RDAS: Reputation-Based Resilient Data Aggregation in Sensor Network

Data aggregation in wireless sensor networks is vulnerable to security attacks and natural failures. A few nodes can drastically alter the result of the aggregation by reporting erroneous data. In this paper we present RDAS, a robust data aggregation protocol that uses a reputation based approach to identify and isolate malicious nodes in a sensor network. RDAS is based on a hierarchical clustering arrangement of nodes, where a cluster head analyzes data from the cluster nodes to determine the location of an event. It uses the redundancy of multiple nodes sensing an event to determine what data should have been reported by each node. Nodes form part of a distributed reputation system, where they share information about other node´s performance in reporting accurate data and use the reputation ratings to suppress reports from malicious nodes. RDAS is able to perform accurate data aggregation in the presence of individually malicious and colluding nodes, as well as nodes that try to compromise the integrity of the reputation system by lying about other nodes´ behavior. We show that RDAS is more resilient to security attacks with respect to accuracy of event localization than the baseline data aggregation protocol with no security feature.