A Node Deployment Optimization Algorithm of WSNs Based on Improved Moth Flame Search

Wireless sensor networks, one of the basic technologies of remote environmental monitoring, can provide efficient sensing and communication services under limited energy supply. Coverage control is an important method to ensure efficient communication and reliable data transmission. Given the complex physical environments, which impede the energy supplement and recovery of sensor nodes, the motivation of our research is to repair the coverage holes and reduce the energy consumption during the deployment of sensor nodes. Firstly, the variable spiral position update and the adaptive inertia weight strategy are adopted to improve local development and global search ability of the moth flame algorithm. Secondly, we analyze the virtual force of nodes, including the attractive force of uncovered grid points, the virtual force between adjacent sensor nodes and the repulsive force of boundary. The node resultant force is used as the disturbance factor of moth position updating to optimize the path, which effectively avoids the “premature” problem of the algorithm and accelerates global convergence. Finally, moth search is used to guide nodes to move to the area with coverage holes to achieve coverage optimization. In addition, we limit the random walking range of moths to reduce the moving distance. The simulation results show that compared with VFPSO, VFA and MFO algorithms, the coverage rate of VF-IMFO algorithm is increased by 7.16%, 3.85% and 22.2%, and the average moving distance of nodes is reduced by 9.01
$\textbf {m}$
, 0.51
$\textbf {m}$
and 32.46
$\textbf {m}$
respectively. Moreover, under different deployment environments, the VF-IMFO algorithm still maintains remarkable performance advantages.