Underwater Sensor Network Node Locator Based on Improved Whale Optimization Iterative Algorithm

To address the issues of low node localization accuracy caused by the limited number of anchor nodes and large iteration errors in underwater wireless sensor networks, an improved whale optimization-Newton iteration (IWONI) algorithm for underwater three-dimensional node localization was proposed. IWONI first uses the Newton iteration algorithm to establish a corresponding rule for the distance relationship between nodes, and utilizes the estimated target position and correction factor to provide a dynamic search area for the improved whale optimization algorithm. Secondly, a fitness function weighted by measurement error is established as the judgment criterion, and the improved whale optimization algorithm is used for iterative solution to obtain the optimal solution. Finally, the network node positions are calculated through the localization equation. To validate the performance of the IWONI algorithm, comparative experiments were conducted on localization error, convergence performance, and localization success rate against time difference of arrival algorithms (TDOA-Taylor, TDOA-CHAN), ranging algorithms (least squares method, Gauss-Newton iteration), and Newton iteration algorithm. The impact of the number of nodes on localization accuracy was also investigated. The comparison results show that: (1)The IWONI algorithm has significantly lower localization error and faster convergence speed than other compared algorithms. (2)The IWONI algorithm has a high localization success rate of 92% even in the presence of high measurement noise, which is significantly better than other compared algorithms. (3)In the case of a constant communication radius, employing 5 to 7 sensor nodes can achieve a balance between localization accuracy and cost effectiveness in the IWONI algorithm.