Abstract

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. the objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this paper for image segmentation. in addition, PSO and BFA are presented for distributed localization of the deployed nodes. Image segmentation for autonomous deployment and distributed localization are formulated as multidimensional optimization problems, and PSO and BFA are used as optimization tools. Comparisons of the results of PSO and BFA for autonomous deployment and distributed localization are presented. Simulation results show that both the algorithms perform multilevel image segmentation faster than the exhaustive search for optimal thresholds. Besides, PSO-Based localization is observed to be faster, and BFA-Based localization is more accurate. © 2006 IEEE.

Department(s)

Electrical and Computer Engineering

Comments

National Science Foundation, Grant ECCS 0529292

Keywords and Phrases

Bacterial foraging algorithm (BFA); image thresholding; node localization; particle swarm optimization (PSO); wireless sensor networks (WSNs)

International Standard Serial Number (ISSN)

1094-6977

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Nov 2010

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