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Nader Mbarek is an Associate Professor ? HDR at the engineering school, ESIREM, within the University of Bourgogne Franche-Comte, France. He leads the Networking branch of the CombNet team at the LIB (Laboratoire d?Informatique de Bourgogne) laboratory and holds a Computer Science PhD from the University of Bordeaux, France as well as a Habilitation (HDR) degree from the University of Bourgogne Franche-Comte, France.
Preface xi
Chapter 1. Service Level Management in the Internet of Things (IoT) 1Ahmad KHALIL, Nader MBAREK and Olivier TOGNI
1.1. Introduction 1
1.2. IoT: definitions 2
1.3. IoT: an overview 3
1.3.1. IoT architectures 3
1.3.2. Application fields of the IoT 6
1.4. Security management and privacy protection in the IoT 8
1.4.1. Motivations and challenges 8
1.4.2. Security services in the IoT environment 10
1.4.3. Privacy protection and trust in the IoT 18
1.5. QoS management for IoT services 21
1.5.1. Motivations and challenges 21
1.5.2. Guaranteeing QoS in IoT 22
1.6. QBAIoT: QoS-based access method for IoT environments 28
1.6.1. Service level guarantee in the IoT 28
1.6.2. The QBAIoT process in the IoT 31
1.6.3. QBAIoT performance evaluation 36
1.7. Conclusion 38
1.8. References 39
Chapter 2. Service Level Management in the Cloud 45Nader MBAREK
2.1. Introduction 45
2.2. The Cloud environment 46
2.2.1. Cloud Computing 46
2.2.2. Cloud Networking 50
2.2.3. Inter-Cloud 52
2.3. Service level and self-management in the Cloud 54
2.3.1. Quality of Service in a Cloud environment 54
2.3.2. Security in a Cloud environment 57
2.3.3. Self-management of Cloud environments 60
2.4. QoS guarantee in Cloud Networking 63
2.4.1. Cloud Networking architectures 63
2.4.2. Performance evaluation 68
2.5. Conclusion 75
2.6. References 75
Chapter 3. Managing Energy Demand as a Service in a Smart Grid Environment 83Samira CHOUIKHI, Leila MERGHEM-BOULAHIA and Moez ESSEGHIR
3.1. Introduction 83
3.2. The Smart Grid environment 84
3.2.1. Smart microgrids 85
3.2.2. Information and communication infrastructure 86
3.3. Demand management: fundamental concepts 87
3.3.1. Predicting loads 87
3.3.2. DR - demand response 88
3.4. Demand-side management 89
3.4.1. The architectures and components of DSM platforms 90
3.4.2. Classifying DSM approaches 91
3.4.3. Deterministic approaches for individual users 92
3.4.4. Stochastic approaches for individual users 93
3.4.5. Deterministic approaches for consumer communities 94
3.4.6. Stochastic approaches for consumer communities 94
3.5. Techniques and methods for demand scheduling 96
3.5.1. Game theory 97
3.5.2. Multiagent systems 98
3.5.3. Machine learning 99
3.6. Conclusion 100
3.7. References 101
Chapter 4. Managing Quality of Service and Security in an e-Health Environment 107Mohamed-Aymen CHALOUF
4.1. Introduction 107
4.2. e-health systems 109
4.2.1. Architecture 110
4.2.2. Characteristics 111
4.3. QoS in e-health systems 114
4.3.1. e-health services and QoS 114
4.3.2. QoS management in e-health systems 117
4.4. Security of e-health systems 124
4.4.1. Threats and attacks specific to e-health systems 124
4.4.2. Security management in e-health systems 127
4.5. Conclusion 130
4.6. References 131
Chapter 5. Quality of Service Management in Wireless Mesh Networks 139Hajer BARGAOUI, Nader MBAREK and Olivier TOGNI
5.1. Introduction 139
5.2. WMNs: an overview 140
5.2.1. Definition of a WMN 140
5.2.2. Architecture of a radio mesh wireless network 140
5.2.3. Characteristics of a WMN environment 142
5.2.4. Standards for WMNs 143
5.2.5. Domains of applications 144
5.3. QoS in WMNs 146
5.3.1. QoS in networks 146
5.3.2. QoS constraints in WMNs 146
5.3.3. QoS mechanisms in WMNs 147
5.3.4. Research projects on QoS in WMNs 150
5.4. QoS-based routing for WMNs 152
5.4.1. Routing requirements in WMNs 152
5.4.2. Routing metrics in WMNs 153
5.4.3. QoS-based routing protocols in WMNs 154
5.5. HQMR: QoS-based hybrid routing protocol for mesh radio networks 157
5.5.1. Description of the HQMR protocol 157
5.5.2. How the HQMR protocol works 160
5.5.3. Validation of the HQMR protocol 162
5.6. Conclusion 168
5.7. References 168
Chapter 6. Blockchain Based Authentication and Trust Management in Decentralized Networks 175Axel MOINET and Benoît DARTIES
6.1. Introduction 175
6.1.1. Challenges and motivations, the state of the art 177
6.1.2. Blockchain, a support for authentication and trust 181
6.2. The Blockchain Authentication and Trust Module (BATM) architecture 184
6.2.1. Context and development 184
6.2.2. Managing identities and authentication 185
