Оглавление
Nader Mbarek. Service Level Management in Emerging Environments
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Service Level Management in Emerging Environments
Preface
1. Service Level Management in the Internet of Things (IoT)
1.1. Introduction
1.2. IoT: definitions
1.3. IoT: an overview. 1.3.1. IoT architectures
1.3.1.1. The ITU-T reference model
1.3.1.2. The IIC architecture
1.3.2. Application fields of the IoT
1.3.2.1. E-health
1.3.2.2. Smart cities
1.3.2.3. Vehicular networks
1.4. Security management and privacy protection in the IoT. 1.4.1. Motivations and challenges
1.4.2. Security services in the IoT environment
1.4.2.1. Identification and authentication in the IoT. 1.4.2.1.1. Definition
1.4.2.1.2. Research projects
1.4.2.2. Access control in the IoT. 1.4.2.2.1. Definition
1.4.2.2.2. Research projects
1.4.2.3. Confidentiality in the IoT. 1.4.2.3.1. Definition
1.4.2.3.2. Research projects
1.4.2.4. Integrity in the IoT. 1.4.2.4.1. Definition
1.4.2.4.2. Research projects
1.4.2.5. Non-repudiation in the IoT. 1.4.2.5.1. Definition
1.4.2.5.2. Research projects
1.4.2.6. Availability in the IoT. 1.4.2.6.1. Definition
1.4.2.6.2. Research projects
1.4.3. Privacy protection and trust in the IoT. 1.4.3.1. Privacy
1.4.3.2. Trust
1.4.3.3. Regulations
1.4.3.4. Research projects
1.5. QoS management for IoT services. 1.5.1. Motivations and challenges
1.5.2. Guaranteeing QoS in IoT. 1.5.2.1. IoT device layer. 1.5.2.1.1. QoS requirements
1.5.2.1.2. Research projects
1.5.2.2. IoT network layer. 1.5.2.2.1. QoS requirements
1.5.2.2.2. Research projects
1.5.2.3. The support and application layer in the IoT. 1.5.2.3.1. QoS requirements
1.5.2.3.2. Research projects
1.5.2.4. Cross-layer approach in IoT. 1.5.2.4.1. QoS requirements
1.5.2.4.2. Research projects
1.6. QBAIoT: QoS-based access method for IoT environments
1.6.1. Service level guarantee in the IoT. 1.6.1.1. QoS-based architecture for the IoT
1.6.1.2. Service level agreement in the IoT
1.6.2. The QBAIoT process in the IoT
1.6.2.1. QBAIoT gateways
1.6.2.2. QBAIoT objects
1.6.3. QBAIoT performance evaluation. 1.6.3.1. Simulation environment
1.6.3.2. Results
1.7. Conclusion
1.8. References
2. Service Level Management in the Cloud
2.1. Introduction
2.2. The Cloud environment. 2.2.1. Cloud Computing. 2.2.1.1. Definition
2.2.1.2. Characteristics
2.2.2. Cloud Networking. 2.2.2.1. Definition
2.2.2.2. Characteristics
2.2.3. Inter-Cloud. 2.2.3.1. Definition
2.2.3.2. Characteristics
2.3. Service level and self-management in the Cloud
2.3.1. Quality of Service in a Cloud environment. 2.3.1.1. Definition and characteristics
2.3.1.2. Research work and standardization
2.3.2. Security in a Cloud environment. 2.3.2.1. Definition and characteristics
2.3.2.2. Research work and standardization
2.3.3. Self-management of Cloud environments. 2.3.3.1. Definition and characteristics
2.3.3.2. Research work and standardization
2.4. QoS guarantee in Cloud Networking. 2.4.1. Cloud Networking architectures
2.4.1.1. Inter-Cloud broker-type Cloud Networking architecture
2.4.1.2. Federated Cloud Networking architecture
2.4.2. Performance evaluation
2.4.2.1. Simulation environment
2.4.2.2. Simulation scenarios
2.4.2.3. Results
2.5. Conclusion
2.6. References
3. Managing Energy Demand as a Service in a Smart Grid Environment
3.1. Introduction
