Compressed Sensing-Based Clone Identification in Sensor Networks

Abstract

Clone detection, aimed at detecting illegal copies with all of the credentials of legitimate sensor nodes, is of great importance for sensor networks because of the severe impact of clones on network operations, like routing, data collection, and key distribution. Various detection methods have been proposed, but most of them are communication-inefficient due to the common use of the witness-finding strategy. In view of the sparse characteristic of replicated nodes, we propose a novel clone detection framework, called CSI, based on a state-of-the-art signal processing technology, compressed sensing. Specifically, CSI bases its detection effectiveness on the compressed aggregation of sensor readings. Due to its consideration of data aggregation, CSI not only achieves the asymptotically lowest communication cost but also makes the network traffic evenly distributed over sensor nodes. In particular, this is achieved by exploiting the sparse property of the clones within the sensor network caused by the clone attack. The performance and security of CSI will be demonstrated by numerical simulations, analyses, and prototype implementation.