A Distributed Trust Evaluation Model for Wireless Mobile Sensor Networks

Abstract

The high-level contribution of this paper is the design and development of a distributed trust evaluation model for data aggregation in mobile sensor networks whose topology changes dynamically with time due to the movement of sensor nodes. Each sensor node maintains an estimate of the trust score of each of its neighbor nodes, based on the beacon data gathered in the neighborhood. Once the estimated trust score for a neighbor node falls below a threshold, the sensor node "locally" classifies its neighbor node as a "Compromised or Faulty" (CF) node. An intermediate node in the data gathering tree discards the aggregated data or individual data received from a child node that the former has classified as a CF node and does not further forward the data up the tree. This way, the erroneous data generated by the CF nodes could be filtered at various levels of the data gathering tree and are prevented from reaching the root node (sink node). We have assessed the effectiveness of our trust evaluation model through a comprehensive simulation study. We observe stability-based data gathering trees to be more suitable to assess the trust levels of the nodes. We also observe that other operating parameters like trust buffer size (the size of the buffer that stores the raw trust scores), transmission range of the sensor nodes, the history weight (importance to be given to the trust scores assessed during the previous associations) and the trust threshold score play a significant role in effectively detecting the presence of CF nodes as well as in reducing the extent to which the aggregated data gets corrupted during its propagation up the data gathering tree.