Sensor Deployment Optimization Methods in Electromagnetic Field Based on EEIF-PLI

Electromagnetic field has a great impact on the performance of wireless sensor networks (WSNs). The influence of electromagnetic field generated by power equipment on wireless sensor (WS) nodes is designated as two parameters: environmental electromagnetic interference factor (EEIF) and path loss index (PLI). EEIF is used to describe the probability in the sensor node probability sensing coverage model. PLI is substituted into a node communication path loss model; then, a node packet receiving success rate model can be obtained by above models, which is used to evaluate node communication quality in electromagnetic interference environment. Combined with a service cost model of wireless sensor networks in complex electromagnetic environment, the performance evaluation function of wireless sensor networks is established, and constraints are determined. Solved by the particle swarm optimization (PSO)-gray wolf optimizer (GWO) algorithm, optimal deployment of wireless sensor networks in complex electromagnetic environment is realized. Experimental results show that the proposed method can achieve best performance.

Automated summary

The electromagnetic field significantly affects the performance of wireless sensor networks (WSNs). The impact of electromagnetic field generated by power equipment on wireless sensor (WS) nodes can be characterized by two parameters: environmental electromagnetic interference factor (EEIF) and path loss index (PLI). EEIF is used to describe the sensing coverage probability in the sensor node probability model, while PLI is used in a node communication path loss model to evaluate the node communication quality in the electromagnetic interference environment. By combining a service cost model of wireless sensor networks in complex electromagnetic environments, the optimal deployment of wireless sensor networks in this environment can be achieved using the particle swarm optimization (PSO)-gray wolf optimizer (GWO) algorithm. Experimental results demonstrate the effectiveness of the proposed method in achieving optimal performance.