Contents
Contributors
Preface

Networks

1. WSN Routing Improvement Techniques

1.1. Introduction
1.2. Architecture of a Wireless Sensor Node
1.2.1. The Sensing Unit
1.2.2. The Processing Unit
1.2.3. The Wireless Transceiver Unit
1.2.4. The Power Unit
1.3. The Offer of Sensor Manufacturers
1.3.1. The Tmote Sky of Moteiv
1.3.2. Sun SPOT from Sun Microsystems
1.3.3. The WiEye of EasySen
1.3.4. The Micaz Mote of Crossbow
1.3.5. Jennic Sensors
1.3.6. Other Commercial Applications
1.4. Wireless Sensor Network Architecture
1.4.1. Wireless Sensor Network Architecture
1.4.2. Energy Consumption Factors in WSNs
1.4.2.1. Collisions
1.4.2.2. Idle Listening
1.4.2.3. Overhearing
1.4.2.4. Overmitting
1.4.2.5. Control Packets Overhead
1.4.2.6. Network Protocols
1.4.2.7. Attacks and Security Issues
1.4.2.8. Cryptography and Security Solutions
1.5. Application Domains of Sensor Networks
1.5.1. Military Applications
1.5.2. Security and Safety Related Applications
1.5.3. Medical Applications
1.5.4. Environmental Applications
1.5.5. Commercial Applications
1.5.6. Cold Chain Monitoring System
1.5.6.1. Issues and Challenges
1.5.6.2. Needs and Operating Principles

1.6. Routing Mechanisms for WSN Cold Chain Monitoring System
1.6.1. Introduction
1.6.2. Backgrounds and Related Works
1.6.3. Routes Selection Criteria
1.6.3.1. Remaining Energy Level
1.6.3.2. Sensor Proximity with Respect to the Base Station (Proximity-BS)
1.6.3.3. Degree of Connectivity
1.6.3.4. LQI: Link Quality Indicator
1.6.3.5. Composite or Hybrid Metric
1.6.4. Routing Mechanisms
1.6.4.1. Simple Routing
1.6.4.2. Round Robin Routing
1.6.4.3. Weighted Round-Robin Routing (W2R routing)
1.7. L2RP: The Link Reliability Based Routing Protocol
1.8. Routing Protocol Performance Criteria
1.8.1. Average Ratio of the Remaining Energy
1.8.2. Average Path Lengths
1.8.3. LIF: Load Imbalance Factor
1.8.4. Network Lifetime
1.8.5. Average Percentage of Lost Packets
1.9. L2RP Routing Protocol Performance Evaluation Model
1.9.1. Energy Consumption Model
1.9.2. Network Deployment and Performance Evaluation Parameters
1.9.3. LQI Model for Performance Evaluation Purposes
1.10. L2RP Routing Protocol Performance Evaluation
1.10.1. Average Path Length
1.10.2. LIF: Load Imbalance Factor
1.10.3. Average Percentage of Packet Losses
1.10.4. Composite or Hybrid Metric
1.10.5. Average Network Lifetime
1.10.6. Average Ratio of the Remaining Energy
1.10.7. Impacts of Increasing the Number of Achtophorous Nodes
1.10.8. Impacts of the Unreliability of Wireless Links
1.11. Energy Over-Consumption Induced by Securing Routing Operations
1.11.1. Introduction and Backgrounds
1.11.2. Energy Over-Consumption Induced by Adding an Integrity Key to L2RP
1.12. Conclusions
Acknowledgements
References

2. Connectivity Recovery and Augmentation in Wireless Ad Hoc Networks

2.1. Introduction
2.2. Articulation Nodes and Bridges
2.2.1. Definitions and Terms
2.2.2. Location of Articulation and Bridges
2.2.3. Locating Articulations and Bridges in Wireless Ad Hoc Networks
2.3. Cooperative Communication
2.4. Connectivity Recovery
2.5. Connectivity Augmentation
2.6. Conclusion
References

3. QoS Routing in Ad Hoc Network

3.1. Introduction
3.2. Routing in Ad Hoc Network
3.3. Usual Metrics
3.3.1. Hop-Count Metric
3.3.2. Delay-Based Metrics
3.3.3. ETX Metric
3.3.4. BER-Based Metric
3.3.5. Retransmission-Based Metric
3.4. Taking Into Account the Quality of Links in the Choice of Route
3.5. Conclusion
Acknowledgements
References

