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Jiguo Yu 1,2 and Shaohua Ren 1 and Shengli Wan 1 and Dongxiao Yu 3 and Guanghui Wang 4
Recommended by Limin Sun
1, School of Computer Science, Qufu Normal University, Rizhao 276826, China
2, Key Laboratory for Intelligent Control Technique of Shandong Province, Qufu Normal University, Rizhao 276826, China
3, Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong
4, School of Mathematics, Shandong University, Shandong, Jinan 250100, China
Received 20 July 2012; Accepted 19 September 2012
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1. Introduction
Wireless sensor networks (WSNs) are composed of a large number of sensor nodes, which are densely deployed in a given region. All nodes collaborate to execute sensing and monitor tasks and to send sensed data to sinks. It has so far been employed in military activities, target acquisition, environmental activities, and civil engineering. On the one hand, each sensor is equipped with a limited power source, and it is impossible to replenish power resources in most applications. On the other hand, many applications require a durable lifetime. Thus, a major constraint for WSNs to be widely used is network lifetime.
Since wireless sensor networks are characterized by high density and limited energy. It is not necessary to have all sensor nodes operate in active mode simultaneously. Sensor scheduling, the most effective method to solve coverage problems, makes redundant nodes into sleep mode, in which energy consumption is lower, while active nodes meet specialized requirements. It can decrease the number of active nodes, thus avoiding the channel collision, reducing the network energy consumption, and prolonging the network lifetime substantially. However, most of the existing results on k -coverage are based on the deterministic sensing model, where a point in a region is guaranteed to be covered by k sensors, that is, the point is within the sensing ranges of those k sensors.
In this paper, we consider the k -coverage sensor scheduling problem. A more realistic sensing model, called stochastic sensing model, was considered. Under the stochastic sensing model, a point is covered by a sensor with...