Secure Data Aggregation in Wireless Sensor Networks




Roy, Sankardas

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Wireless sensor networks have proved to be useful in several applications, such as environment monitoring and perimeter surveillance. In a large sensor network, in-network data aggregation (i.e., combining partial results at intermediate nodes during message routing) significantly reduces the amount of communication and energy consumption. Recently, the research community has proposed a robust aggregation framework called synopsis diffusion which combines multi-path routing schemes with duplicate-insensitive algorithms to accurately compute aggregates (e.g., Count, Sum) in spite of message losses resulting from node and transmission failures. However, this aggregation framework does not address the problem of false sub-aggregate values contributed by compromised nodes resulting in large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. This is an important problem since sensor networks are highly vulnerable to node compromises due to the unattended nature of sensor nodes and the lack of tamper-resistant hardware. In this dissertation, we make the synopsis diffusion approach secure against attacks in which compromised nodes contribute false sub-aggregate values. In particular, we present two classes of algorithms to securely compute Count or Sum. First, we propose a lightweight verification algorithm which enables the base station to determine if the computed aggregate includes any false contribution. Second, we present attack-resilient computation algorithms which can be used to compute the true aggregate by filtering out the contributions of compromised nodes in the aggregation hierarchy. Thorough theoretical analysis and extensive simulation study show that our algorithms outperform other existing approaches. This dissertation also addresses the security issues of in-network computation of Median and presents verification algorithms and attack-resilient computation algorithms to securely compute an approximate estimate of this aggregate. To the best of our knowledge, prior to this dissertation there was no other work related to the security of in-network computation of Median. We evaluate the performance and cost of our algorithms via both analysis and simulation. The results show that our approach is scalable and efficient.



Sensor Networks, Data Aggregation, Security, In-network Aggregation