CATEGORISATION OF LOCALISATION ALGORITHMS

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THESIS STATEMENT

In the light the above, the hypothesis of this study is that a localisation algorithm can rely on using a low number of references to achieve an accurate estimation without compromising the simplicity, security, robustness or the energy efficiency of the algorithm. Using all of the available references could enhance the accuracy of position estimation. However, following this approach in WSNs with limited resources could result in several constraints and problems, as mentioned earlier. An efficient localisation algorithm for WSNs will be designed, which relies on proper selection criteria for references in order to enable sensor nodes to estimate their position with good accuracy but using a low number of references.
Designing a proper method to select the best subset of references to contribute to high accuracy is a challenge. However, using this subset of references would not only overcome the problems associated with using all of the available references, but would also help to achieve several design objectives. A subset of references makes the localisation algorithm tolerant of failures of nodes and so enhances its robustness. Reducing the number of references used will dramatically reduce the computation and communication overheads, which will improve the energy efficiency of the algorithm. excluding malicious nodes from the selected subset of references. The selection criteria may be defined in a manner that will fulfil the three required conditions of the “localised position discovery algorithm”, which will be mentioned in the next section.

SUMMARY
OPSOMMING
ACKNOWLEDGEMENTS
LIST OF ABBREVIATIONS
INTRODUCTION 1
1.1 PROBLEM STATEMENT
1.2 THESIS STATEMENT
1.3 RESEARCH OBJECTIVES
1.4 CHAPTER OVERVIEWS
2 BACKGROUND
2.1 CATEGORISATION OF LOCALISATION ALGORITHMS
2.1.1 Pre-configured coordinates
2.1.2 Location propagation of nodes
2.1.3 Granularity of information
2.1.4 Computational distribution
2.1.5 Number of estimations
2.1.6 The set of references used
2.2 LOCALISATION SYSTEMS
2.2.1 Components of localisation systems
2.2.2 Multilateration method
2.2.3 Assumptions and variables
2.2.4 Localisation errors
2.3 APPROACHES TO SELECTING A SUBSET OF REFERENCES
2.3.1 Nearest references
2.3.2 Low-error references
2.3.3 Malicious node removal
2.3.4 Consistency of references
2.3.5 Impact of geometry
2.3.6 Noisy distance estimate
2.4 COMPARISON OF THE ANALYSED APPROACHES
2.5 CHAPTER CONCLUSIONS
3 ACCURATE LOCALISATION SYSTEM
3.1 ALWADHA ALGORITHM
3.1.1 Initialisation
3.1.2 Initial position estimation
3.1.3 Refined position estimation
3.1.4 Position update
3.2 IMPLEMENTATION
3.2.1 Network simulator (ns-2) overview
3.2.2 The extended ns-2
3.2.3 Class hierarchy
3.2.4 The structure of the new ns-2
3.2.5 Guidelines for running the simulation
3.2.6 Manipulate output file
3.3 SIMULATION
3.3.1 Localisation algorithms..
3.3.2 Performance comparison..
3.3.3 Setup and environment.
3.3.4 Results and comparisons
3.4 CHAPTER CONCLUSIONS
INFORMATION FUSION PROPERTIES
4 LOCALISATION SYSTEM
4.1 INFORMATION FUSION
4.1.1 Information fusion and localisation systems
4.2 INFORMATION-FUSION TECHNIQUES FOR LOCATION DISCOVER
4.3 LOCALISED INFORMATION-FUSION ALGORITHMS
4.4 THE THREE FILTERS OF ALWADHA
4.4.1 Filter one
4.4.2 Filter two
4.4.3 Filter three
4.5 INFORMATION FUSION IN A LEADING ROLE
4.6 SIMULATION
4.6.1 Localisation error vs number of iterations
4.6.2 Number of “location request” packet
4.6.3 Number of “location response” packets
4.6.4 Remaining energy
4.6.5 Performance comparison
4.7 TOWARDS MORE ENERGY EFFICIENCY
4.7.1 Single-estimation approach
4.7.2 Dynamic power control
4.7.3 Incremental and exponential requesting rate
4.7.4 Performance comparison
4.8 CHAPTER CONCLUSION
5 SECURE LOCALISATION SYSTEMS
5.1 THE SECURITY OF LOCALISATION SYSTEMS
5.1.1 Attacks on localisation systems
5.1.2 Secure localisation algorithms.
5.2 SECURE DISTANCE ESTIMATION.
5.2.1 Distance bounding as a possible solution for ALWadHA.
5.3 DISTANCE-BOUNDING PROTOCOLS
5.3.1 Distance-bounding attacks
5.3.2 Types of distance-bounding protocol.
5.3.3 Principles of secure distance bounding
5.4 COMPARISON FRAMEWORK
5.5 SELECTED DISTANCE-BOUNDING PROTOCOLS
5.5.1 Brands and Chaum’s distance-bounding protocol
5.5.2 Bussard and Bagga’s distance-bounding protoc
5.5.3 Čapkun et al.’s distance-bounding protocol
5.5.4 Hancke and Kuhn’s distance-bounding protocol
5.5.5 Reid et al.’s distance-bounding protocol
5.5.6 Tu and Piramuthu’s distance-bounding protocol
5.5.7 Munilla and Peinado’s distance-bounding protocol
5.5.8 Kim and Avoine’s distance-bounding protocol
5.5.9 Kim et al.’s distance-bounding protocol
5.5.10 Meadows et al.’s distance-bounding protocol
5.5.11 Avoine et al.’s technique.
5.5.12 Avoine and Tchamkerten’s distance-bounding protocol.
5.5.13 Trujillo-Rasua et al.’s distance-bounding protocol.
5.5.14 Peris-Lopez et al.’s distance-bounding protocol
5.6 COMPARISON OF DISTANCE-BOUNDING PROTOCOLS.
5.6.1 Security.
5.6.2 Memory and transmitted data
5.6.3 Computation
5.6.4 Choosing a suitable protocol.
5.7 ATTACK RESISTANCE OF ALWADHA ALGORITHM
5.8 SIMULATION RESULTS.
5.8.1 Dishonest reference nodes.
5.8.2 Compromised beacon nodes.
5.9 CHAPTER CONCLUSIONS
6.1 CONCLUSIONS
6.2 SUMMARY OF CONTRIBUTIONS
6.3 FUTURE WORK
References