The Coverage Problem In Wireless Sensor Network (wsn) Research

Wireless sensor networks (WSNs) are composed of a large number of sensor nodes, which are self-organized and communicate with each other in a multi-hop manner. WSNs combine sensor technology, embedded computing technology, wireless communication technology and distributed information processing technology. These sensor nodes in which are used for real-time monitoring, sensing and collecting all kinds of information, and then, sending the information to terminal users through sink node. Currently, wireless sensor networks have been widely used in the fields such as military, environment detection and disaster avoiding. In practical applications, sensor nodes are battery-powered. Due to the limitations of node size, cost and working environment of WSNs, energy of nodes is limited and can not be recharged. Therefore, how to achieve energy-efficiency of the networks and maximize the networks lifetime with limited energy becomes an important research goal of WSNs.Coverage problem is a critical issue in the energy-efficiency research of WSNs. In order to collect all the information we need, a network must meet specific coverage requirements, meet coverage requirement is the premise of realizing the network availability. Scheduling the redundant nodes to the low-power sleep mode can reduce energy consumption while meeting the coverage requirement. The lifetime of the network can be extended by node scheduling.Basing on the detailed analysis of the existing achievements, we research the advantages and disadvantages of these results. In this paper, an energy-efficient local coverage algorithm and a stochastic k-coverage sensor nodes scheduling algorithm are proposed. The correctness of the algorithms in theory and practical feasibility can be guaranteed by our mathematics analysis and simulation results. The details are as follows.In the third chapter of our paper, we use cover sets to solve the coverage problem, an energy efficient local coverage algorithm is proposed. When generate a cover set, our algorithm gives a full consideration to the network contribution of the sensor. We use a cost function to takes into account the monitoring capabilities, criticality and remaining battery life of the sensor. The node with larger function value is prior to be selected to the cover set, which can achieve the purpose of cover more goals with much less sensors. The algorithm can generates joint cover sets, theoretical analysis shows that joint cover sets can extend network lifetime effectively. When we get all the cover sets of WSNs, we use cover sets scheduling to extend network lifetime.K-coverage can improve the fault tolerance of the network, in the fourth chapter, we propose a stochastic k-coverage sensor nodes scheduling algorithm, and it can maintain network connectivity. The algorithm considers both deterministic and stochastic sensing models of the sensors. Precisely, we solve the sensor scheduling problem for k-coverage under the deterministic sensing model, and compute the corresponding minimum number of sensors. Then, we adapt the results of this sensing model to solve the sensor scheduling problem for stochastic k-coverage under a probabilistic sensing model. We get the cover set under random sensor model to achieve k-coverage of WSNs in using of the algorithm. We use regular pentagons in place of Reuleaux triangle to divide the sensing disk. We check whether the center of each regular pentagon contains at least k active sensors to determine the redundancy of the node. While the algorithm is finished, the sensors are divided into different cover sets, it can effectively extending the network lifetime by cover set scheduling. Theoretical analysis and simulation results show that our algorithm can use same number of sensors to cover a larger area, can save the number of nodes to fulfill k-coverage, and effectively extend the network lifetime. At the same time,k-coverage improves the fault-tolerant performance of the network.