A Node Selection Algorithm Based on Multi-Objective Optimization Under Position Floating

Almost all existing node selection algorithms of the underwater sensor networks (USNs) are designed by assuming ideal environments. However, the position floating of the underwater sensor nodes which caused by ocean currents cannot be ignored in practice. Aiming at solving this problem during underwater target tracking, a node selection algorithm based on multi-objective optimization under position floating was proposed in this paper. First, the error caused by position floating is converted into a floating noise. Then, as the criteria for node selection, both Fisher information matrix (FIM) and mutual information (MI) under position floating are derived by the particle filter under position floating (PF-PF). Finally, the number of nodes, the corresponding FIM and MI are set as the objective function, both nondominated sorting genetic algorithm II (NSGA-II) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are used to find the optimal node selection scheme. Simulation results show that the proposed algorithm can overcome the influence of position floating, and ultimately, its tracking performance is more stable and accurate.