Mathematical Modeling and Simulation of Wireless Sensor Network Coverage Problem

• Main Author: Jianxia Guo
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Wireless Communications and Mobile Computing
• Online Access: http://dx.doi.org/10.1155/2021/5537004

The research on wireless sensor networks has achieved a lot in recent years and some of the results have been put into practical applications, but with the increasing demand and requirements for wireless sensor networks, many old and new problems need to be solved urgently. In this paper, a data topology optimization algorithm based on local tree reconstruction for heterogeneous wireless sensor networks is proposed for data transmission in wireless sensor networks that are easily affected by external instabilities. This heterogeneous network can accomplish better data transmission; firstly, the nodes are divided into different layers according to the hop count of nodes in the network, and a certain proportion of relay nodes are selected for different layer nodes; then, different initial energy is set for different layer nodes, and since the data packets of different nodes have different sizes, the corresponding data aggregation coefficients are used in this paper according to the actual data requirements of the network during data transmission; finally, the topology of the tree is dynamically updated in real time during the operation of the network to extend the lifetime of the nodes. The simulation results verify that the proposed heterogeneous network topology evolution algorithm effectively extends the network lifetime and improves the utilization of nodes. This paper establishes a modified least-squares target localization model to achieve accurate 3D localization of targets in real scenes and proposes an optimal base station node selection strategy based on spectral clustering using the location distribution information of base station nodes in space. The simulation results show that the error of the terminal 3D coordinates calculated by the proposed algorithm is smaller than the real coordinates, and the error is smaller than other existing algorithms with the same simulation data.