Research On Key Technologies Of Effective Coverage And Topology Control For Wireless Sensor Networks

Wireless sensor networks (WSNs) consist of low-cost, low-power tiny sensor nodes which form networks in an ad hoc manner and communicate with each other to perform sensing, data processing and data storage cooperatively. Wireless sensor networks have wide application prospects in the fields of military and civilian applications and have attracted extensive attention. Network coverage control and topology control are key supporting technologies in wireless sensor networks, which affect directly the monitoring performance of the networks on the physical world and are the first issue to be considered when carrying out an application. Effective coverage and topology control algorithms are also an important guarantee for successfully carrying out other operations of wireless sensor networks. This dissertation focuses on the energy conservation and the lifetime extension problems in wireless sensor networks and presents an in-depth study on the sensor network coverage control and topology control problems, and makes some innovative research results as follows.(1) A sensor node distribution optimization mechanism for mobile sensor networks is proposed based on differential evolution algorithm. Considering the characteristics of WSNs and the intrinsic constraints of sensor nodes, and utilizing the mobility of sensor nodes in mobile sensor networks, a sensor node coverage model is proposed and methods are proposed for the coverage performance evaluation. The sensor node distribution optimization objectives are used as the fitness functions of the differential evolution algorithm, and differential evolution operations are performed based on these fitness functions, the sensor node locations initialized randomly are optimized. Simulation results demonstrate that the proposed algorithm can quickly achieve node distribution optimization of a mobile sensor network with a relatively low cost, efficiently decrease the number of activated nodes, increase the effective coverage rate, and achieve global optimization of the deployment of the mobile sensor network.(2) A node distribution optimization scheme for mobile sensor networks is proposed based on multi-objective optimization algorithm. In order to further improve the performance of network coverage, aiming at the sensor node distribution optimization problem of the sensor nodes, this scheme introduces multi-objective optimization theory and makes use of the mobility of sensor nodes to optimize the sensor node distribution, and thus improves the network performance. The Pareto criterion is also proposed for evaluating the coverage performance. A series of simulation results demonstrate that the above optimization mechanism can quickly converge to the tradeoff point between the network coverage rate and the network sensing consumption, increase the effective coverage rate, reduce network sensing consumption, and prolong the network lifetime.(3) A node distribution optimization mechanism based on minimum redundant coverage for mobile sensor networks is proposed. In the environments like battlefields and other adverse environments, in order to improve the performance of monitoring and the reliability of the sensor network, sensor nodes are usually deployed in the target area with a high-density by random deployment. This leads to the coverage regions of the sensors overlapped each other, which causes large network redundant coverage, and thus causes redundant data collection and transmission, and unnecessary energy consumption. Then the sensor nodes will die because of quick energy consumption. The goal of the majority of the existing coverage control algorithms is only to increase the network coverage rate and to maximize the effective cover area. The mechanism proposed in this dissertation systematically analyzes the relationships between network coverage rate, network redundant coverage and displacement of mobile nodes, and then the corresponding sub-objective functions are defined and weighted as the fitness function of the optimization mechanism. Simulation results demonstrate the convergence and the effectiveness of the proposed algorithm, and that the above optimization mechanism can reduce network redundant coverage and prolong the network lifetime, with the actual demand of the network coverage satisfied, and provide reliable solution for deployment optimization of the mobile sensor network.(4) A strategy for the discovery of coverage blind spot based on Voronoi diagram and a dynamic restoration mechanism based on ant colony algorithm are proposed. The random initial deployment may not be able to fully cover the monitoring region, or the node failure, energy depletion, and other reasons for the failure of nodes may result in coverage blind spots in a network. In practical applications, the uncovered regions result in incomplete monitoring data and thus affect the service quality of the whole network. This may affect the final decision. The proposed discovery strategy discovers and ascertains the locations of blind spots, and then the above restoration mechanism is used to restore the coverage blind spots, so that to make the network restore to work normally. Simulation experiments are performed to analyze the utilization of the mobile node and the average moving distance of the nodes. Compared with the scheme of using of all static nodes, this method not only can make the node deployment more evenly, but also can improve the network quality of service, which verifies the feasibility and effectiveness of the restoration mechanism based on ant colony algorithm.(5) A hierarchical topology control algorithm based on optimal clustering head for wireless sensor networks is proposed. This algorithm uses a clustering strategy to divide the sensor nodes into two types, cluster head nodes and common nodes. The common nodes sense and send data to the cluster head. The cluster head is responsible for coordinating and managing the other nodes, and sends the data to the sink node after fusion. This algorithm introduces residual energy of sensor nodes, communication energy consumption of neighbor nodes, and the distance between the nodes and the sink nodes to select the cluster head. Under the premise that the residual energy of the node exceeds a certain threshold, the node with higher residual energy and closer to the sink and the node with minimal residual energy has higher probability to become the cluster head than the node with lower residual energy and farther to the sink. Simulation results demonstrate that the proposed algorithm can balance the energy consumption and efficiently prolong the network lifetime.