Coverage Enhancement Strategy for WSNs Based on Virtual Force-Directed Ant Lion Optimization Algorithm

When deploying wireless sensor networks in complex monitoring areas such as battlefields and disaster areas, sensor nodes usually form an initial deployment by airdropping. This random deployment method causes the nodes to deviate from the optimal deployment position and the phenomenon of coverage holes appears. This paper proposes a coverage enhancement strategy for WSNs based on the virtual force-directed ant lion optimization algorithm (VF-IALO). First, based on the original ant lion optimization algorithm, we re-assign antlions and dynamically reduce the number of antlions. The strategy of continuous ant random walk boundary shrinkage factor is combined. Secondly, we limit the range of ants’ random walk to reduce the moving distance of the sensor node during the secondary deployment process. Finally, we introduce the virtual force composed of neighbor nodes force, grid point gravity, and boundary repulsion. The weight coefficients of the virtual force, antlion, and elite antlion dynamically changed to update the ant position. It can avoid the algorithm fall into the local optimal solution, accelerate the algorithm convergence speed and improve the global optimization ability. The simulation results show that when 30 sensors are deployed in a monitoring area of 60m $\times60\text{m}$ , compared with the VFA, ALO, and VFPSO algorithms, the coverage rate of the VF-IALO algorithm is increased by 7.656%, 11.048%, and 4.088%, the average moving distance of the nodes is reduced by 0.4759m, 2.3387m, and 3.3762m respectively. More importantly, when the network scale (region size and number of nodes) changes, the VF-IALO algorithm still maintains a clear performance advantage.