Trust-based backpressure routing in wireless sensor networks

In this paper, we apply a Vector AutoRegression (VAR) based trust model over the Backpressure Collection Protocol (BCP), a collection mechanism based on dynamic backpressure routing in Wireless Sensor Networks (WSN) and show that the VAR trust model is suited for resource constraint networks. The backpressure scheduling is known for being throughput-optimal. However, it is usually assumed that nodes cooperate with each other to forward the network traffic. In the presence of malicious nodes, the throughput optimality no longer holds and this affects the network performance in collection tree applications of sensor networks. We apply an autoregression based scheme to embed trust into the link weights, making it more likely for trusted links to be scheduled. The novelty in our approach is that the notion of trust can be easily incorporated in a new state of the art distributed and dynamic routing Backpressure Collection Protocol in sensor networks. We have evaluated our work in a real sensor network testbed and shown that by carefully setting the trust parameters, substantial benefit in terms of throughput can be obtained with minimal overheads. Our performance analysis of VAR in comparison with other existing trust models demonstrate that even when 50% of network nodes are malicious, VAR trust offers approximately 73% throughput and ensures reliable routing, with a small trade-off in the end-to-end packet delay and energy consumptions.