The presence of malicious nodes in the ad hoc and sensor networks poses serious security attacks during routing which affects the network performance. To address such attacks, numerous researchers have proposed defense techniques using a human behavior pattern called trust. Among existing solutions, direct observations based trust models have gained significant attention in the research community. In this paper, the authors propose a Self Adaptive Trust Model (SATM) of secure geographic routing in wireless sensor networks (WSNs). Unlike conventional weight based trust models, SATM intelligently assigns the weights associated with the network activities. These weights are applied to compute the final trust value. SATM considers direct observations to restrict the reputation based attacks. Due to the flexible and intelligent weight computation, SATM dynamically detects the malicious nodes and direct the traffic towards trustworthy nodes. SATM has been incorporated into Greedy Perimeter Stateless Routing (GPSR) protocol. Simulation results using the network simulator NS-2 have shown that GPSR with SATM is robust against detecting malicious nodes.

In this paper, an improvement over Trusted Greedy Perimeter Stateless Routing (T-GPSR) is presented. T-GPSR employs heuristic weight values to evaluate total trust value of neighboring nodes. However, heuristic assignment of weights provide flexibility but it is not suitable in presence of several security attacks such as Grey hole, selfish behavior, on-off attack etc., are launched in the network in different proportions. To overcome this limitation, an improvement is suggested with an emphasis on trust update, lightweight trust computation and storage to reduce communication and storage overhead. The simulation study indicates that the packet delivery ratio of the improved T-GPSR has improved by 10% over T-GPSR in the presence of 50% of malicious nodes in the network.