6.2.3. Calculating trust and reputation using the MLTE algorithm 188
6.3. Evaluating BATM 197
6.3.1. Simulation plan 197
6.3.2. Results and interpretation 198
6.4. Conclusion 201
6.5. References 202
Chapter 7. How Machine Learning Can Help Resolve Mobility Constraints in D2D Communications 205Chérifa BOUCETTA, Hassine MOUNGLA and Hossam AFIFI
7.1. Introduction 205
7.2. D2D communication and the evolution of networks 207
7.2.1. The discovery phase in D2D communications 208
7.2.2. The data exchange phase in D2D communications 209
7.2.3. Investigations into future mobile networks 210
7.3. The context for machine learning and deep learning 210
7.3.1. Overview of deep learning and its application 212
7.3.2. Types of machine learning 213
7.3.3. Linear regression and classification 213
7.4. Dynamic discovery 215
7.4.1. Real-time prediction of user density 216
7.4.2. The dynamic discovery algorithm 217
7.5. Experimental results 218
7.5.1. General hypotheses 218
7.5.2. Traffic with low user density 219
7.5.3. Traffic with high user density 219
7.6. Conclusion 222
7.7. References 222
Chapter 8. The Impact of Cognitive Radio on Green Networking: The Learning-through-reinforcement Approach 227Mohammed Salih BENDELLA and Badr BENMAMMAR
8.1. Introduction 227
8.2. Green networking 228
8.2.1. Why should we reduce energy consumption? 228
8.2.2. Where can we reduce energy consumption? 228
8.2.3. Definition and objectives of green networking 229
8.3. Green strategies 230
8.3.1. Consolidation of resources 230
8.3.2. Selective connectivity 231
8.3.3. Virtualization 231
8.3.4. Energy-proportional computing 231
8.4. Green wireless networks 233
8.4.1. Energy efficiency in wireless networks 235
8.4.2. Controlling transmission power 236
8.5. How CR contributes to green networking 238
8.5.1. The principle behind CR 238
8.5.2. The cognition cycle 238
8.5.3. Green networking in CR networks 240
8.6. Learning through reinforcement by taking into account energy efficiency during opportunistic access to the spectrum 243
8.6.1. Formulating the problem 245
8.6.2. Comparison between CR and Q_learning enabled CR 247
8.7. Conclusion 248
8.8. References 249
List of Authors 253
Index 255
Ahmad KHALIL, Nader MBAREK and Olivier TOGNI
LIB, University of Bourgogne Franche-Comté, Dijon, France
The Internet of Things (IoT) is now an integral part of our daily life. By 2020, there will be over 20 billion connected digital and electronic devices, which works out to about two devices per human being on Earth (Nordrum 2016). The IoT will thus have a significant impact on human life and will improve quality of life. The future growth of the IoT will lead to advanced use of technology in order to facilitate accomplishing daily human tasks. Consequently, improving corresponding services is an important challenge that must be faced in order to allow the expansion of this environment. In this context, it is expected that there will be a better user experience that will make up for the limitations experienced when using IoT services. User experience may translate to a service level that includes the expected Quality of Service (QoS) and also the expected level of security and privacy offered by the IoT environment. The objects connected to the IoT have certain restrictions in terms of memory, computing capacity and energy consumption. However, existing QoS security and privacy protection mechanisms do not take these constraints into consideration. Thus, it is primordial that we design and develop new QoS and security mechanisms or adapt and improve existing mechanisms in the context of the IoT.
In this chapter, we will first introduce, in section 1.2, definitions related to the IoT environment. We will then describe, in section 1.3, the architectures proposed by different standardization bodies and the fields of application of the IoT. section 1.4 introduces security management as well as the management of privacy protection in the IoT through the motivations, challenges and different security services that must be considered in this kind of environment. section 1.5 describes QoS management by highlighting the needs and requirements of every layer of the IoT architecture in terms of QoS as well as the proposed QoS mechanisms that will respond to these. section 1.6 defines our framework using a three-layer IoT architecture and a QoS-based access mechanism concerning the lowest level of this architecture. Finally, section 1.7 presents conclusions and perspectives related to service level management in an IoT environment.
