Wireless sensor network coverage optimization strategy based on improved cuckoo search algorithm
Siyang Li · 2023
Efficient deployment of nodes inside the Wireless Sensor Network contributes to achieving maximum network coverage and minimum energy consumption. Aiming at the problems of low sensor node coverage and high redundancy of Sink nodes in the convergence layer in wireless sensor networks, we propose the WSN coverage optimization method based on the improved cuckoo search algorithm. First, the qubit Bloch spherical coordinates are introduced in the population initialization process to maintain better population diversity and wider coverage space. Secondly, in the Levy flight optimization stage of the cuckoo search algorithm, the residual operation inside the candidate solution matrix is used to generate long-distance dependency information of sensor nodes and new candidate solutions. Finally, a greedy evaluation strategy of dimension-by-dimension update is added to improve the random walk selection process to avoid the interference of node information between the same dimensions, so as to improve the iteration speed and accuracy of cuckoo search algorithm. Here, we use Matlab to build the coding environment and simulate the sensor nodes. The simulation results show that, compared with the leading methods in the current field, the improved cuckoo search algorithm can achieve a higher coverage ratio, and the redundancy ratio of Sink nodes in the aggregation layer is lower. Our proposed ICS model has better robustness and higher accuracy.