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Sensor networks simulation framework for target tracking applications: SN-SiFTTA

Sensor networks simulation framework for target tracking applications: SN-SiFTTA This work presents a new simulation framework for wireless sensor networks dedicated for target tracking applications named: SN-SiFTTA. SiFTTA does not address routing protocols as they have been studied extensively in the literature. Two scheduling finite state machine models are proposed to control the behaviour of the sensor nodes and cluster heads. Four different deployment methods are proposed and investigated using four performance metrics: target tracking accuracy (TTA), energy efficiency index (EEI), quality of clustering index (QCI) and deployment feasibility. A simulator with friendly and interactive graphical user interface was developed using Matlab integrated development environment. The simulator provides rich environment that allows the user to customized wide varieties of scenarios. Among these, we focus on deployment method effects on the aforementioned four performance metrics. Four hundred experiments were conducted 100 per each deployment method of global random, local random, grid random and uniform grid. After analysing the experimental results, the study suggested a weighted-criteria formula to select the best deployment method considering all performance metrics to avoid bias and to ensure problem context relevance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Engineering and Technology Inderscience Publishers

Sensor networks simulation framework for target tracking applications: SN-SiFTTA

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1476-1289
eISSN
1741-9212
DOI
10.1504/IJWET.2021.117767
Publisher site
See Article on Publisher Site

Abstract

This work presents a new simulation framework for wireless sensor networks dedicated for target tracking applications named: SN-SiFTTA. SiFTTA does not address routing protocols as they have been studied extensively in the literature. Two scheduling finite state machine models are proposed to control the behaviour of the sensor nodes and cluster heads. Four different deployment methods are proposed and investigated using four performance metrics: target tracking accuracy (TTA), energy efficiency index (EEI), quality of clustering index (QCI) and deployment feasibility. A simulator with friendly and interactive graphical user interface was developed using Matlab integrated development environment. The simulator provides rich environment that allows the user to customized wide varieties of scenarios. Among these, we focus on deployment method effects on the aforementioned four performance metrics. Four hundred experiments were conducted 100 per each deployment method of global random, local random, grid random and uniform grid. After analysing the experimental results, the study suggested a weighted-criteria formula to select the best deployment method considering all performance metrics to avoid bias and to ensure problem context relevance.

Journal

International Journal of Web Engineering and Technology – Inderscience Publishers

Published: Jan 1, 2021

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