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Using genetic algorithms to optimise Wireless Sensor Network design

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thesis
posted on 08.01.2020, 10:17 by Jin Fan
Wireless Sensor Networks(WSNs) have gained a lot of attention because of their potential to immerse deeper into people' lives. The applications of WSNs range from small home environment networks to large habitat monitoring. These highly diverse scenarios impose different requirements on WSNs and lead to distinct design and implementation decisions. This thesis presents an optimization framework for WSN design which selects a proper set of protocols and number of nodes before a practical network deployment. A Genetic Algorithm(GA)-based Sensor Network Design Tool(SNDT) is proposed in this work for wireless sensor network design in terms of performance, considering application-specific requirements, deployment constrains and energy characteristics. SNDT relies on offine simulation analysis to help resolve design decisions. A GA is used as the optimization tool of the proposed system and an appropriate fitness function is derived to incorporate many aspects of network performance. The configuration attributes optimized by SNDT comprise the communication protocol selection and the number of nodes deployed in a fixed area. Three specific cases : a periodic-measuring application, an event detection type of application and a tracking-based application are considered to demonstrate and assess how the proposed framework performs. Considering the initial requirements of each case, the solutions provided by SNDT were proven to be favourable in terms of energy consumption, end-to-end delay and loss. The user-defined application requirements were successfully achieved.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Publisher

Loughborough University

Rights holder

© Jin Fan

Publication date

2009

Notes

A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.

EThOS Persistent ID

uk.bl.ethos.520401

Language

en

Supervisor(s)

David Parish

Qualification name

PhD

Qualification level

Doctoral