Secure and energy-efficient data aggregation with malicious aggregator identification in wireless sensor networks

  • Authors:
  • Hongjuan Li;Keqiu Li;Wenyu Qu;Ivan Stojmenovic

  • Affiliations:
  • School of Computer Science and Technology, Dalian University of Technology, Dalian, China;School of Computer Science and Technology, Dalian University of Technology, Dalian, China;School of Information Science and Technology, Dalian Maritime University, Dalian, China;SITE, University of Ottawa, Ontario, Canada

  • Venue:
  • ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Data aggregation in wireless sensor networks is employed to reduce the communication overhead and prolong the network lifetime. However, an adversary may compromise some sensor nodes, and use them to forge false values as the aggregation result. Previous secure data aggregation schemes have tackled this problem from different angles. The goal of those algorithms is to ensure that the Base Station (BS) does not accept any forged aggregation results. But none of them have tried to detect the nodes that inject into the network bogus aggregation results. Moreover, most of them usually have a communication overhead that is (at best) logarithmic per node. In this paper, we propose a secure and energy-efficient data aggregation scheme that can detect the malicious nodes with a constant per node communication overhead. In our solution, all aggregation results are signed with the private keys of the aggregators so that they cannot be altered by others. Nodes on each link additionally use their pairwise shared key for secure communications. Each node receives the aggregation results from its parent (sent by the parent of its parent) and its siblings (via its parent node), and verifies the aggregation result of the parent node. Theoretical analysis on energy consumption and communication overhead accords with our comparison based simulation study over random data aggregation trees.