Internet of Things: An Overview

Abstract

As technology proceeds and the number of smart devices continues to grow substantially, need for ubiquitous context-aware platforms that support interconnected, heterogeneous, and distributed network of devices has given rise to what is referred today as Internet-of-Things. However, paving the path for achieving aforementioned objectives and making the IoT paradigm more tangible requires integration and convergence of different knowledge and research domains, covering aspects from identification and communication to resource discovery and service integration. Through this chapter, we aim to highlight researches in topics including proposed architectures, security and privacy, network communication means and protocols, and eventually conclude by providing future directions and open challenges facing the IoT development.

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Chapter 1
Internet of Things: An Overview
Farzad Khodadadi, Amir Vahid Dastjerdi, and Rajkumar
Buyya
Abstract
As technology proceeds and the number of smart devices continues to grow substantially,
need for ubiquitous context-aware platforms that support interconnected, heterogeneous,
and distributed network of devices has given rise to what is referred today as Internet-of-
Things. However, paving the path for achieving aforementioned objectives and making
the IoT paradigm more tangible requires integration and convergence of different
knowledge and research domains, covering aspects from identification and
communication to resource discovery and service integration. Through this chapter, we
aim to highlight researches in topics including proposed architectures, security and
privacy, network communication means and protocols, and eventually conclude by
providing future directions and open challenges facing the IoT development.
Keywords: Internet of Things; IoT; Web of Things; Cloud of Things.
1.1 Introduction
After four decades from the advent of Internet by ARPANET[1], the term “Internet”
refers to vast category of applications and protocols built on top of sophisticated and
interconnected computer networks, serving billions of users around the world in 24/7
fashion. Yet, we are at the beginning of an emerging era where ubiquitous

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communication and connectivity is not a dream or challenge any more. Subsequently, the
focus has shifted towards seamless integration of people and devices to converge physical
realm with human-made virtual environments, creating the so called Internet-of-Things
(IoT) utopia.
A closer look at this phenomenon reveals two important pillars of IoT; “Internet” and
“Things” that require more clarification. While it seems that every object capable of
connecting to Internet will fall into the “Things” category, this notation is used to
encompass more generic set of entities, including smart devices, sensors, human beings,
and any other object that is aware of its context and is able to communicate with other
entities, making it accessible at any time anywhere. This implies that objects are required
to be accessible without any time or place restrictions.
Ubiquitous connectivity is a crucial requirement of IoT and to fulfil it, applications
need to support diverse set of devices and communication protocols, from tiny sensors
capable of sensing and reporting a desired factor to powerful back-end servers utilized for
data analysis and knowledge extraction. This also requires integration of mobile devices,
edge devices like routers and smart hubs, and humans in the loop as controllers.
Initially, Radio-Frequency Identification (RFID) used to be the dominant technology
behind IoT development, but with further technological achievements, wireless sensor
networks (WSN) and Bluetooth-enabled devices augmented the mainstream adoption of
IoT trend. These technologies and IoT applications have been extensively surveyed
before[2],[3],[4],[5], however less attention has been given to unique characteristics and
requirements of IoT such as scalability, heterogeneity support, total integration, and real-
time query processing. To make these required advances bold, this chapter lists IoT

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challenges and promising approaches by considering recent researches and advances that
made in the IoT ecosystem as shown in Figure 1.1. In addition, it discusses emerging
solutions based on cloud, fog, and mobile computing facilities. Furthermore, the
applicability and integration of cutting-edge approaches like Software Defined
Networking (SDN) and containers for embedded and constrained devices with IoT is
investigated.
Figure 1.1 : IoT Ecosystem.
Connecting Objects and Humans
Data Analytics and Decision Making
Extract and Filter
Visualise
and Model
Predic t and
Optimise
the Deci sion
Sensing and Collecting Data Keeping Humans in the Loop
Cloud Services
Security and Privacy

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1.2 Internet-of-Things Definition Evolution
IoT emergence: Kevin Ashton is accredited for using the term “Internet-of-Things” for
the first time during a presentation in 1999 regarding supply chain management[6]. He
believes the “things” aspect of the way we interact and live within the physical world that
surround us needs serious reconsideration, due to advances in computing, Internet, and
data generation rate by smart devices. At the time, he was an executive director at the
MIT's Auto-ID center where contributed to extension of RFID applications into broader
domains, which built the foundation for current IoT vision.
Internet of Everything (IoE): Since then, many definitions for IoT have been
presented, including the definition provided by Gubbi et al.[7] that focuses mostly on
connectivity and sensory requirements for entities involved in typical IoT environments.
While those definitions reflect IoT's basic requirements, new IoT definitions give more
value to the need for ubiquitous and autonomous networks of objects where identification
and service integration have an important and inevitable role. For example, Internet of
Everything (IoE) is used by Cisco to refer to people, things, and places that can expose
their services to other entities[8].
Industrial IoT (IIoT): Also referred to as Industrial Internet [87], is another form of
IoT applications favoured by big high-tech companies. The fact that machines can
perform specific tasks such as data acquisition and communication more accurately than
humans has boosted IIoT's adoption. Machine-to-machine (M2M) communication, Big
Data analysis, and machine learning techniques are major building blocks when comes to
definition of IIoT. This data enables companies to detect and resolve problems faster,
thus resulting in overall money and time savings. For instance, in a manufacturing