Various standardization bodies have worked on the IoT in order to specify the definitions, architecture, recommendations and the fields of application for this new paradigm. The ITU-T (International Telecommunication Union- Telecommunication Standardization Sector) is a standardization body that works on the IoT environment and its different fields of applications through the SG20 work group. According to the ITU-T document Y.2060, the IoT is a ubiquitous network that is available everywhere, anytime and to anyone (ITU-T 2012). The IoT is a global infrastructure for the information society that makes it possible to offer advanced services by interconnecting objects using various communication technologies (Minerva et al. 2015). Further, in ISO/IEC (2015a), the ISO/IEC (International Organization for Standardization/International Electrotechnical Commission) provides a definition and specification for the vocabulary used within the IoT environment. According to this, the IoT is a network of physical objects that collect and transmit data. It is an infrastructure made up of interconnected objects, humans and information resources that make it possible to process data collected by the objects and then react as a consequence (ISO/IEC 2015a; International Electrotechnical Commission 2017b). According to the Internet Engineering Task Force (IETF), the general idea behind the IoT is to connect objects in order to provide contextual services across different technologies, thereby offering a service available anywhere and at any time (Minerva et al. 2015). The IETF considers the IoT to be a network of interconnected objects that can be addressed uniquely and that use standardized protocols for communication between the objects (Lee et al. 2012). Further, the IETF and ISO/IEC take different requirements into consideration in the IoT environment, such as auto-configuration, unique identification, interfaces standardization, connectivity, reliability and mobility.
We propose a definition that brings together information from all the definitions discussed above: the IoT is a global infrastructure that interconnects objects (which are identified uniquely) and humans to offer advanced, autonomous services via smart interfaces. It must be noted that the unique identification allows the identity of the objects to be verified and enables data processing based on the source of the data.
Standardization bodies and research projects have introduced various architectures for the IoT. In the following sections, we describe two examples of proposed architectures for the IoT.
The ITU-T proposed a reference model for the IoT that is based on multiple layers (ITU-T 2012). This reference model (see Figure 1.1) defines four horizontal layers (application, support, network and devices) and two vertical layers (management and security). The application layer includes IoT applications and services. Next, the Service Support and Application Support Layer defines the capacities for generic support that are common to all applications, such as data processing and storage. This also includes specific support capacities that respond to the needs of a particular application. The Network Layer offers two services. First there are network capacities, which ensure that connectivity, mobility management, authentication, authorization and accounting functions are all monitored. Second, there are transport capacities that control the routing of data coming from applications or information from the monitoring and management of the environment. The Device Layer defines the capacities of each connected object and device as well as the capacities of the communication gateways.
The vertical layers define the generic management capacities (management of objects, network, traffic and congestion) as well as the security capacities (authorization, authentication, integrity and privacy protection). Further, these vertical layers introduce specific capacities that are dependent on the type of IoT application being considered.
Figure 1.1. The ITU-T architecture of the Internet of Things (ITU-T 2012). For a color version of this figure, see www.iste.co.uk/mbarek/service.zip
The Industrial Internet Consortium is a consortium of several well-known industrial groups in the IT world, such as IBM, HUAWEI and Intel. Through the Industrial Internet Reference Architecture report, this consortium puts forth a system architecture that is applicable to the IoT. This three-tiered architecture is based on three vertical layers or three levels (see Figure 1.2) (Lin et al. 2015).
The Edge Tier corresponds to all the nodes that collect data from proximity networks. This layer makes it possible to implement all control functions. Then comes the Platform Tier, which receives, processes and transmits control commands to the Edge Tier. This layer also enables the processing, analysis and running of operations on data collected from objects, before transmitting them in the opposite direction, toward the Enterprise Tier. The Enterprise Tier takes decisions and carries out the role of an interface with the end-user. It thus includes applications that allow control commands to be generated and to be sent to the Platform Tier. The different layers in this architecture are interconnected via access networks and service networks.
Figure 1.2. Architecture of the Internet of Things (Lin et al. 2015). For a color version of this figure, see www.iste.co.uk/mbarek/service.zip
The IoT improves the quality of life in different areas of daily life. Examples include the field of health, smart cities, vehicular networks and so on. The ISO/IEC focuses on the standardization of the underlying technology used in different fields of application of the IoT. Working Group 9 under Technical Committee 1 (JTC 1/WG9) of ISO/IEC studies the normalization of Big Data technologies in IoT domains (International Electrotechnical Commission 2017). Further, various providers offer solutions for IoT service offerings and implementation in different fields of application. For example, the Kaa1 project offers a range of features allowing us to create advanced applications for smart devices, to flexibly manage ecosystems and their peripherals, to orchestrate end-to-end data processing, etc.
An aging population requires monitoring of old people through a decentralized healthcare system based on a set of connected sensors. Each patient possesses a surveillance system that allows them to be monitored and surveilled without the need of visiting the medical center. The medical data collected in this way improve healthcare by customizing treatments...
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