Mobile-Assisted Anchor Outlier Detection for Localization in Wireless Sensor Networks

Accurate location information is critical to many applications in wireless sensor networks (WSNs) such as target tracking, environmental monitoring and geographical routing. Localization aims to figure out the locations of unknown nodes based on global locations of anchors and inter-node distance measurements. However, the existence of outlier anchors and outlier distances degrade localization accuracy in many localization algorithms. Most existing outlier detection approaches focus on distance outlier detection; few efforts have been devoted to anchor outlier detection. In this paper, we propose a mobile-assisted approach to detect outlier anchors and mitigate their negative effects in localization to achieve high localization accuracy. The proposed approach, namely Mobile-Assisted Anchor Outlier detection (MAAO), employs a mobile element to traverse the wireless sensor network several times to collect position information from static anchors in the network. For every static anchor, the mobile element computes the average of the anchor's positions acquired from all tours, and compare it with the position acquired from the last mobile tour to detect whether the anchor is an outlier or not. The evaluation results show that MAAO can effectively detect outlier anchors, which consequently results in remarkable improvement in localization accuracy by not using outlier anchors in the localization process.