3.2. The Smart Grid environment
3.2.1. Smart microgrids
3.2.2. Information and communication infrastructure
3.3. Demand management: fundamental concepts
3.3.1. Predicting loads
3.3.2. DR – demand response
3.4. Demand-side management
3.4.1. The architectures and components of DSM platforms
3.4.2. Classifying DSM approaches
3.4.3. Deterministic approaches for individual users
3.4.4. Stochastic approaches for individual users
3.4.5. Deterministic approaches for consumer communities
3.4.6. Stochastic approaches for consumer communities
3.5. Techniques and methods for demand scheduling
3.5.1. Game theory
3.5.2. Multiagent systems
3.5.3. Machine learning
3.6. Conclusion
3.7. References
4. Managing Quality of Service and Security in an e-Health Environment
4.1. Introduction
4.2. e-health systems
4.2.1. Architecture
4.2.2. Characteristics
4.2.2.1. Services offered
4.2.2.2. Tools and technologies
4.2.2.3. Specific criteria
4.3. QoS in e-health systems
4.3.1. e-health services and QoS
4.3.1.1. Importance of QoS for e-health services
4.3.1.2. QoS needs of e-health services
4.3.1.2.1. Multimedia conferencing
4.3.1.2.2. Transfer/streaming of medical images
4.3.1.2.3. Transmission of a patient’s vital signs
4.3.1.2.4. Access to the electronic health record
4.3.2. QoS management in e-health systems
4.3.2.1. Mobile network-based e-health systems
4.3.2.2. New Generation Network-based e-health systems
4.3.2.3. e-health systems integrating new technologies
4.4. Security of e-health systems
4.4.1. Threats and attacks specific to e-health systems
4.4.1.1. Threats and attacks specific to e-health systems
4.4.1.2. Security needs of e-health systems
4.4.1.2.1. Privacy and security of data access
4.4.1.2.2. Security of communications
4.4.1.2.3. Security of storage
4.4.2. Security management in e-health systems
4.5. Conclusion
4.6. References
5. Quality of Service Management in Wireless Mesh Networks
5.1. Introduction
5.2. WMNs: an overview. 5.2.1. Definition of a WMN
5.2.2. Architecture of a radio mesh wireless network
5.2.3. Characteristics of a WMN environment
5.2.4. Standards for WMNs. 5.2.4.1. IEEE 802.11s technology
5.2.4.2. IEEE 802.16j technology
5.2.5. Domains of applications
5.3. QoS in WMNs
5.3.1. QoS in networks
5.3.2. QoS constraints in WMNs
5.3.3. QoS mechanisms in WMNs
5.3.3.1. QoS in the IEEE 802.11s standard
5.3.3.2. QoS in the IEEE 802.16j standard
5.3.3.3. Other QoS mechanisms in WMNs
5.3.4.1. The CARMNET project
5.3.4.2. The EU-MESH project
5.3.4.3. The SMART-NET project
5.3.4.4. The WING project
5.4. QoS-based routing for WMNs
5.4.1. Routing requirements in WMNs
5.4.2. Routing metrics in WMNs
5.4.2.1. The hop count metric
5.4.2.2. The expected transmission count metric
5.4.2.3. The expected transmission time metric
5.4.2.4. The airtime link metric
5.4.3. QoS-based routing protocols in WMNs
5.4.3.1. The wireless mesh routing protocol
5.4.3.2. The QUORUM protocol
5.4.3.3. The interference and delay aware routing protocol
5.4.3.4. The MARIA protocol
5.4.3.5. Other protocols
5.5. HQMR: QoS-based hybrid routing protocol for mesh radio networks
5.5.1. Description of the HQMR protocol. 5.5.1.1. General architecture of the framework
5.5.1.2. Design principle for the HQMR protocol
5.5.1.3. QoS guarantee with HQMR
5.5.2. How the HQMR protocol works. 5.5.2.1. IMRR: reactive QoS-based subprotocol
