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Algorithms for Spatial Partitioning in Wireless Sensor Network

Algorithms for Spatial Partitioning in Wireless Sensor Network

Kakia Panagidi
Copyright: © 2013 |Pages: 25
ISBN13: 9781466640382|ISBN10: 1466640383|EISBN13: 9781466640399
DOI: 10.4018/978-1-4666-4038-2.ch006
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MLA

Panagidi, Kakia. "Algorithms for Spatial Partitioning in Wireless Sensor Network." Intelligent Technologies and Techniques for Pervasive Computing, edited by Kostas Kolomvatsos, et al., IGI Global, 2013, pp. 109-133. https://doi.org/10.4018/978-1-4666-4038-2.ch006

APA

Panagidi, K. (2013). Algorithms for Spatial Partitioning in Wireless Sensor Network. In K. Kolomvatsos, C. Anagnostopoulos, & S. Hadjiefthymiades (Eds.), Intelligent Technologies and Techniques for Pervasive Computing (pp. 109-133). IGI Global. https://doi.org/10.4018/978-1-4666-4038-2.ch006

Chicago

Panagidi, Kakia. "Algorithms for Spatial Partitioning in Wireless Sensor Network." In Intelligent Technologies and Techniques for Pervasive Computing, edited by Kostas Kolomvatsos, Christos Anagnostopoulos, and Stathes Hadjiefthymiades, 109-133. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-4038-2.ch006

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Abstract

Recent interest in integrated electronic devices (sensors) that operate wirelessly creates a wide range of applications related to national security, surveillance, military, healthcare, and environmental monitoring. Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life. However, a fundamental feature of sensor networks is coverage: how these tiny devices can cover a certain terrain. These devices should be organized in an optimal manner, consuming the minimum energy and covering the whole area of interest. The coverage concept is subject to a wide range of interpretations due to the variety of sensors and applications. Different coverage formulations have been proposed based on the subject to be covered (area in relation to specific items and obstacles), sensor development mechanisms (random versus deterministic), and other properties of wireless sensor networks (e.g. network connectivity and minimum energy consumption). In this chapter, the authors study the coverage problem in wireless sensor networks using the most recent algorithms. The aim of this chapter is to present these algorithms and a comparison between them based on various criteria. The Node Self-Scheduling algorithm, the Centralized Voronoi Tessellation (CVT), the Particle Swarm Optimization Algorithm (PSO), the Virtual Forces Algorithm (VFA), etc. are analyzed. Through the algorithms’ analysis, the interested reader can have a complete view of the proposed solutions related to the coverage problem.

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