An integrated incentive and trust-based optimal path identification in ad hoc on-demand multipath distance vector routing for MANET

Abstract

A Mobile Ad hoc Network (MANET) can work well only when the mobile nodes behave cooperatively in packet routing. To reduce the hazards from malicious nodes and enhance the security of the network, this paper extends an Ad hoc On-Demand Multipath Distance Vector (AOMDV) routing protocol, named as an Integrated Incentive and Trust-based optimal path identification in AOMDV (IIT-AOMDV) for MANET. The proposed IIT-AOMDV routing protocol integrates an Intrusion Detection System (IDS) with the Bayesian Network (BN) based trust and payment model. The IDS utilises the empirical first-and second-hand trust information of BN, and it underpins the cuckoo search algorithm to map the QoS and trust value into a single fitness metric, tuned according to the presence of malicious nodes. The simulation results show that the IIT-AOMDV improves the detection accuracy and throughput by 20% and 16.6%, respectively, more than that of existing AOMDV integrated with the IDS (AID).