4. A Knowledge-Based Modeling and Simulation Approach for the Management of Sensor Networks

4.1. Introduction
4.2. Related Work
4.2.1. Modelling and Simulation Tools for Sensor Networks
4.2.2. Environment Representation
4.2.3. Spatial Behaviours and Knowledge Management
4.3. Generation of Informed Virtual Geographic Environments
4.3.1. GIS Input Data Selection
4.3.2. Spatial Decomposition
4.3.3. Map Unification
4.3.4. Informed Topologic Graph
4.4. From Semantic Information to Environment Knowledge
4.4.1. Environment Knowledge
4.4.2. Inference Engine
4.4.3. Mapping Knowledge Using Environment Knowledge and Inference Engine
4.5. From Environment Knowledge to Spatial Behaviors
4.5.1. Agent Archetypes
4.5.2. Action Archetypes
4.6. A Knowledge-Based Approach
4.6.1. Agent-Based Simulation Using Informed Virtual Geographic Environments
4.6.2. Spatio-Temporal Knowledge
4.6.2.1. Representation Formalism
4.6.2.2. Knowledge Categories
4.7. Case Study: Simulation of a Sensor Network for Weather Monitoring
4.8. Discussion
4.9. Conclusion and Future Perspectives
Acknowledgements
References
 

Security

5. Reference Monitor-based Security Framework for Trust in Mobile Agent Computing

5.1. Introduction
5.2. Background Study
5.3. Reference Monitor Based Security Framework
5.4. Integration and Evaluation of RM-Agent
5.4.1. Distribution and Integration of RM-Agent into an AP
5.4.2. Verifying Authenticity and Integrity of RM-Agent
5.4.2.1. Authentication and Integrity Checking Protocol (AICP)
5.5. Experiments and Results
5.6. Conclusions and Future Work
References

6. Risk Assessment Considering Configuration of Hybrid Cloud Computing

6.1. Introduction
6.2. Overview of Hybrid Cloud Computing Configuration
6.2.1. Reference Model of Cloud Computing
6.2.2. Hybrid Cloud Computing
6.2.3. Related Work
6.3. Risk Identification and Analysis: Qualitative Risk Analysis of Hybrid Cloud Computing Configuration
6.3.1. Risk Identification: Extraction of Risk Factors in Hybrid Cloud Computing Configuration
6.3.2. Risk Analysis: Qualitative Analysis of Risk Factors in Hybrid Cloud Computing Configuration
6.3.2.1. Risk Analysis Method
6.3.2.2. Risk Analysis Result from Qualitative Viewpoint
6.3.2.3. Summary of Risk Analysis Results
6.4. Risk Evaluation: Quantitative Risk Evaluation of Hybrid Cloud Computing Configuration
6.4.1. Risk Formula
6.4.1.1. Approximation of Asset Value
6.4.1.2. Approximation of Threat Value
6.4.1.3. Approximation of Value of Vulnerability
6.4.2. Calculation of Risk Value
6.4.3. Discussion
6.5. Conclusion and Future Work
Acknowledgements
References

7. The Mathematical Modeling of Road Transport in Context of Critical Infrastructure Protection

7.1. Introduction
7.2. Critical Infrastructure
7.2.1. The Cross-Cutting Criteria
7.2.2. The Sectoral Criteria
7.3. Critical Road Infrastructure
7.4. Mathematical Models of Road Transport
7.4.1. Macroscopic Model
7.4.2. Macroscopic Model
7.5. The Use of Dynamic Modeling of the Impacts of Road Transport
7.6. Conclusion
Acknowledgements
References

8. The Assessment of the Soft Targets

8.1. Introduction
8.2. Methodology of the Solving
8.2.1. Deming’s Circle Methodology
8.2.2. Processes Management
8.2.3. Crisis Escalation
8.2.4. Failure Mode and Effect Analysis
8.3. The Proposed Static Assessment
8.4. The Proposed Dynamic Assesment
8.5. The Proposal of the Preventive Actions
8.6. Conclusions
Acknowledgements
References

Communications

9. Performance Analysis of Quadrature Amplitude Modulation Schemes in Amplify-and-Forward Relay Networks over Rayleigh Fading Channels

9.1. Introduction
9.2. System Model
9.3. ASER Performance Analysis
9.3.1. ASER of General Order RQAM for MRC Scheme
9.3.2. ASER of General Order XQAM for MRC Scheme
9.3.3. ASER of General Order XQAM for BRS Scheme
9.4. Numerical and Simulation Results
9.5. Conclusion
Acknowledgements
References

