Aiming at the poor positioning accuracy of the APIT positioning algorithm in wireless sensor networks, this paper proposes an APIT positioning algorithm 3DTC-APIT based on tetrahedral centroid cutting. First, the algorithm reduces the occurrence of misjudgment by setting the node counter. Second, use the centroid of the tetrahedron to cut the node location area multiple times to solve the node coordinates to be located. Finally, the node to be located whose position has been determined is added as a temporary anchor node to assist other anchor nodes in positioning. The simulation results show that compared with the comparison algorithms, the algorithm 3DTC-APIT can better improve the positioning accuracy and positioning coverage of nodes.
Aiming at the problem of low tracking accuracy caused by the sparse deployment of UWSN nodes in the process of target tracking based on underwater wireless sensor network (UWSN), a target tracking algorithm based on the regulate of node height and the consideration of node mobile energy consumption is proposed. First of all, the optimal height solution methods are given when the target height is known and unknown. Secondly, a dynamic coefficient is introduced to adjust the weight between tracking accuracy and energy consumption, and an objective function is established to comprehensively consider the target tracking accuracy and node moving energy consumption, so as to realize the dynamic regulate of node height. The simulation results show that the algorithm can achieve good and stable tracking effect and effectively reduce the energy consumption caused by node movement.
The differential evolution algorithm is a random search algorithm. Aiming at the problems of premature convergence and slow optimization in differential evolution algorithm, a differential adaptive SA-DEPSO algorithm based on particle swarm optimization is proposed. First, the positioning problem is transformed into a function iteration optimization problem by using the least square method. Then the adaptive differential evolution strategy is fused on the basis of the particle swarm optimization algorithm. This algorithm can not only avoid the problem of premature convergence, but also improve the optimization speed and reduce the positioning error. Simulation analysis shows that when the number of iterations reaches 40, the algorithm in this paper reaches the optimal value and converges, saving the optimization time. Compared with DEPSO, SA-MCDE and literature 11, the average number of optimization runs is reduced by 75, 55 and 25 times; the average positioning error of the algorithm in this paper is reduced by 17.3%, 13.1% and 7.5% respectively.
In order to improve the positioning accuracy of the centroid algorithm, this paper proposes the RSSI centroid node positioning algorithm based on chimpanzee optimization (SRSSI-CHOA). The algorithm first optimizes the received signal strength value by velocity constant filtering, and then calculates the unknown node coordinates using the distance-weighted centroid algorithm, and then constructs the fitness function based on the distance information and the obtained position information; Finally, the chimpanzee algorithm is used to find the optimal solution position and optimize the node coordinates. The simulation results show that the proposed algorithm is 30.78%, 24.42% and 8.3% lower than the RSSI - centroid, Gaussian model RSSI centroid average and RSSI-PSO algorithm.
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