Considering the high accuracy needed for indoor positioning, this paper develops a novel indoor positioning algorithm for the wireless sensor network (WSN) in the following steps. First, the RSSIs of the network nodes were sampled and analyzed, and the excess errors were filtered to enhance positioning accuracy. Next, the initial position was iteratively obtained by the weighted centroid algorithm, and a correction matrix was developed to improve the Taylor series expansion (TSE), and the final position was determined through improved TSE iteration. The proposed positioning method was verified through simulation.

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The nonlinear and uncertain of water environmental pollution,make the traditional water quality evaluation methods have limitations.In order to improve the accuracy of water quality evaluation,The paper put forward the water quality evaluation model based on improved wavelet neural network (Wavelet Neural Network, the WNN).Optimize the initial weights of wavelet neural network based on Adaptive Genetic Algorithm (Adaptive Genetic Algorithm, AGA),and then training the network by used wavelet neural network algorithm,finally,testing the trained network.The simulation results show that the combination of Adaptive genetic algorithm and wavelet neural network improved the efficiency of network training,and this method can be used in water quality evaluation, and the evaluation result has high precision and accuracy.