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company, IIoT can be used to efficiently track and manage the supply chain, perform
quality control and assurance, and lower the total energy consumption.
Smartness in IoT: Another characteristic of IoT, which is highlighted in recent
definitions, is “smartness”. This distinguishes IoT from similar concepts like sensor
networks and it can be further categorized into “object smartness” and “network
smartness”. A smart network is a communication infrastructure characterized by the
following functionalities:
• standardization and openness of the communication standards used, from
layers interfacing with the physical world (i.e. tags and sensors) up to the
communication layers between nodes and with the Internet;
• object addressability (direct IP address) and multi-functionality, i.e. the
possibility that a network built for one application (e.g. road traffic
monitoring) be available for other purposes (e.g. environmental pollution
monitoring or traffic safety) [9].
Market share: In addition, definitions draw special attention to potential market of
IoT with fast growing rate by having a market value of $44.0 billion in 2011[10].
According to a comprehensive market research conducted by RnRMarketResearch[11]
that includes current market size and future predictions, IoT and Machine-to-Machine
(M2M) market will be approximately worth $498.92 billion by 2019. Quoting from the
same research, the value of IoT market is expected to hit $1423.09 billion by 2020, while
Internet of Nano Things (IoNT) playing a key role in future market and holding a value
of approximately $9.69 billion by 2020.

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Besides all these fantastic and optimistic opportunities, for current IoT to reach the
foreseen market, various innovations and progress in different areas are required.
Furthermore, cooperation and information sharing between leading companies in IoT
such as Microsoft, IBM, Google, Samsung, Cisco, Intel, ARM, Fujitsu, Ecobee Inc, and
other smaller businesses and start-ups will boost IoT adoption and market growth.
IoT growth rate with estimated number of active devices until 2018 is depicted in
Figure 1.2 [88]. The increase of investment in IoT by developed and developing countries
hints at the gradual change in strategy of governments by recognizing IoT's impacts and
trying to keep themselves updated as IoT gains momentum. For example, the IoT
European Research Cluster (IERC)
1
has conducted and supported several projects about
fundamental IoT researches by considering special requirements from end-users and
applications. As an example, the project named Internet of Things Architecture (IoT-A)
2
aimed at developing a reference architecture for specific type of applications in IoT and is
discussed in more details in Section 1.3. UK government has also initiated a 5 million
project on innovations and recent technological advances on IoT[12]. Similarly, IBM in
USA[13] have plans to spend billions of dollars on IoT research and its industrial
applications. Singapore has also announced its intention to be the first smart nation by
investing on smart transport systems, developing the e-government structure, and using
surveillance cameras and other sensory devices to obtain data and extract information
from them[14].
1
http://www.rfid- in- action.eu/cerp/
2
http://www.iot-a.eu

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Figure 1.2 : IoT trend forecast [88]
Human in the loop: IoT is also identified as an enabler for machine-to-machine,
human-to-machine, and human-with-environment interactions. With the increase in
number of smart devices and adoption of new protocols such as IPv6, the trend of IoT is
expected to shift towards fusion of smart and autonomous network of Internet-capable
objects equipped with the ubiquitous computing paradigm. Involving human in the loop
[89] of IoT offers numerous advantages to a wide range of applications including
emergency management, healthcare, etc. Therefore, another essential role of IoT is
building a collaborative system that is capable of effectively responding to an event
captured via sensors, by effective discovery of crowds and also successful
communication of information across discovered crowds of different domains.
Improving the quality of life: IoT is also recognized by the impacts on quality of
life and businesses[8] which can revolutionize the way our medical systems and
businesses operate via: 1) expanding the communication channel between objects by
providing more integrated communication environment where different sensors data such
0
5000000
10000000
15000000
20000000
25000000
30000000
35000000
200420052006200720082009201020112012201320142015201620172018
Estimated number of devices
Internet of Everything
PCs Smartphones Tablets IoT

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as location, heartbeat , etc can be measured and shared more easily. 2) Facilitating the
automation and control process, where administrators can manage each object's status via
remote consoles. 3) savings in the overall cost of implementation, deployment, and
maintenance, by providing detailed measurements and the ability to check the status of
devices remotely.
According to Google Trends, the word “IoT” is used more often than “Internet of
Things” since 2004 and after that “web of things” and “Internet of Everything” are the
most frequently used words. Quoting the same reference, Singapore and India are the
countries with the most regional interest about Internet of Things. This is aligned with the
fact that India is estimated to be the world’s largest consumer of IoT devices by
2020[15].
1.3 IoT Architectures
The building blocks of IoT are sensory devices, remote service invocation,
communication networks, and context-aware processing of events and these have been
around for many years. However, what IoT tries to picture is a unified network of smart
objects and human beings responsible for operating them (if needed) that are capable of
universally and ubiquitously communicate with each other.
When talking about a distributed environment, interconnectivity among entities is a
critical requirement and IoT is a good example. A holistic system architecture for IoT
needs to guarantee flawless operation of its components (reliability is considered as the
most import design factor in IoT) and link the physical and virtual realms together. To
achieve so, careful consideration is needed in designing failure recovery and scalability.

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Additionally, since mobility and dynamic change of location has become an integral part
of IoT systems with the widespread use of smart phones, state-of-the-art architectures
need to have certain level of adaptability to properly handle dynamic interactions within
the whole ecosystem.
Reference architectures and models give a bird-eye view of the whole underlying
system, hence their advantage over other architectures relies in providing better and
greater level of abstraction which consequently hides specific constraints and
implementation details.
Several research groups have proposed reference architectures for IoT[16],[17]. The
IoT-A[16] focuses on the development and validation of an integrated IoT network
architecture and supporting building blocks, with the objective to be “the European
Lighthouse Integrated Project addressing the Internet-of-Things Architecture”. IoT-i
project, related to the former mentioned IoT-A project, focuses on the promotion of IoT
solutions, catching requirements and interests. IoT-i aims to achieve strategic objectives
such as: creating a joint strategic and technical vision for the IoT in Europe that
encompasses the currently fragmented sectors of the IoT domain holistically, and
contributes to the creation of an economically sustainable and socially acceptable
environment in Europe for IoT technologies and respective R&D activities.
Figure 1.3 depicts outline of our extended version of a reference architecture for
IoT[17]. Different service and presentation layers are shown in this architecture. Service
layers include event processing and analytics, resource management and service
discovery as well as message aggregation and Enterprise Service Bus (ESB) services
built on top of communication and physical layers. API management which is essential