5.5.2.2. IMPR: QoS-based proactive subprotocol
5.5.3. Validation of the HQMR protocol
5.5.3.1. Simulation environment
5.5.3.2. Evaluating the IMRR subprotocol
5.5.3.3. Evaluating the IMPR subprotocol
5.6. Conclusion
5.7. References
6. Blockchain Based Authentication and Trust Management in Decentralized Networks
6.1. Introduction
6.1.1. Challenges and motivations, the state of the art
6.1.1.1. Identification and authentication
6.1.1.2. Reputation and trust
6.1.2. Blockchain, a support for authentication and trust
6.2. The Blockchain Authentication and Trust Module (BATM) architecture. 6.2.1. Context and development
6.2.2. Managing identities and authentication
6.2.2.1. Registering and managing keys and authentication
6.2.3. Calculating trust and reputation using the MLTE algorithm
6.2.3.1. Notations
6.2.3.2. The general functioning of the MLTE algorithm
6.2.3.3. How the MLTE functions: a detailed discussion
6.2.3.4. Selecting the provider based on trust
6.2.3.5. Integrating MLTE in BATM using smart contracts
6.3. Evaluating BATM
6.3.1. Simulation plan
6.3.2. Results and interpretation
6.3.2.1. Evaluating MLTE in ideal conditions
6.3.2.2. Behavior of the MLTE in the event of node failure
6.4. Conclusion
6.5. References
7. How Machine Learning Can Help Resolve Mobility Constraints in D2D Communications
7.1. Introduction
7.2. D2D communication and the evolution of networks
7.2.1. The discovery phase in D2D communications
7.2.1.1. The direct approach
7.2.1.2. The centralized approach
7.2.2. The data exchange phase in D2D communications
7.2.3. Investigations into future mobile networks
7.3. The context for machine learning and deep learning
7.3.1. Overview of deep learning and its application
7.3.2. Types of machine learning
7.3.3. Linear regression and classification
7.3.3.1. Linear classification with SVM
7.3.3.2. Support Vector Regression
7.4. Dynamic discovery
7.4.1. Real-time prediction of user density
7.4.2. The dynamic discovery algorithm
7.5. Experimental results
7.5.1. General hypotheses
7.5.2. Traffic with low user density
7.5.3. Traffic with high user density
7.6. Conclusion
7.7. References
8. The Impact of Cognitive Radio on Green Networking: The Learning-through-reinforcement Approach
8.1. Introduction
8.2. Green networking. 8.2.1. Why should we reduce energy consumption?
8.2.2. Where can we reduce energy consumption?
8.2.3. Definition and objectives of green networking
8.3. Green strategies
8.3.1. Consolidation of resources
8.3.2. Selective connectivity
8.3.3. Virtualization
8.3.4. Energy-proportional computing
8.4. Green wireless networks
8.4.1. Energy efficiency in wireless networks
8.4.2. Controlling transmission power
8.5. How CR contributes to green networking
8.5.1. The principle behind CR
8.5.2. The cognition cycle
8.5.2.1. Observation phase (detection and perception)
8.5.2.2. Orientation phase
8.5.2.3. Planning phase
8.5.2.4. Decision-making phase
8.5.2.5. Action phase
8.5.2.6. Learning phase
8.5.3. Green networking in CR networks
8.6. Learning through reinforcement by taking into account energy efficiency during opportunistic access to the spectrum
8.6.1. Formulating the problem
8.6.2. Comparison between CR and Q_learning enabled CR
8.7. Conclusion
8.8. References
List of Authors
Index
A
B, C
D
E, G
H, I
L, M
N
O, P
Q, R
S, T
U, W
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