 are adaptive and can change over the network's lifespan. To demonstrate the effectiveness of the pro-
posed process, we compared it to OF0 and MRHOF. After comparing AOF0 to existing OFs that use only one, two, or
three metrics that are insufficient to meet application requirements, we discovered that AOF0 is more appropriate for
respecting real-time application requirements, as it combines five metrics, including node rank, real-time constraints,
energy constraints, link capacity (threshold), and input/output. Extensive simulation experiments show that the pro-
posed OF outperforms previous work by achieving the highest PDR, in particular, PDR according to packet priorities,
and the best throughput when compared to a low average in consumed energy. In the future, rather than moving the
fog router position with an increasing number of nodes each time covering a larger area, an improvement of the exis-
ting RPL routing protocol should be developed to provide better network efficiency.
TABLE 3 Comparative study between the conventional approaches and the proposed scheme
Reference Conventional techniques Scope of improvement Proposed scheme merits
Yu et al.
42
Secure reciprocal authentication using logic
analysis from Burrows–Abadi–Needham.
Not provide two-way
security and limited to
cloud-based IoT
applications.
Two-way authentication security scheme
and best for IoT with FC.
Lamaazi and
Benamar
43
Proposed and compared the RPL-EC (RPL-
based combined ETX and energy
consumption) to the RPL-FL (RPL based
on the flexible trickle algorithm).
Only limited to the
energy consumption
applications, not
provide high security.
Our test simulation results show that the
RPL-AOF0 provides the best values in
terms of overhead, successful delay
reduction, and less energy consumption
and provides high DTLS security for
network life.
Chen et al.
44
The proposed offloading scheme is superior
in reliability and energy consumption.
Due to the lack of
security, the
authentication process
is not done.
The proposed protection framework is
based on the most commonly used
cryptography of the public key (RSA)
and operates on top of regular stacks of
low-power communication.
Onwuegbuzie
et al.
45
Proposed and investigated the performance
of RPL concerning its two OFs: OF0 and
MRHOF.
Network overhead has
not been reflected in
this work.
Network overhead problem resolve by
AOF0 in proposed work
KUMAR ET AL.19 of 21
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
ORCID
Sachin Kumar Gupta https://orcid.org/0000-0001-8270-5853
Saru Kumari https://orcid.org/0000-0003-4929-5383
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How to cite this article: Kumar A, Sharma S, Goyal N, Gupta SK, Kumari S, Kumar S. Energy-efficient fog
computing in Internet of Things based on Routing Protocol for Low-Power and Lossy Network with Contiki. Int
J Commun Syst. 2021;e5049. doi:10.1002/dac.5049
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