Intelligent Network Management and Control. Badr Benmammar
Artificial Intelligence Application to Cognitive Radio Networks 9.1. Introduction 9.2. Cognitive radio 9.3. Application of AI in CR 9.4. Categorization and use of techniques in CR 9.5. Conclusion 9.6. References 10 Cognitive Radio Contribution to Meeting Vehicular Communication Needs of Autonomous Vehicles 10.1. Introduction 10.2. Autonomous vehicles 10.3. Connected vehicle 10.4. Communication architectures 10.5. Contribution of CR to vehicular networks 10.6. SERENA project: self-adaptive selection of radio access technologies using CR 10.7. Conclusion 10.8. References
11 Index
List of Tables
1 Chapter 2Table 2.1. Classification of shared network information (Oktian et al. 2017)Table 2.2. System configuration of SDN networksTable 2.3. The most commonly used machine learning techniques for computer secur...Table 2.4. Construction of rule heading
2 Chapter 4Table 4.1. Weighting methodsTable 4.2. Advantages and drawbacks of multicriteria optimization methodsTable 4.3. Values of simulationsTable 4.4. Input matrixTable 4.5. Weighting vectorsTable 4.6. Ranking for VoIPTable 4.7. Ranking for video serviceTable 4.8. Ranking for best effort serviceTable 4.9. Ranking when one alternative disappears
3 Chapter 7Table 7.1. Comparative table of works
4 Chapter 8Table 8.1. Comparison of various proposed architectures
5 Chapter 10Table 10.1. Various SAE automation levels (source: 2014 SAE International)Table 10.2. Constraints expressed for road safety vehicular applicationsTable 10.3. Constraints expressed for vehicular entertainment applicationsTable 10.4. ITS-G5 systemTable 10.5. Table of ITS-G5 frequency allocation in Europe (IEEE 2010)
List of Illustrations
1 Chapter 1Figure 1.1. Progress in image recognition (benchmark ImageNet), “Electronic Fron...Figure 1.2. Organizations and countries relying on artificial intelligence to id...
2 Chapter 2Figure 2.1. Simplified SDN architecture (Zhang et al. 2017)Figure 2.2. Control plane distribution modelsFigure 2.3. Architecture of IPSec tunnel service deployment in an SDN-based netw...Figure 2.4. Tunnel deployment processFigure 2.5. Logical topology of the test bedFigure 2.6. Physical topology of the test bedFigure 2.7. (A) Performance of the deployment of IPSec security service on SDN. ...Figure 2.7. (B) Performance of the deployment of IPSec security service on SDN. ...Figure 2.8. Architecture of signature learning serviceFigure 2.9. Architecture of a Learning Node (LN)Figure 2.10. Architecture of an intelligent SDN with IDSFigure 2.11. Interactions between components of data architecture
3 Chapter 3Figure 3.1. Analogy square of ESFigure 3.2. Artificial neural network (Decourt 2018)
4 Chapter 4Figure 4.1. A heterogeneous environment (Bendaoud 2018)Figure 4.2. Heterogeneous environment (Bendaoud 2018)Figure 4.3. Network selection process (Bendaoud et al. 2018b)Figure 4.4. Network selection processFigure 4.5. Delay and lost packets comparison for N(0) and N(4). For a color ver...Figure 4.6. Throughput and delay comparison for N(5) and N(4). For a color versi...Figure 4.7. Comparison between N(2) and N(4). For a color version of this figure...Figure 4.8. Comparison between N(2) and N(1). For a color version of this figure...
5 Chapter 5Figure 5.1. Public deployment of cloud computing. For a color version of this fi...Figure 5.2. Private deployment of cloud computing. For a color version of this f...Figure 5.3. Community deployment of cloud computing. For a color version of this...Figure 5.4. Distribution of customer/provider management in a cloud computing en...
6 Chapter 6Figure 6.1. Conversion of an application code into a weighted relation graph (Mo...Figure 6.2. Stages of offloading decisionFigure 6.3. Star graphFigure 6.4. Generic architecture of MCC (Gupta and Gupta 2012)Figure 6.5. B&B treeFigure 6.6. Chromosome
7 Chapter 7Figure 7.1. Smart grid network architecture2. For a color version of this figure...Figure 7.2. Generation, transportation and distribution systems in the smart gri...Figure 7.3. Interaction of data centers with the smart grid and its usersFigure 7.4. Fog–cloud computing systemFigure 7.5. Example of smart micro-grid-cloud architecture. For a color version ...
8 Chapter 8Figure 8.1. Presentation of C-ITS architectureFigure 8.2. Presentation of IoV architectures described in the literatureFigure 8.3. Our proposal
9 Chapter 9Figure 9.1. Learning process in cognitive radio networks (Abbas et al. 2015)Figure 9.2. V-shaped formation of Anser flight (Bestaoui 2015)
10 Chapter 10Figure 10.1. Autonomous vehicle (Hubaux 2005)Figure 10.2. Illustration of C-ITS systems (ETSI 2010)Figure 10.3. Reference architecture of an ITS station (ETSI 2010)Figure 10.4. Cognition loopFigure 10.5. The main problems related to CR in the literature (Singh et al. 201...Figure 10.6. Example of cognitive loop of the SERENA project
Guide
1 Cover
8 Index
Pages
1 Page v
2 Page iii