10. High-Gain Low-Cost Microstrip Antennas and Arrays Based on FR4 Epoxy

10.1. Introduction
10.2. The Studied Methods
10.3. Parameter Study and Performance Comparison
10.4. Design Examples
10.5. Conclusions
References

11. Interference Management and System Optimisation for Femtocells Technology in LTE and Future 4G/5G Networks

11.1. Introduction
11.1.1. Motivation Towards Small Cells
11.1.2. Challenges of Vehicular Environment
11.2. Related Work
11.2.1. LTE Vehicular UEs Penetration Loss
11.2.2. Vehicular UEs Performance
11.2.3. Small Cells Interference
11.3. Mobile Femtocell Technology
11.4. Vehicular UEs Performance Analysis in LTE Networks
11.4.1. System Model
11.4.2. Results and Discussion
11.5. Interference Management for Co-Channel and Co-Tire Femtocells Technology
11.5.1. Coverage Optimisation
11.5.2. Transmission Power Control
11.5.3. The Proposed Interference Management Scheme
11.5.4. The Proposed Scheme (System Performance Analysis)
11.5.5. Results and Discussion
11.6. Conclusions and Further Work
11.6.1. Conclusion
11.6.1.1. Performance Evaluation
11.6.1.2. Interference Management Evaluation
11.6.2. Future Work
References

12. The Buffer Delay Correction Algorithm for VoIP Communication

12.1. Background
12.2. Theatrical Aspects of De-Jitter Buffer
12.3. Buffer Delay Correction Algorithm (BDCA)
12.4. Implementation and Results
12.5. Conclusion
References

13. Opportunistic Max2-Degree Network Coding for Wireless Data Broadcasting

13.1. Introduction
13.1.1. Motivation and Related Work
13.1.1.1. WDNC Based WBC
13.1.1.2. IDNC Based WBC
13.1.2. Contributions
13.2. System Model and Parameters
13.2.1. Mathematical Description
13.2.1.1. Signal Transmission
13.2.1.2. Signal Reception
13.2.2. Assumptions
13.3. The Proposed OM2DNC Based WBC Protocol
13.3.1. OM2DNC Strategy
13.3.2. Joint Network-RSC Decoding
13.4. Performance Analysis
13.5. Simulation Results
13.6. Conclusions
References

14. Analytical Model for Vehicular Mobility: A Microscopic Approach

14.1. Introduction
14.2. Communication Patterns
14.3. Information Dissemination
14.4. Vehicular Mobility Models
14.5. Network Architecture
14.6. Microscopic Analysis
14.6.1. Joint Poisson Spatial Distribution
14.6.1.1. Two-Lane Highway
14.6.1.2. K-Lane Highway
14.6.2. Conditional Probability of Number of Vehicles
14.6.2.1. Two-Lane Highway
14.6.2.2. Three-Lane Highway
14.6.3. Conditional Expected Number of Vehicles
14.6.3.1. Two-Lane Highway
14.6.3.2. Three-Lane Highway
14.6.3.3. K-Lane Highway
14.6.4. Tail Probability of Number of Vehicles
14.6.4.1. Two-Lane Highway
14.6.4.2. Three-Lane Highway
14.7. Conclusions and Future Work
References

15. USRP-based Implementations of Various Scenarios for Spectrum Sensing

15.1. Introduction
15.2. Theoretical Aspects
15.2.1. Energy Detection
15.2.1.1. System Model
15.2.1.2. Conventional Energy Detector
15.2.1.3. Energy Detection with Uncertainty
15.2.2. Eigenvalue Based Detection
15.2.2.1. Maximum-Minimum Eigenvalue (MME) Detection Method
15.2.2.2. Energy with Minimum Eigenvalue (EME) Detection Method
15.2.2.3. Cooperative Spectrum Sensing
15.2.2.4. Soft Data Fusion
15.2.2.5. Hard Decision Fusion
15.3. Experimental Setup
15.3.1. GNU Radio
15.3.2. GNU Radio Blocks
15.3.3. Results
15.3.3.1. Simulation Results
15.3.3.2. Experimental Results for Single Energy Detector
15.3.3.3. Eigenvalue Based Spectrum Sensing
15.3.3.4. Cooperative Spectrum Sensing Results
15.4. Conclusions
Acknowledgements
References

Index