Abstract. Considering the topology of real distance of the anchor node and the real trilateration composed of the unknown positioning node, an improved DV-Hop localization algorithm for wireless sensor networks was proposed. Firstly, unknown node will be measured by the trilateration-based triangle. Any of the three anchor nodes which meet collinear degree within a predetermined range, will be divided into one group. According to multiple groups of location estimation, finally by using clustering analysis, the result of the location could be optimized by selecting the largest cluster, removing the noise points and boundary points and leaving the core point with small errors. In the end, the unknown node estimation of location will be obtained.
Abstract. Monitoring of continuous object distribution in large-scale environmental monitoring depends on the problem of high-density sensor networks. The RBF neural network is used to fit the distributed data of continuous objects at low sensor network density. Firstly, based on the distribution characteristics of continuous objects, a continuous evolution model of continuous objects based on Gaussian smoke model [1] is established. Secondly, the RBF neural network is trained based on the known distributed node data. Thirdly, RBF neural network is used to fit the distribution data of continuous objects. Finally, the fitting error of different number of training nodes is calculated. Through the experiment of matlab simulation, the practicability of RBF neural network for continuous object distribution has been validated.
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