A Cluster-Based False Data Filtering Scheme in Wireless Sensor Networks
Zhixiong Liu, Jianxin Wang, Shigeng Zhang, Huafu Liu and Xi Zhang
In wireless sensor networks, the adversaries can inject false data reports from compromised nodes. Previous approaches for false reports filtering, e.g., SEF, only verify the correctness of the MACs carried in each data report on intermediate nodes probabilistically. They cannot filter out fake reports that are forged in a collaborative manner by a group of compromised nodes, if these compromised nodes distribute in different geographical areas. Furthermore, false reports may travel multiple hops before being detected and filtered out, leading to a waste of energy in these schemes. In this paper, we propose a Cluster-based False data Filtering Scheme (CFFS) that can cope with these problems. In CFFS, nodes are grouped into clusters and all cluster heads form a tree rooting at the sink. After deployment, each cluster distributes some of its members’ key indexes to some forwarding cluster heads. Closer forwarding cluster heads are assigned with more authentication keys than further ones to balance the number of keys held by each cluster. Theoretical analysis and simulation results both demonstrate that CFFS outperforms existing schemes in terms of compromise tolerance, filtering efficiency, energy consumption and key distribution balance.
Keywords: Wireless sensor networks; false report filtering; compromise tolerance; filtering efficiency; key distribution balance