RFLS - Resilient Fault-proof Localization System in IoT and Crowd-based Sensing Applications

Abstract

In this paper, we consider the problem of event localization in the presence of anomalous nodes, in Internet of Things (IoT) and Mobile Crowd Sensing (MCS) systems. A sensing node could be anomalous due to faultiness in any of its components, or due to maliciousness, where it may forge and inject false readings. In both cases, anomalous nodes can significantly alter the task quality and outcome, which may lead to catastrophic consequences, especially in sensitive applications. The current localization systems are not designed to account for the probability of having anomalous readings, hence subjecting them to high errors. Additionally, current anomaly detection systems are not well suited for localization tasks because they are neither dynamic nor continuous, and they do not account for the radial-spreading patterns of data in localization tasks. To overcome these challenges, a Resilient Fault-proof Localization System (RFLS) is proposed, which a) includes an anomaly detection process designed specifically for localization tasks using means of data-based clustering and centroiding, b) dynamically integrates greedy- and genetic-based active nodes selection, Bayesian-based data fusion, and anomaly detection processes in one full localization system, and c) assesses and updates the nodes’ reputations to ensure better performance in future tasks. The efficacy of the proposed system is validated by running experiments for single and sequential localization tasks, for varying conditions, and by using a real-life dataset of the vehicular mobility traces in the city of Cologne, Germany. The results demonstrate that anomalous nodes are efficiently detected, eliminated, and penalized, which in turn greatly improves the accuracy of the localization tasks.