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for defining and sharing system services and web-based dashboards (or equivalent
smartphone applications) for managing and accessing these APIs are also included in the
architecture. Due to importance of device management, security and privacy enforcement
in different layers, and the ability to uniquely identify objects and control their access
level, these components are prestressed independently in this architecture. These
components and the related research projects are described in more details throughout
this chapter.
Figure 1.3: A reference architecture for IoT

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1.3.1 SOA-based Architecture
In IoT, service-oriented architecture (SOA) might be imperative for the service providers
and users[18],[19]. SOA ensures the interoperability among the heterogeneous devices
[20],[21]. To clarify this, let us consider a generic SOA consists of four layers with
distinguished functionalities as below:
• Sensing layer is integrated with available hardware objects to sense the statuses of
things;
• Network layer is the infrastructure to support over wireless or wired connections
among things;
• Service layer is to create and manage services required by users or applications;
• Interfaces layer consists of the interaction methods with users or applications.
Generally, in such architecture a complex system is divided into subsystems that are
loosely coupled and can be reused later (modular decomposability feature), hence
providing an easy way to maintain the whole system by taking care of its individual
components[22]. This can ensure that in the case of a component failure the rest of the
system (components) can still operate normally. This is of immense value for effective
design of an IoT application architecture where reliability is the most significant
parameter.
SOA has been intensively used in wireless sensor networks, due to its appropriate
level of abstraction and advantages pertaining to its modular design[23],[24]. Bringing
these benefits to IoT, SOA has the potential to augment the level of interoperability and
scalability among the objects in IoT. Moreover, from the user's perspective, all services
are abstracted into common sets, removing extra complexity for the user to deal with

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different layers and protocols[25]. Additionally, the ability to build diverse and complex
services by composing different functions of the system (i.e modular composability)
though service composition suits the heterogeneous nature of IoT, where accomplishing
each task requires a series of service call on all different entities spread across multiple
locations [26].
1.3.2 API-oriented Architecture
Conventional approaches for developing service-oriented solutions use SOAP and
Remote Method Invocation (RMI) as means for describing, discovering, and calling
services, however, due to overhead and complexity imposed by these techniques, Web
APIs and Representational State Transfer (REST)-based methods introduced as
promising alternative solutions. The required resources range from network bandwidth to
computational and storage capacity and are triggered by request-response data
conversions happening regularly during service calls. Lightweight data exchange formats
like JSON can reduce the aforementioned overhead, especially for smart devices and
sensors with limited amount of resources, by replacing large XML files used to describe
services. This helps in using the communication channel and processing power of devices
more efficiently.
Likewise, building APIs for IoT applications helps service provider attract more
customers while focusing on the functionality of their products rather than on
presentation. In addition, it is easier to enables multi-tenancy by the security features of
modern Web APIs such as OAuth, APIs which indeed is capable of boosting an

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organization's service exposition and commercialization. It also provides more efficient
service monitoring and pricing tools than previous service-oriented approaches[27].
To this end, in our previous research we have proposed Simurgh [28] which describes
devices, sensors, humans, and their available services using web API notation and API
definition languages. Furthermore, a two-phase discovery approach was proposed in the
framwork to find sensors that provide desirable services and match certain features, like
being in a specific location. Similarly, Elmangoush, et al.[29] proposed a service broker
layer (named FOKUS) that exposes set of APIs for enabling shared access to the
OpenMTC core. Novel approaches for defining and sharing services in distributed and
multi-agent environments like IoT can reduce the sophistication of service discovery in
the application development cycle and diminish service call overhead in runtime.
Shifting from Service delivery platforms (SDPs) towards web-based platforms and
the benefits of doing so are discussed by Manzalini et al. [30]. Developers and business
managers are advised to focus on developing and sharing APIs from the early stage of
their application development life cycle, so that eventually by properly exposing data to
other developers and end users, an open data environment is created that facilitates
collaborative information gathering, sharing, and updating.
1.4 Resource Management
Picturing IoT as a big graph with numerous nodes with different resource capacity,
selecting and provisioning the resources greatly impacts Quality of Service (QoS) of the
IoT applications. Resource management is very important in distributed systems and have
been a subject of research for years. What makes resource management more challenging

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for IoT relies in the heterogeneous and dynamic nature of resources in IoT. Considering
large-scale deployment of sensors for a smart city use-case, it is obvious that an efficient
resource management module needs consider robustness, fault-tolerance, and scalability,
energy efficiency, QoS, and SLA.
Resource management involves discovering and identifying all available resources,
partitioning them to maximize a utility function which can be in terms of cost, energy,
performance, etc, and finally scheduling the tasks on available physical resources. Figure
1.4 depicts the taxonomy of resource management activities in IoT.
Figure 1.4 : Taxonomy of resource management in IoT

