Research On Secure Data Aggregation Algorithm In Wireless Sensor Networks

Data aggregation in aggregator nodes is an efficient idea to optimize the consumption of lacking-resources such as bandwidth and energy in wireless sensor networks (WSNs). However, this in-network processing may causing a problem for the privacy of the sensor data since individual data of sensor nodes need to be acquired by the aggregator node before it perform the aggregation operation. In applications of WSNs, providing secure data aggregation has become an important requirement for the sake of sensitive nature of the sensor data. Most of the existing schemes often have some drawbacks for they either having a lower privacy-preserving ability or having a higher energy consumption. For the secure data aggregation in WSNs, we carry out our research from the following two aspects:data confidentiality and data integrity. Data confidentiality ensures the readings of a sensor node is closed to any other nodes, while data integrity protects the aggregation data or results from being corrupted.Aiming at the data confidentiality in data aggregation, we propose a algorithm based on data slicing for the tree-structure network (M-SMART). M-SMART is evolved from the thought of data slicing, mainly for the tree topology networks. In order to make a better control of the communication overhead and computation overhead, M-SMART sets that only the leaf nodes can perform slicing operation, while the non-leaf nodes just need to receive the slices from the leaf nodes or the mixing results from its sub nodes. Theoretical analysis and experimental results show that M-SMART has a good performance of protecting the data privacy. Meanwhile, it takes a reasonable’control of communication overhead and computation overhead also makes the aggregation results reach a higher accuracy.For the integrity of the aggregation data, a novel secure data aggregation algorithm (iM-SMART) is proposed which can protect both data confidentiality and integrity. iM-SMART is evolved from the M-SMART, it applies the Chinese Remainder Theorem to achieve the integrity verification. In iM-SMART, a group of remainders are appended to the mixing result, then the Sink node will recover the aggregation result by using Chinese Remainder Theorem. By comparing the recovery result with the normal aggregation result, the Sink could tell whether the aggregation data is corrupted. The experimental results show that iM-SMART not only ensures the data confidentiality but also recognizes the tamper attack effectively.