A cooperative detection model based on artificial neural network for VANET QoS-OLSR protocol

Abstract

In this paper, we address the problem of detecting misbehaving vehicles in Vehicular Ad-Hoc Network using VANET QoS-OLSR, Quality of Service-Optimized Link State Routing protocol. VANET QoS-OLSR is a clustering protocol that is able to increase the stability of the network while maintaining the QoS requirements. However, in this protocol, vehicles can misbehave either by under-speeding or over- speeding the road speed limits after clusters are formed. Such misbehavior leads to a widely disconnected network, which raises the need for a detection mechanism. The majority of the existing detection mechanisms are non-cooperative in the sense that they are based on unilateral judgments, which may be untrustworthy. Others employ cooperative detection scheme with evidence-based aggregation techniques such as the Dempster-Shafer (DS) which suffers from the (1) instability when observations come from dependent sources and (2) absence of learning mechanism. To overcome these limitations, we propose a cooperative method using Artificial Neural Network (ANN), which is able to (1) aggregate judgments and prevent the unilateral decisions, and (2) benefit from the previous detection experience by continuous learning. Simulation results show that our model improves the detection probability and reduces the false alarms rate.