Research On Key Technology Of Coverage Optimization In WSN

The wireless sensor network (WSN) has significant application value in traditional and emerging fields like industry, agriculture, circulation and environment. One of the key technologies of the WSN is the network coverage control. The network coverage, together with signal transmission,directly affects the data sensing and monitoring ability in large-scale habitat monitoring system. Energy consumption measurement is critical to the life cycle of the WSN, which is prone to node failure, rapid energy consumption, loss of network connectivity and coverage during the operation. What is worse, the network may fail at the disconnection caused by physical node failure and at the rapid consumption of the energy due to the frequent use of the remaining nodes. Therefore, it is urgently needed to find a way to make effective use of the limited energy, ensure data transmission, improve network coverage and maximize network life. In order to solve the above problems, this paper takes WSN as the research object,and probes deep into the optimization technologies of WSN network coverage from the aspects of irregularity perception of coverage optimization, energy efficient coverage control, and restoration of network coverage connectivity.The main findings and innovations are as follows:First of all, this paper proposes the coverage optimization algorithm based on channel irregularity in view of how the network coverage quality is affected by the irregularity of node perception domain in the network arising from the channel attenuation in wireless signal propagation. In the process of wireless channel propagation, there are different path losses and signal attenuations. Based on the analysis of wireless propagation characteristics, the author puts forward an irregularity sensing model based on channel characteristics, which quantifies the irregular sensing area of network nodes and gives the effective sensing area of the sensor nodes.Besides, the author applies the coverage optimization algorithm based on irregularity sensing in the zoning and adjustment of the monitoring area of the nodes in the network. In the aspect of network coverage, the algorithm optimizes the irregular sensing areas by constructing the optimal coverage set, thereby improving the effective coverage of the network. The results show that the algorithm boasts excellent convergence. Moreover, the coverage optimization algorithm based on irregularity sensing is analyzed based on the experimental data collected from the instance of farming environment.Furthermore, targeted at the problem of energy limitation and uneven distribution of energy consumption during the operation of the WSN, this paper comes up with the coverage control strategy for energy efficient WSN. Whereas the network nodes have limited energy, the author develops an adaptive coverage algorithm based on probabilistic dynamic model. The model establishes the optimal set of working nodes to reduce the energy consumption of individual nodes, aiming at identifying the suitable number of working nodes. For the balance of network energy consumption, the author proposes a network coverage algorithm based on energy consumption sensing. In light of node energy consumption distribution, the author uses the algorithm to build a nonlinear optimization model for node energy consumption and coverage, converts the constraints into linear ones by the constraint reduction method, and seeks the suboptimal solution of the problem to strike a balance between the network coverage and network energy distribution.Last but not the least, this paper proposes a WSN coverage connectivity restoration strategy based on mobile nodes in consideration of the balance relationship between network connectivity and network coverage quality in WSN coverage control. The specific steps are as follows. First, the author analyzes the minimum connectivity set of the network is analyzed, and studies a network coverage enhancement method based on mobile nodes. Second, the author discusses the irregularity of the blind spots in network coverage, finds the minimum approximate numerical solution based on the quantitative relationship between the energy consumption of the associated nodes in the blind spots and the quantitative relationship between the mobile nodes, and determines the optimal relative position of the mobile nodes. Third, considering the connectivity factors among the nodes in the network, the author sets up a coverage area restoration method based on network connectivity to modify the redundant area and blind spots of the coverage area so that the network could have better coverage connectivity.