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1.4.1 Resource Partitioning
The first step for satisfying resource provisioning requirements in IoT is to efficiently
partition the resources and gain higher utilization rate. This idea is vastly used in cloud
computing via virtualization techniques and commodity infrastructures, however, virtual
machines are not the only method for achieving the aforementioned goal. Since the
hypervisor, that is responsible for managing interactions between host and guest VMs,
require considerable amount of memory and computational capacity, this configuration is
not suitable for IoT where devices often have constrained memory and processing power.
To address these challenges, the concept of Containers has emerged as a new form of
virtualization technology that can match the demand of devices with limited resources.
Docker
3
and Rocket
4
are the two most famous container solutions.
Containers are able to provide portable and platform-independent environments for
hosting the applications and all their dependencies, configurations, and input/output
settings. This significantly reduces the burden of handling different platform-specific
requirements when designing and developing applications, hence providing convenient
level of transparency for applications architects and developers. In addition, containers
are lightweight virtualization solutions that enable infrastructure providers to efficiently
utilize their hardware resources by eliminating the need for purchasing expensive
hardware and virtualization software packages. Since containers, compared to VMs,
require considerably less spin-up time, they are ideal for distributed applications in IoT
that need to scale up within a short amount of time.
3
https://www.docker.com/
4
Available at https://github.com/coreos/rkt

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An extensive survey by Gu et al.[91] focuses on virtualization techniques
proposed for embedded systems and their efficiency for satisfying real-time application
demands. After explaining numerous Xen-based, KVM-based, and microkernel-based
solutions that utilise processor architectures such as ARM, authors argue that operating
system virtualization techniques, known as container-based virtualization, can bring
advantages in terms of performance and security by sandboxing applications on top of a
shared OS layer. Linux VServer[92], Linux Containers LXC, and OpenVZ are examples
of using OS virtualization in embedded systems domain.
The concept of virtualized operating systems for constrained devices has been
further extended to smartphones by providing means to run multiple Android operating
systems on a single physical smartphone[93]. With respect to heterogeneity of devices in
IoT and the fact that many of them can leverage virtualization to boost their utilisation
rate, task-grain scheduling which considers individual tasks within different containers
and virtualized environments can potentially challenge current resource management
algorithms that view these layers as blackbox[91].
1.4.2 Computation Offloading
Code offloading (computation offloading) [90] is another solution for addressing the
limitation of available resources in mobile and smart devices. The advantages of using
code offloading appear in more efficient power management, less storage requirements,
and higher application performance. Several surveys about computation offloading has
carefully studied its communication and execution requirements as well as its adaptation

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criteria[31],[32],[33], hence here we mention some of the approaches that focus on
efficient code segmentation and cloud computing.
Majority of code offloading techniques require the developers to manually annotate
the functions required to execute on another device[32]. However, using static code
analyzers and dynamic code parsers is an alternative approach that results in better
adaptivity in case of network fluctuations and increased latency[34]. Instead of using
physical instances, ThinkAir[35] and COMET[36] leverage virtual machines offered by
IaaS cloud providers as offloading targets to boost both scalability and elasticity. The
proposed combination of VMs and mobile clouds can create a powerful environment for
sharing, synchronizing, and executing codes in different platforms.
1.4.3 Identification and Resource/Service Discovery
Internet of Things has emerged as a great opportunity for industrial investigations and
similarly pursued by research communities, but current architectures proposed for
creation of IoT environments lack support for efficient and standard way of service
discovery, composition, and their integration in scalable manner[37].
The discovery module in IoT is twofold. First objective is to identify and locate the
actual device, which can be achieved by storing and indexing metadata information about
each object. The final step is to discover the target service that needs to be invoked.
Lack of effective discovery algorithm can result in execution delays, poor use
experience, and runtime failures. As discussed in[38], efficient algorithms that
dynamically choose centralized or flooding strategies can help minimize the consumed
energy, although, other parameters such as mobility and latency should be factored in to

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offer a suitable solution for IoT, considering its dynamic nature. In another approach
within the fog computing context[39], available resources like network bandwidth and
computational and storage capacity metrics are converted to time resources, forming a
framework that facilitates resource sharing. Different parameters like energy
consumption level, price, and availability of services need to be included in proposing
solutions that aim to optimize resource sharing within heterogeneous pool of resources.
The Semantic Web of Things (SWoT) envisions advanced resource management and
service discovery for IoT by extending Semantic Web notation and blending it with IoT
and Web of Things. To achieve so, resources and their metadata are defined and
annotated using standard ontology definition languages such as RDF and OWL.
Additionally, search and manipulation of these metadata can be done through query
languages like SPARQL. Ruta et al [94] has adopted the SSN-XG W3C ontology to
collect and annotate data from Semantic Sensor Networks (SSN) and by extending the
CoAP protocol (discussed in section 1.6) and CoRE Link Format that is used for resource
discovery, their proposed solution ranks resources based on partial or full request
matching situations.
1.5 IoT Data Management and Analytics
While Internet of Things (IoT) is getting momentum as enabling technology for creating
a ubiquitous computing environment, special considerations are required to process huge
amount of data originating from and circulating in such a distributed and heterogeneous
environment. To this extent, Big Data related procedures such as data acquisition,

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filtering, transmission, and analysis have to be updated to match the requirements of IoT
data deluge.
Generally, Big Data is characterized by 3Vs, namely velocity, volume, and variety.
Focusing on individual or combination of the three mentioned Big Data dimensions has
lead to the introduction of different data processing approaches. Batch Processing and
Stream Processing are two major methods used for data analysis. Lambda Architecture
[40] is an exemplary framework proposed by Nathan Marz to handle Big data processing
by focusing on multi-application support, rather than data processing techniques. It has
three main layers that enable the framework to support easy extensibility through
extension points, scale-out capabilities, low latency query processing, and the ability to
tolerate human and system faults. From a top-down view, first layer is called “Batch
Layer” and hosts the master dataset and batch views where pre-computed queries are
stored. Next is the “Serving Layer” which adds dynamic query creation and execution to
the batch views by indexing and storing them, and finally, the “Speed Layer” captures
and processes recent data for delay-sensitive queries.
Collecting and analysing the data circulating in IoT environment is where the real
power of IoT resides [41]. To this end, applications utilize pattern detection and data
mining techniques to extract knowledge and make smarter decisions. One of the key
limitations in using currently developed data mining algorithms lies in the inherent
centralized nature of these algorithms which drastically affects their performance and
makes them unsuitable for IoT environments that are meant to be geographically
distributed and heterogeneous. Distributed anomaly detection techniques that process
multiple streams of data concurrently to detect outliers have been well studied in the

