Hindawi Publishing Corporation International Journal of Distributed Sensor Networks Volume 2012, Article ID 746501, 11 pages doi:10.1155/2012/746501 Research Article A Stochastic k -Coverage Scheduling Algorithm in Wireless Sensor Networks Jiguo Yu, 1, 2 Shaohua Ren, 1 Shengli Wan, 1 Dongxiao Yu, 3 and Guanghui Wang 4 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 Correspondence should be addressed to Jiguo Yu, jiguoyu@sina.com Received 20 July 2012; Accepted 19 September 2012 Academic Editor: Limin Sun Copyright © 2012 Jiguo Yu et al. 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. Coverage is one of the key issues to achieve energy efficiency of a wireless sensor network. Sensor scheduling is one of the most important methods to solve coverage problems. It can ensure the coverage degree of a region and prolong the network lifetime. In this paper, we focus on the k-coverage scheduling problem to guarantee k-coverage sensing and network connectivity. We consider both deterministic and stochastic sensing models of the sensors and adapt the results of deterministic sensing model to solve the sensor scheduling problem under the stochastic sensing model. We use regular pentagons to divide the sensing disks to solve the k-coverage problem. Each sensor node runs a stochastic k-coverage-preserving scheduling algorithm to determine its work modes, and redundant nodes can enter into sleep mode, while active nodes ensure the coverage of the network. Theoretical analysis and simulation results show that our algorithm can reduce the number of active nodes and extend the network lifetime significantly while maintaining a given coverage degree. 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 spe- cialized 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 schedul- ing 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 some proba- bility. Firstly, we solve the sensor scheduling problem of k-coverage under the deterministic sensing model. Then, we utilize the results to solve the problem under the stochastic sensing model. Finally, we present a stochas- tic k-coverage-preserving sensor scheduling algorithm to achieve lifetime extension. We use regular pentagon instead of Reuleaux Triangle to achieve k-coverage and give the minimum number of sensors to fully k-cover a region while ensuring the connectivity. Theoretical analysis and comprehensive simulation show that our approach is effec- tive.