Secure continuous aggregation and load balancing with false temporal pattern identification for wireless sensor networks

Continuous aggregation is required in sensor applications to obtain the temporal variation information of aggregates. It helps the users to understand how the environment changes over time and track real time measurements for trend analysis. In the continuous aggregation, the attacker could manipulate a series of aggregation results through compromised nodes to fabricate false temporal variation patterns of the aggregates. Existing secure aggregation schemes conduct one individual verification for each aggregation result. Due to the high frequency and the long period of a continuous aggregation in every epoch, the false temporal variation pattern would incur a great communication cost. In this paper, we detect and verify a false temporal variations pattern by checking only a small part of aggregation results to reduces a verification cost. A sampling based approach is used to check the aggregation results and we also proposed a security mechanisms to protect the sampling process.