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literature [42]. A comprehensive survey of data mining researches in IoT has been
conducted by Tsai et al.[44] and includes details about various classification, clustering,
knowledge discovery in databases (KDD), and pattern mining techniques. Nevertheless,
new approaches like ellipsoidal neighbourhood factor outlier [43] that can be efficiently
implemented on constrained devices are not fully benchmarked in respect to different
configurations of their host devices.
1.5.1IoT and the Cloud
Cloud computing due to its on-demand processing and storage capabilities can be used to
analyse data generated by IoT objects in batch or stream format. Pay-as-you-go model
adopted by all cloud providers has reduced the price of computing, data storage, and data
analysis, creating a streamlined process for building IoT applications. With cloud's
elasticity, distributed Stream Processing Engines (SPEs) can implement important
features such as fault-tolerance and auto-scaling for bursty workloads.
IoT application development in clouds has been investigated in number of researches.
Alam et al.[45] proposed a framework that supports sensor data aggregation in cloud-
based IoT context. The framework is an SOA-based in event-driven and defines and a
benefits from a semantic layer that is responsible for event processing and reasoning.
Similarly, Li et al.[46] proposed a Platform as a Service (PaaS) solution for deployment
of IoT applications. The solution is multi-tenant and a virtually isolated service is
provided for users that can be customized to their IoT devices while sharing the
underlying cloud resources with other tenants.

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Nastic et al.[47] proposed PatRICIA, a framework that provides a programming
model for development of IoT applications in the cloud. PatRICIA proposes new
abstraction layer that is based on the concept of Intent-based programming. Parwekar[48]
discussed the importance of identity detection devices in IoT and proposed a service layer
to demonstrate how a sample tag-based acquisition service can be defined in the cloud. A
simple architecture for integrating machine to machine (M2M) platform, network, and
data layers has also been proposed. Focusing on the data aspect of IoT, in our previous
research we proposed an architecture based on Aneka by adding support for data filtering,
multiple simultaneous data source selection, load balancing, and scheduling[49].
IoT applications can harness cloud services and use the available storage and
computing resources to meet their scalability and compute-intensive processing demands.
Most of current design approaches for integrating cloud with IoT are based on a three tier
architecture where the bottom layer consists of IoT devices, middle layer is the cloud
provider, and top layer hosts different applications and high-level protocols. However,
using this approach to design and integrate cloud computing with an IoT middleware
limits the practicality and fully utilization of cloud computing in scenarios where
minimizing end-to-end delay is the goal. For example in online game streaming where
perceived delay is an important factor for user satisfaction, a light and context-aware IoT
middleware [50] that smartly selects nearest Content Distribution Network (CDN) can
significantly reduce the overall jitter.

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1.5.2 Real-time Analytics in IoT and Fog Computing
Current data analytics approaches mainly focus on dealing with Big Data, however,
processing data generated from millions of sensors and devices in real time is a more
challenging[51]. Proposed solutions that only utilize cloud computing as processing or
storage backbone are not scalable and cannot address the latency constraints of real-time
applications. Real-time processing requirements and the increase in computational power
of edge devices like routers, switches, and access points lead to the emergence of Edge
Computing paradigm.
Edge layer contains the devices that are in closer vicinity to the end user than the
application servers and can include smartphones, smart TVs, network routers, etc.
Processing and storage capability of these devices can be utilized to extend the
advantages of using cloud computing by creating another cloud, known as Edge Cloud,
near application consumers in order to: decrease networking delays, save processing or
storage cost, perform data aggregation, and avoid sensitive data leaving the local
network[52].
Similarly, Fog Computing is a term coined by Salvatore Stolfo[53] and applies to an
extension of cloud computing that aims to keep the same features of Cloud such as
networking, compute, virtualization, and storage, but also meet the requirements of
applications that demand low latency, specific QoS requirements, Service Level
Agreement (SLA) considerations, or any combination of them[54]. Moreover, these
extensions can ease application development for mobile applications, Geo-distributed
applications such as wireless sensor networks, and large-scale systems used for
monitoring and controlling other systems, such as surveillance camera networks[55],[56].

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A comparison of Cloud and Fog features is presented in Table 1.1 and Figure 1.5 shows a
general architecture for using cloud and fog computing together.
Table 1.1: Cloud versus Fog
Fog
Cloud
Response time
Low
High
Availability
Low
High
Security level
Medium to hard
Easy to medium
Service focus
Edge devices
Network/enterprise core services
Cost for each device
Low
High
Dominant architecture
Distributed
Central/distributed
Main content generator-consumer
Smart Devices-humans and devices
Humans-end devices
Stonebraker et al. [57] pointed that the following requirements should be fulfilled in
an efficient real-time stream processing engine (SPE):
1) data fluidity, which refers to processing data on-the-fly without need for costly
data storage,
2) handling out-of-order, missing, and delayed streams,
3) having repeatable and deterministic outcome after processing series or bag of
streams,
4) keeping streaming and stored data integrated by using embedded database
systems,
5) assuring high-availability using real-time failover and hot backup mechanisms,
6) supporting auto-scaling and application partitioning.

