Secure Data Aggregation based on Interval Analysis for Wireless Sensor Networks

Data aggregation is an efficient way to prolong the lifetime of wireless sensor networks (WSNs) by reducing communication traffic. However, sensor nodes are usually deployed in harsh or hostile environments. They are easy to be malfunctioning or become compromised nodes, which makes the sensor data unreliable and affects the accuracy of data aggregation. This paper proposes a secure data aggregation algorithm that can detect and exclude abnormal data sent by faulty or compromised nodes. The algorithm can improve reliability of sensor data and accuracy of data aggregation. In clustering phase, the network is divided into several clusters according to spatial correlation. In interval analysis phase, we use bootstrap resample to construct the confidence interval of sensor data, and utilize interval analysis to detect and remove the abnormal data. Experiments results show that the proposed algorithm provides high detection accuracy rate while with very low false alarm ratio.