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Energy-Efficient Detection of Compromised Nodes in Wireless Sensor Networks

A wireless sensor network (WSN) can provide a low cost and flexible solution to sensing and monitoring for large distributed applications. To save energy and prolong the network lifetime, the WSN is often partitioned into a set of spatial clusters. Each cluster includes sensor nodes gathering similar data, and just a few sensor nodes (samplers) report their sensed data to a base node. The base node may predict the missed data of non-samplers using the spatial correlation between sensor nodes. The problem is that the WSN is vulnerable to internal security threats such as node compromise. If the samplers are compromised and report incorrect data intentionally, then theWSN will become contaminated rapidly due to the process of data prediction at the base node. In this paper, we propose three algorithms to detect compromised samplers for secure data collection in the WSN. The proposed algorithms leverage the unique property of spatial clustering to alleviate the overhead of compromised node detection. Experiments indicate that the proposed algorithms can identify compromised samplers with a high accuracy and low energy consumption when as many as 50% of the sensor nodes misbehave.