Resilient Privacy-Preserving Distributed Localization Against Dishonest Nodes in Internet of Things

Existing distributed localization methods rarely consider the location privacy preservation problem, which however is nonnegligible. Regarding location privacy, typical solutions rely on a curious-but-honest model, requesting that all participants follow the rule. Different from the existing studies, both honest and dishonest models are considered in this article. We first propose a privacy-preserving distributed localization algorithm (PP-DILOC) by adopting a noise-adding mechanism under the curious-but-honest model. The performance of localization and privacy preservation of PP-DILOC are both theoretically analyzed. Then, in the presence of dishonest nodes, we propose a resilient PP-DILOC (RPP-DILOC), where a time-varying relax factor and an adversary detection procedure are added into PP-DILOC. Theoretical results provide sufficient conditions for the convergence of RPP-DILOC. The privacy levels and the localization performance in the absence/presence of dishonest nodes are evaluated through numerical and experimental results.