基于概率感知的传感器网络节点部署策略

iming at the characteristics of complex and variable environment, high sensor deployment cost, poor connectivity and high energy consumption in target coverage of wireless sensor networks, a cost optimization node deployment strategy for heterogeneous sensor networks based on particle swarm optimization and genetic hybrid algorithm was proposed. This strategy is oriented towards more complex realistic scenarios and adopts a probabilistic perception model that is more realistic. In the process of node deployment, the uncertainty of the number of sensor nodes, the different importance of the target to be monitored, the different deployment costs of different potential deployment locations, and various obstacles in the deployment environment are taken into account. On the basis of satisfying the minimum sensing probability of the target to be monitored and the connectivity of each sensor node, the solution is carried out with the deployment cost, energy consumption, connectivity and sensing probability as the optimization objectives. Through simulation experiments in various scenarios, compared with other similar algorithms, PSO-genetic hybrid algorithm can still effectively optimize the overall network performance while meeting the requirements of target perception and connectivity of each sensor node under the condition that the number of sensors is unknown. Moreover, the optimization effect and convergence time of the objective function are greatly improved.