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To harness the full potential of Fog computing for applications demanding real-time
processing, researcher can look into necessary approaches and architectures to fulfil the
above-mentioned requirements.
Figure 1.5: Typical Fog computing architecture
1.6 Communication Protocols
From the network and communication perspective, IoT can be viewed as aggregation of
different networks including mobile networks (3G, 4G, CDMA, etc), WLANs, Wireless
Sensor Networks (WSN) and Mobile Adhoc Networks (MANET)[18].
Seamless connectivity is a key requirement for IoT. Network communication speed,
reliability, and connection durability will impact overall IoT experience. With the
emergence of high-speed mobile networks like 5G and higher availability of local and
urban network communication protocols such as Wi-Fi, Bluetooth, and WiMax, creating
an interconnected network of objects seems feasible, however dealing with different
communication protocols that link these environments is still challenging.
Cloud Computing (Centralized computing)
Applications, Private/Public cloud hosting, Core Network
Fog Computing
(Distributed computing)
Access points, Wi-Fi/LAN
End Devices (Smart Devices)
Vehicles, Setup box, IP TV, Machines, Gaming
Fog Computing
(Distributed computing)
Access points, Wi-Fi/LAN
Fog Computing
(Distributed computing)
Access points, Wi-Fi/LAN

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1.6.1 Network Layer
Based on the devices specification (memory, CPU, storage, battery life), the
communication means and protocols vary. However, the commonly used communication
protocols and standards are listed below:
• RFID (e.g. ISO 18000 series that come with 5 classes and 2 Generations and
cover both active and passive RFID tags)
• IEEE 802.11 (WLAN), IEEE 802.15.4 (ZigBee), Near Field Communication
(NFC), IEEE 802.15.1 (Bluetooth)
• Low power Wireless Personal Area Networks (6LoWPAN) standards by IEFT
• Machine to Machine (M2M) protocols such as MQTT and CoAP
• IP layer technologies such as IPv4, IPv6, etc.
More elaboration on the above mentioned network layer communication protocols is
available in [58] and a breakdown of layers in IoT communication stack that these
protocols will operate is shown in Figure 1.6.

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Figure 1.6: Use of various protocols in IoT communication layers
1.6.2 Transport and Application Layer
Segmentation and poor coherency level, which are results of pushes from individual
companies to maximize their market share and revenue, has made developing IoT
applications cumbersome. Universal applications that require one-time coding and can be
executed on multiple devices are the most efficient.
Protocols in IoT can be classified into three categories:
1) general-purpose protocols like IP and SNMP that has been around for many years
and are vastly used to manage, monitor, configure network devices, and establish
communication links ;

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2) lightweight protocols such as CoAP that has been developed to meet the
requirements of constrained devices with tiny hardware and limited resources;
3) device or vendor specific protocols and APIs that usually require certain build
environment and toolset.
Selecting the right protocols at the development phase can be challenging and
complex as factors such as future support, ease of implementation, and universal
accessibility have to be considered. Additionally, thinking of other aspects that will affect
the final deployment and execution, like required level of security and performance, will
add to the sophistication of protocol selection stage. Lack of standardization for particular
applications and protocols is another factor that increases the risk of poor protocol
selection and strategic mistakes that are more expenses to fix in the future. Yet abother
challenge is insufficient documentation for some protocols sensors and smart devices
limits their usage in IoT.
Table 1.2 summarizes the characteristics of major communication protocols in IoT,
while also comparing their deployment topology and environments.
Table 1.2: IoT communication protocols comparison
Protocol
Name
Transport
Protocol
Messaging Model
Security
Best Use cases
Architecture
AMPQ
TCP
Publish/Subscribe
High-Optional
Enterprise
Integration
P2P
CoAP
UDP
Request/Response
Medium-
Optional
Utility field
Tree
DDS
UDP
Publish/Subscribe and Request/Response
High-
Optional
Military
Bus
MQTT
TCP
Publish/Subscribe and Request/Response
Medium-
Optional
IoT messaging
Tree
UPnP
-
Publish/Subscribe and Request/Response
None
Consumer
P2P

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XMPP
TCP
Publish/Subscribe and Request/Response
High-
Compulsory
Remote
management
Client server
ZeroMQ
UDP
Publish/Subscribe and Request/Response
High-
Optional
CERN
P2P
Machine to Machine (M2M) communication aims to enable seamless integration of
physical and virtual objects into larger and geographically distributed enterprises by
eliminating the need for human intervention. To achieve so, enforcing harmony and
collaboration among different communication layers (physical, transport, presentation,
application) and approaches used by devices for message storage and passing can be
challenging [59].
Publish/subscribe model is a common way of exchanging messages in distributed
environments and because of simplicity, it has been adopted by popular M2M
communication protocols like MQTT. In dynamic scenarios where nodes join or leave
the network frequently and handoffs are required to keep the connections alive,
publish/subscribe model is efficient. This is because of using push-based notifications
and maintaining queues for delayed delivery of messages.
On the other hand, protocols like HTTP/REST and CoAP only support the
request/response model in which pulling mechanism is used to fetch new messages from
the queue. CoAP also uses IPv6 and 6LoWPAN protocols in its network layer to handle
node identification. Ongoing efforts are still being made to merge these protocols and
standardize them as to support both publish/subscribe and request/response
models[60],[61].

