Density Mining Based Resilient Data Aggregation for Wireless Sensor Network

Data aggregation is generally used to reduce data streams and save energy consumption in cluster-based wireless sensor networks (CWSNs). However the vulnerable deployment environment of CWSN challenge the data aggregation in terms of data privacy and resiliency. If a node or a group of nodes are compromised or the sensing environment is manipulated by an adversary, the aggregation result will be changed easily. Therefore, it is essential to design a resilient data aggregation scheme with data privacy and security guarantees. This paper proposes a scheme that provides privacy-preserving data fusion, and tolerate data disruption and node compromise as well. We make use of subgroup within each cluster to improve the resiliency against node compromise; and we employ a novel encryption algorithm that support secure comparison between concealed data, which will be further used for density mining against the manipulating data disruption. The simulation results show that this scheme can preserve relatively accurate average aggregation with malicious data filtering. And the mathematical evaluation and comparison show better effectiveness and fitness of our scheme for CWSN in terms of fault tolerance and process efficiency than the previous data aggregation schemes.