Deployment Optimization of Self-Powered Sensors for Coverage Enhancement in the Internet of Things Sensing Layer

Coverage is an important indicator of the data acquisition and transmission of the Internet of Things IoT sensing layer. However, the sensor nodes are equipped with battery units, which results in limited coverage performance. Self-powered sensors provide a solution to handle energy constraint problems. In this article, self-powered sensors are used as the sensing nodes, which could collect energy from the environment to supplement the battery unit while performing coveragetasks. Adeploymentoptimizationmethod of self-powered sensors is first proposed based on an improved particle swarm optimization (PSO). In this method, the population is divided into homogeneous and heterogeneous clusters. Homogeneous clusters use the same search strategy as the PSO, while the heterogeneous clusters are further divided into multiple subgroups by the topology structure and using the improved communication strategy. On this basis, the position of self-powered sensors is obtained, which could achieve the desired coverage performance. Then, a local information-based critical node identification method is proposed. When the failed critical nodes are found, the redundant nodes around the critical nodes will work in place. The tolerance performance of coverage is enhanced. The results of simulation experiments show that the self-powered nodes are more uniformly distributed; the coverage is significantly improved, and the coverage could achieve 95.95%.

Automated summary

This article presents a method for deployment optimization and critical node identification using self-powered sensors to address the limited coverage problem in the sensing layer of the Internet of Things. By improving the particle swarm optimization algorithm, the position of self-powered sensors can be obtained to achieve the desired coverage performance. When failed critical nodes are detected, redundancy nodes are activated to enhance coverage tolerance.