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 (WSNs). The backpressure scheduling is known for being throughput-optimal. In the presence of malicious nodes, the throughput optimality no longer holds. 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, so that the trusted links are scheduled. 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 results show 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.