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Vehicular ad hoc network (VANET) is considered as a sub-set of mobile ad hoc network (MANET). VANET can provide road safety by generating collision warning messages before a collision takes place, lane change assistance; can provide efficient traffic system by introducing cooperation among vehicles; and can also improves in infotainment applications like cooperative file accessing, accessing internet, viewing movies etc. It provides smart Transportation System i.e., wireless ad-hoc communication among vehicles and vehicle to roadside equipments. VANET communication broadly distinguished in two types; 1) vehicle to vehicle interaction, 2) vehicle to infrastructure interaction. The main objective of VANET is to provide safe, secure and automated traffic system. For this automated traffic techniques, there are several types of routing protocols has been developed. MANET routing protocols are not equally applicable in VANET. In the recent past Roy and his group has proposed several study in VANET transmission in [1-3]. In this study, we propose a modified AODV routing protocol in the context of VANET with the help of dqueue introduction into the RREQ header. Recently Saha et al [4] has reported the results showing the nature of modified AODV obtained from the rudimentary version of their simulation code. It is mainly based on packet delivery throughput. It shows greater in-throughput information of packet transmission compare to original AODV. Hence our proposal has less overhead and greater performance routing algorithm compared to conventional AODV. In this study, we propose and implement in the NCTUns-6.0 simulator, the neural network based modified dqueue AODV (dqAODV) routing protocol considering Power, TTL, Node distance and Payload parameter to find the optimal route from the source station (vehicle) to the destination station in VANET communications. The detail simulation techniques with result and output will be presented in the conference

AODV Routing Protocol Modification With Dqueue(dqAODV) and Optimization With Neural Network For VANET In City Scenario