Fuzzy Logic and its applications in the networks

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Table of contents

Chapter 1 Introduction
1.1 Context
1.2 Challenges
1.2.1 QoS support
1.2.2 Context-awareness and self-adaptation
1.3 Thesis Contributions
1.3.1 “Maisons Vill’Âge®”: A telehomecare system for elderly [1-5]
1.3.2 QoS support in wireless sensor networks [6-9]
1.3.3 Context-aware adaptive QoS Middleware [10]
1.4 Thesis Outline
Chapter 2 “Maisons Vill’Âge®”: Smart Use of Sensor Networks for Healthy Aging
2.1 Introduction
2.2 System architecture
2.3 Maisons Vill’Âge®
2.4 Discussion
2.5 Conclusion
Chapter 3 FLoR: a Fuzzy logic based Adaptive Clustering and Routing
3.1 Introduction
3.2 Challenges and objectives
3.3 Toward a multi-objective and multi criteria approach
3.4 Fuzzy Logic and its applications in the networks
3.4.1 Clustering
3.4.2 Routing
3.4.3 QoS
3.5 FLoR – Fuzzy Logic based Adaptive Clustering and Routing for Wireless Sensor Networks
3.5.1 Definitions
3.5.2 Mobility management and new definition for mobility
3.5.3 Load Balancing and our definition of load
3.5.4 Unavailability management
3.5.5 Cluster head election
3.6 How does FLoR work?
3.6.1 Different processes in the nodes
3.6.2 Some examples
3.7 Evaluation
3.7.1 Overview of AODV and LEACH
3.7.2 Evaluation metrics – QoS requirements in wireless sensor networks
3.7.3 Simulation environment
3.7.4 Simulation results
3.8 Conclusion
Chapter 4 CodaQ: a Context-aware Adaptive QoS Middleware
4.1 Introduction
4.2 Definitions
4.2.1 Context
4.2.2 Context awareness and self-adapting
4.2.3 QoC – Quality of Context
4.2.4 Middleware
4.2.5 Context ontology
4.3 Challenges in designing context-aware middleware
4.4 Reference model
4.5 Related works
4.6 Discussion
4.7 System architecture
4.8 Data modeling
4.8.1 Raw Event (RE)
4.8.2 VirtualSensor
4.8.3 Deduced State
4.8.4 Zone
4.8.5 Query and Query Reply
4.9 CodaQ – Context-aware Adaptive QoS Middleware
4.9.1 Context Collector
4.9.2 Data management
4.9.3 Context process
4.9.4 System Observer
4.9.5 Context abstraction
4.10 Context-based Adaptive QoS
4.10.1 Embedded QoS
4.10.2 Run-Time State-based QoS
4.10.3 Spatial and Temporal Consistency
4.11 Discussion and concluding remarks
Chapter 5 Implementation
5.1 Introduction
5.2 General view
5.3 XML-based data presentation
5.3.1 Sensor installation and configuration
5.3.2 Raw event
5.3.3 Context information providing – deduced state
5.4 Sub-systems of the prototype
5.4.1 CodaQ Middleware
5.4.2 Application layer
5.4.3 Sensor nodes side
5.5 Evaluation
Chapter 6 Conclusion
6.1 Summary of Contributions
6.1.1 FLoR – Fuzzy Logic based Adaptive Clustering and Routing for Wireless Sensor Networks
6.1.2 CodaQ – a Context-aware Adaptive QoS middleware
6.1.3 Others
6.2 Future work and concluding remarks
Appendix A Fuzzy Logic
Introduction
What is Fuzzy Logic?
Crisp sets
Fuzzy sets and membership functions
Fuzzy sets operators
Linguistic variables
Hedges
If-Then rules
How does it work?
Fuzzy Inference Systems
Appendix B Hardware and Software environment of development
Hardware overview
Imote2 Processor Board
ITS400 Sensor Board
Software overview
Imote2.Builder SDK
Microsoft .NET Micro Framework
References