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1.7 Internet of Things Applications
IoT promises an interconnected network of uniquely identifiable smart objects. This
infrastructure creates the necessary backbone for many interesting applications that
require seamless connectivity and addressability between their components. The range of
IoT application domain is wide and encapsulates applications from home automation to
more sophisticated environments such as smart cities and e-government.
Industry-focused applications include logistics and transportation[62], supply chain
management[63], fleet management, aviation industry, and enterprise automation
systems. Healthcare systems, smart cities and buildings, social IoT, and smart shopping
are few examples of applications that try to improve the daily life of individuals, as well
as the whole society. Disaster management, environmental monitoring, smart watering
and optimizing energy consumption through smart grids and smart metering are examples
of applications that focus on environment.
In a broader magnitude, Asin and Gascon[64] classified 54 different IoT applications
under the following categories: smart environment, smart cities, smart metering, smart
water, security and emergencies, retail, logistics, industrial control, smart agriculture,
smart animal farming, domestic and home automation, and eHealth. For further
reference, Kim et al.[65] have surveyed and classified researches about IoT applications
based on application domain and target user groups.
In this section we present categorization of enterprise IoT applications based on their
usage domain. These applications usually fall into the following three categories: 1)
Monitoring and actuating, 2) Business process and data analysis, 3) Information

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gathering and collaborative consumption. The rest of this section is dedicated to
characteristics and requirements of each category.
1.7.1 Monitoring and actuating
Monitoring devices via APIs can be helpful in multiple domains. The APIs can report
power usage, equipment performance, sensors status, and perform actions upon sending
pre-defined commands. Real-time applications can utilise these features to report current
system status, while managers and developers have the option to freely call these APIs
without the need of physically accessing the devices. Smart metering, and in a more
distributed form, smart grids can help in identifying production or performance defects
via application of anomaly detection on the collected data and thus increase the
productivity. Likewise, incorporating IoT in building to or even in the construction
process [66] helps in moving towards green solutions, saving energy ,and consequently
minimising operation cost.
Another area that has been under focus by researchers is applications targeting smart
homes that mainly target energy saving and monitoring. Home monitoring and control
frameworks like the ones developed by Verizon[67] and Boss support different
communication protocols (Wi-Fi, Bluetooth, etc) to a create an interconnected network of
objects that can control desired parameters and change configurations based on user’s
settings.

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1.7.2 Business process and data analysis
Riggins et al. [68] categorized level of IoT adoption through Big Data analytics usage to
the following categories:
1) Society level where IoT mainly influences and improves government services by
reducing cost and increasing government transparency and accountability,
2) Industry level in which manufacturing, emergency services, retailing, and education
have been studied as examples,
3) Organizational level in which IoT can bring same type of benefits as those mentioned
in society level,
4) Individual level where daily life improvements and individual efficiency and
productivity growth are marked as IoT benefits.
The ability to capture and store vast amounts of individual data has brought
opportunities to healthcare applications. Patients’ data can be captured more frequently,
using wearable technologies such as smart watches, and can be published over internet.
Later, data mining and machine learning algorithms are used to extract knowledge and
patterns from the raw data and archive these records for future references. Healthsense
eNeighbor developed by Humana is an example of a remote controlling system that uses
sensors deployed in houses to measure frequent daily activities and heath parameters of
occupants. The collected data is then analysed to forecast plausible risks and produce
alerts prevent incidents [69]. Privacy and security challenges are two main barriers that
refrain people and industries from embracing IoT in the healthcare domain.

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1.7.3 Information gathering and collaborative
consumption
Social Internet of Things (SIoT) is where IoT meets social networks and to be more
precise, it promises to link objects around us with our social media and daily interaction
with other people, making them look smarter and more intractable. SIoT concept,
motivated by famous social media like Facebook and Twitter, has the potential to affect
many people’s life style. For example, social network is helpful for evaluation of trust of
crowds involved in an IoT processes. Another advantages is using the humans and their
relationships, communities, and interactions for effective discovery of IoT services and
objects [95].
Table 1.3 contains a list of past and present open source projects regarding IoT
development and its applications
Table 1.3: List of IoT-related projects
Name of project/product
Area of focus
Tiny OS
Operating System
Contiki
Operating System
Mantis
Operating System
Nano-RK
Operating System
LiteOS
Operating System
FreeRTOS
Operating System
RIOT
Operating System
Wit.AI
Natural Language
Node-RED
Visual Programming Toolkit
NetLab
Visual Programming Toolkit
SensorML
Modeling and Encoding
Extended Environments Markup Language (EEML)
Modeling and Encoding
ProSyst
Middleware
MundoCore
Middleware

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Gaia
Middleware
Ubiware
Middleware
SensorWare
Middleware
SensorBus
Middleware
OpenIoT
Middleware and development platform
Koneki
M2M Development Toolkit
MIHINI
M2M Development Toolkit
1.8 Security
As adoption of IoT continues to grow, attackers and malicious users are shifting their
target from servers to end devices. There are several reasons for this, first in terms of
physical accessibility, smart devices and sensors are far less protected than servers and
having physical access to a device gives the attackers privilege to penetrate with less
hassle. Second, the number of devices that can be compromised are way more than
number of servers. Moreover, since devices are closer to the users, security leads to leak
of valuable information and has catastrophic consequences. Finally, due to heterogeneity
and distributed nature of IoT, patching process is more consuming, thus opening the door
for attackers[71].
In an IoT environment, resource constraints are the key barrier for implementing
standard security mechanisms in embedded devices. Furthermore, wireless
communication used by majority of sensor networks is more vulnerable to
eavesdropping and man-in-the-middle (proxy) attacks.
Cryptographic algorithms need considerable bandwidth and energy to provide end-to-
end protection against attacks on confidentiality and authenticity. Solutions have been
proposed in RFID[72],[73] and wireless sensor network[74] context to overcome

page 34
aforementioned issues by considering light cryptographic techniques. With regards to
constrained devices, symmetric cryptography is applied more often as it requires less
resources, however public key cryptography in the RFID context has been also
investigated[75].
Wireless sensor networks with RFID tags and their corresponding readers were the
first infrastructure for building IoT environments and even now, many IoT applications in
logistics, fleet management, controlled farming, and smart cities rely on these
technologies. Nevertheless, these systems are not secure enough and are vulnerable to
various attacks from different layers. A survey by Borgohain et al. [76] investigate these
attacks, but less attention is given to solutions and counter-attack practices.
1.9 Identity Management and Authentication
When talking about billions of connected devices, methods for identifying objects and
setting their access level play an important role in the whole ecosystem. Consumers, data
sources, and service providers are essential parts of IoT, identity management and
authentication methods applied to securely connect these entities affects both the amount
of time required to establish trust and the confidence degree[4]. IoT’s inherent features
like dynamism and heterogeneity require specific consideration when defining security
mechanisms. For instance, in Vehicular Networks (VANETs), cars regularly enter and
leave the network due to their movement speed, thus not only cars need to interact and
exchange data with access points and sensors along the road, but also they need to
communicate with each other and form a collaborative network.

