Anchor supervised distance estimation in anisotropic wireless sensor networks

Distance estimation is a key issue in range-free localization algorithms for wireless sensor networks. Approaches that assume isotropy of networks, such as Dv-hop and Gradient, cannot obtain accurate distance estimations in anisotropic sensor networks thus are not applicable to such networks. The anisotropy of sensor networks comes from two aspects: uneven nodal distribution and irregularity of deployment region. Existing localization algorithms for anisotropic wireless sensor networks usually only deal with one of the two aspects. In this paper, we propose an anchor supervised distance estimation approach which can simultaneously cope with both of the two aspects. In this approach, an anchor node selects a “friendly” subset from all other anchor nodes to which its distance estimates are accurate and broadcasts the selection result to neighboring common nodes. The common nodes then use these friendly anchors to perform distance estimation. We analyze distance estimation accuracy of this approach through extensive simulations. The results show that, compared with Dv-hop, our proposed approach dramatically reduces distance estimation error in anisotropic wireless sensor networks with an average factor of 67%. Consequently, the localization error of Dv-hop is reduced by an average factor of 71% if enhanced with our distance estimation approach.