Static wireless sensor networks deployment using an improved binary PSO

• Published in: International journal of communication systems Vol. 29; no. 5; pp. 1026 - 1041
• Main Authors: Senouci, Mustapha R., Bouguettouche, Daoud, Souilah, Farouk, Mellouk, Abdelhamid
• Format: Journal Article
• Language: English
• Published: Chichester Blackwell Publishing Ltd 25.03.2016; Wiley Subscription Services, Inc; Wiley
• Subjects: Algorithms; Computer Science; Convergence; coverage; deterministic deployment; Networking and Internet Architecture; particle swarm optimization; Remote sensors; sensor placement; Sensors; State of the art; Strategy; Swarm intelligence; Wireless networks; wireless sensor networks; coverage; wireless sensor networks; particle swarm optimization; sensor placement; deterministic deployment
• ISSN: 1074-5351; 1099-1131
• DOI: 10.1002/dac.3040

Summary A major issue in designing wireless sensor networks is the deployment problem. Indeed, many performances of the sensor network, such as coverage, are determined by the number and locations of deployed sensors. This paper reviews existing deterministic deployment strategies and devises a modified binary particle swarm optimization, which adopts a new position updating procedure for a faster convergence and exploits the abandonment concept to avoid some drawbacks such as premature convergence. The devised approach combines, in a meaningful way, the characteristics of the binary particle swarm optimization with the wireless sensor networks deployment requirements in order to devise a lightweight and efficient sensor placement algorithm. The effectiveness and efficiency of the proposed approach are evaluated through extensive simulations. The obtained results show that the proposed algorithm outperforms the state‐of‐the‐art approaches, especially in the case of preferential coverage. Copyright © 2015 John Wiley & Sons, Ltd. In this paper, we devised a polynomial‐time sensor placement algorithm that computes the minimum number of sensors along with their locations to meet the desired user detection requirements. Obtained results showed that the proposed deployment algorithm outperforms the state‐of‐the‐art deployment strategies.