Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

Energy limitation of traditional Wireless Sensor Networks (WSNs) greatly confines the network lifetime due to generating and processing massive sensing data with a limited battery. The energy harvesting WSN is a novel network architecture to address the limitation of traditional WSN. However, existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical space. Comprehensively considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN, we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual coverage. The Confident Information Coverage (CIC) model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage (CICMTP) problem to minimize the deployed sensor nodes. As the CICMTP is NP-hard, we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC (LGTA-CIC) and Overall Greedy Search Algorithm based on CIC (OGSA-CIC). The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation rate. Extensive simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP, TPNP and EENP algorithms.