This thesis is concerned with the design and analysis of new Bayesian network based mobility algorithms for mobile Wireless Sensor Networks (WSNs). The hypothesis for the work presented herein is that incorporating Artificial Intelligence (Al) at the level of the sensor nodes will improve their performance (coverage, connectivity and lifetime) and result in fault tolerance capabilities, in the face of uncertainty associated with incomplete information regarding the network. Two types of mobility strategy are presented and investigated. Firstly, a new gazing mobility strategy is presented which is biologically inspired from herbivores grazing pastures. As part of the latter strategy, and instead of deploying a large number of static sensor nodes to cover a region of interest, a smaller number of mobile nodes are deployed which migrate around the region to achieve coverage over time. To enable the performance evaluation of this strategy a new coverage measure called Coverage Against Time was created. A new decentralised Bayesian network based grazing mobility algorithm called BNGRAZ is presented which uses evidence derived from neighbouring nodes to predict the probability of performance (coverage and connectivity) changes associated with moving in a particular direction. Evidence is also obtained from a new Coverage Approximation (CA) algorithm which enables each sensor node to approximate the WSN coverage in order to determine areas in need of servicing. The performance of BNGRAZ is compared to a fixed path mobility technique, Random Waypoint (RWP) mobility model, and a new Grazing Reference Point Group Mobility (GRPGM) algorithm developed as part of this work. Secondly, a self-healing strategy which physically relocates sensor nodes to repair coverage holes, due to the failure of sensor nodes, is presented. A new decentralised Bayesian network based mobility algorithm called BayesMob, which uses local neighbour information, was created to coordinate the self-healing motion. The algorithm enables sensor nodes to predict the probability of an increase in coverage given a move in a particular direction to repair coverage holes. In addition, the thesis outlines the development of a WSN simulator. The latter provides a tool for evaluating the performance of mobile WSNs. All mobility strategies and algorithms discussed herein were simulated using the new WSN simulator.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:500331 |
| Date | January 2009 |
| Creators | Coles, Matthew David |
| Contributors | Azzi, Djamel ; Haynes, Barry P. ; Sanders, David Adrian |
| Publisher | University of Portsmouth |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://researchportal.port.ac.uk/portal/en/theses/bayesian-network-based-intelligent-mobility-strategies-for-wireless-sensor-networks(23e8243c-d165-40c5-8838-7e8feaa8d965).html |
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