Quasi–optimal scheduling algorithm for area coverage in multi–functional sensor networks. (18th September 2013)

• Record Type: Journal Article
• Authors: Molnár, Miklós; Simon, Gyula; Gönczy, László
• Is Part Of: International journal of ad hoc and ubiquitous computing. Volume 14:Number 2(2013)
• Page Start: 109
• Page End: 122
• Publication Date: 2013-09-18
• Subjects: multi–functional WSNs -- wireless sensor networks -- multi–functional sensors -- k–coverage problem -- energy conservation -- distributed scheduling -- area coverage -- sensor state scheduling -- fault tolerance -- simulation; Ubiquitous computing -- Periodicals; Embedded computer systems -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Wireless communication systems -- Periodicals; Computer architecture -- Periodicals; 004.2
• Languages: English
• ISSNs: 1743-8225

Abstract

Wireless sensor networks (WSNs) are widely used to perform measurement and monitoring tasks in large sensing fields. Several applications require every point in the monitored area to be covered by at least k sensors (k–coverage problem) for measurement accuracy or robustness. In heterogeneous multi–functional sensor networks, p parallel observation tasks are performed with p types of sensors, each task having its own ki (i = 1, 2, . . ., p) coverage requirement, assuming that multi–functional sensors are used which may have multiple measurement capabilities (multi–k–coverage problem). In order to prolong the lifetime of the network, active and sleeping states of sensors can be altered while maintaining the required coverage for all sensing tasks. In this paper an efficient distributed sensor state scheduling algorithm for multi–functional WSNs is proposed to solve the multi–k–coverage problem. The proposed multi–functional controlled greedy sleep algorithm is the generalisation of a recently proposed quasi–optimal scheduling algorithm. It is easy to implement and solves the common scheduling problem of heterogeneous multi–functional sensor networks. It has low local communication overhead and can adapt to dynamic changes in the network, while the required network–wide coverage for all sensing tasks is guaranteed as long as it is physically possible. The performance and the fault tolerance of the algorithm are illustrated by simulation examples.