Refinement of localization results in wireless networks using weighted Universal Improvement Schemes

In this paper, we present an extension of our concept of the Universal Improvement Scheme (UnIS) investigated in our previous work. The main focus of UnIS is to improve the localization results of unknown wireless nodes, aka mobiles, in the network with prepositioned reference nodes, aka anchors. The UnIS algorithm uses some additional information known a priori about the network. This information is represented by distances, known before the experiment, between couples of mobile nodes being localized. In this work, the optimization of the UnIS algorithm is proposed. According to our previous observations, we introduce a novel weighted UnIS (W-UnIS) approach that uses different weighting criteria to achieve a higher improvement ratio. We compare the effectiveness of W-UnIS scheme to its predecessor using the data collected in a deployed wireless sensor network testbed. The obtained results indicate that the localization error can be reduced significantly using our new W-UnIS approach.