page 35
Devices or objects in IoT have to be uniquely identified. There are various
mechanisms such as ucode which generates 128 bit codes and can be used in active and
passive RFID tags and Electric Product Code (EPC) which creates unique identifiers
using Uniform Resource Identifier (URI) codes[77],[78]. Being able to globally and
uniquely identify and locate objects decreases the complexity of expanding the local
environment and linking it with the global markets[76].
It is common for IoT sensors and smart devices to share the same geographical
coordinates and even fall into same type or group, hence identity management can be
delegated to local identity management systems. In such environments, local identity
management systems can enforce and monitor access control policies and establish trust
negotiations with external partners. Liang et al. [79] investigated security requirements
for multimedia applications in IoT and proposed an architecture that supports traffic
analysis and scheduling, key management, watermarking, and authentication. Context-
aware pairing of devices and automatic authentication is another important requirement
for dynamic environments like IoT. Solutions that implement zero-interaction
approach[80] to create simpler yet more secure procedure for creating ubiquitous network
of connected devices can considerably impact IoT and its adoption.
1.10 Privacy
According to the report published by IDC and EMC on December 2012[81], the size of
digital universe containing all created, replicated, and consumed digit data will be
roughly doubled each two year, hence, forecasting its size to be 40,000 Exabytes till
2020, compared to 2.837 Exabytes for 2012. Additionally, sourced from

page 36
statisticbrain.com, the average cost of storage for hard disks has dropped from $437,500
per Gigabyte in 1980 to $0.05 per Gigabyte in 2013. These statistics show the importance
of data and the fact that it is easy and cheap to keep user's data for a long time and follow
the guideline of harvesting as much data as possible and using it when required.
Data generation rate has drastically increased in recent years and consequently
concerns about secure data storage and access mechanisms has be taken more seriously.
With sensors capable of sensing different parameters such as users' location, heartbeat,
and motion, data privacy will remain a hot topic to ensure users have control over the
data they share and the people who have access to these data.
In distributed environments like IoT, preserving privacy can be achieved by either
following a centralized approach or by having each entity manage its own
inbound/outbound data, a technique known as privacy-by-design[76]. Considering the
latter approach, since each entity can access only chunks of data, distributed privacy
preserving algorithms have been developed to handle data scattering and their
corresponding privacy tags[82]. Privacy enhancing technologies[83], [84] are good
candidates for protecting collaborative protocols. In addition, to protect sensitive data,
rapid deployable enterprise solutions that leverage containers on top of virtual machines
can be used[85].
1.11 Standardization and Regulatory Limitations
Standardization and the limitation caused by regulatory policies have challenged the
growth and adoption rate of IoT and can be potential barriers in embracing the
technology. Defining and broadcasting standards will ease the burden of joining IoT

page 37
environments for new users and providers. Additionally, interoperability among different
components, service providers, and even end users will be greatly influenced in a positive
way, if pervasive standards are introduced and employed in IoT[86].
Even though more organizations and industries make themselves ready to embrace
and incorporate IoT, increase in IoT growth rate will cause difficulties for
standardization. Strict regulations about accessing radio frequency levels, creating
sufficient level of interoperability among different devices, authentication, identification,
authorization, and communication protocols are all open challenges facing IoT
standardization. Table 1.4 contains a list of organizations that has worked towards
standardizing technologies used within IoT context or those specifically created for IoT.
Table 1.4: IoT standards
Organization Name
Outcome
Internet of Things Global Standards Initiative
(IoT-GSI)
JCA-IoT
Open Source Internet of Things (OSIOT)
Open Horizontal Platform
IEEE
802.15.4 standards, developing a reference architecture
Internet Engineering Task Force (IETF)
Constrained RESTful Environments (CoRE), 6LOWPAN, Routing Over Low
power and Lossy networks (ROLL), IPv6
The World Wide Web Consortium (W3C)
Semantic Sensor Net Ontology, Web Socket , Web of Things
XMPP Standards Foundation
XMPP
Eclipse Foundation
Paho project, Ponte project, Kura, Mihini/M3DA, Concierge
Organization for the Advancement of Structured
Information Standards
MQTT, AMPQ

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1.12 Conclusions
Internet-of-Things has emerged as a new paradigm aiming at providing solutions for
integration, communication, data consumption and analysis of smart devices. To this end,
connectivity, interoperability, and integration are inevitable parts of IoT communication
systems. While IoT, due to its highly distributed and heterogeneous nature, is comprised
of many different components and aspects, providing solutions to integrate this
environment and hide its complexity from the user side is inevitable. Novel approaches
that utilize SOA architecture and API definition languages to service exposition ,
discovery, and composition will have huge impact in adoption and proliferation of the
future IoT vision.
In this paper, different building blocks of IoT such as sensors and smart devices,
M2M communication, and the role of humans in future IoT scenarios are elaborated and
investigated. Many challenges ranging from communication requirements to middleware
development still remain open and need further investigations. We highlighted these
shortcomings and provided typical solutions and draw guidelines for future researches in
this area.
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