Research on Link Loss Rate Inference Based on Data Aggregation in Wireless Sensor Network

Abstract

The internal link loss characteristic inference has become an increasingly important issue for operating and evaluating a wireless sensor network. In this paper we propose a new algorithm, based on MPLE algorithm and binary hamming distance and hop count, to infer the internal link loss characteristics. First, we use the MPLE model to part the problem of inference into the serial of sub-problem, a sub-problem is compose of subtree that contain two leaf nodes. Then, we select the subtree by using hamming distance of the sequences at each pair of nodes and incorporating the hop count available at each node in WSN. finally the Pseudo-Likelihood Function (PLF) is used to solve the problem. The simulation shows that the link loss performance parameters can be inferred accurately, and the proposed algorithm scales well according to the sensor network size.