Distributed Beacon Drifting Detection for Localization in Unstable Environments

Abstract

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.

Collaborative Localization and Location Verification in WSNs

“…The recognition error rate is a ratio of the number of nodes considered as unreliable nodes by erroneous judgment to the number of actual unreliable nodes. The DBDD algorithm in [25] is used to compare with our VLVA. The number of drifted anchors is fixed, and the density of network is varied from 100 to 400.…”

“…According to mutual observing information between neighbor nodes, Wei et al [24] formulated a probability model to fulfill location verification, which achieved relatively good results, but they didn’t discuss the subsequent moved-node re-localization process. Reference [25] uses a distributed neighbor node scoring mechanism for RSSI to identify any drifted anchors, but it cannot be used in the case with compromised anchors.…”

“…Normal nodes can eliminate these effects by re-locating themselves periodically, but after drifting or being compromised, these anchors can produce large negative effects on normal nodes’ localization process due to the location inconsistency between the claimed location and the real location of anchors. For range-based localization processes, the more anchors there are, the higher the localization accuracy that can be obtained, so some cooperative localization methods are proposed [19,25], which promote normal nodes as anchors, but certain accumulated errors would be introduced in such methods. In addition, distributed algorithms are more suitable for WSNs than centralized algorithms.…”

A Node Localization Verification Model for WSN

“…According to mutual observing information between neighbor nodes, Wei and Guan [16] formulated a probability model to implement location verification, which achieved relatively good results, but they didn't discuss the subsequent moved-node re-localization process. Literature [17] uses a RSSI-based distributed neighbor node scoring mechanism to identify drifted anchors, but it cannot be used in the case with compromised anchors.…”

“…The algorithm in [17] is used to compare with our UNDA, because it also fulfills location verification in a distributed manner, and we call it NDD. However, by reason of the NDD adopts an idea that every node must participate in location verification initiatively, it cannot be applied in scenario with malicious anchor.…”