Vehicle Authentication-Based Resilient Routing Algorithm With Dynamic Task Allocation for VANETs - Nagaraju Pacharla; K. Srinivasa Reddy (IEEE Access)
Vehicles of the future will have built-in means of communication that will allow them to exchange data with one another. One use of this kind of communication between vehicles that ultimately results in an Intelligent Transport System (ITS) is used by Vehicular Ad hoc Networks (VANETs). Parameters, such as environmental changes, that impact the performance of actual VANET applications were mostly disregarded by most techniques. Both VANET performance and throughput are susceptible to environmental variations. The great dynamism of VANETs makes data routing in such a network a difficult challenge. There are constraints in VANET regarding processing power, bandwidth, time, and high-speed mobility in vehicles. In VANETs, fog computing can significantly improve fog-based service quality. The evolution of cloud computing, the Internet of Things (IoT), and intelligent transportation systems has elevated the significance of fog computing’s integration with VANETs. While transferring tasks to the fog layer, it is essential to meet the vehicles’ needs for low latency and quick response times. With the goal of reducing task offloading delay and improving enhanced reaction time, a VANET-based task scheduling system is proposed after selecting an optimal route in the VANET. Taking into account the limited resources of vehicle nodes, the suggested technique efficiently moves workloads to the fog computing layer. The requirement for means to guarantee the constant and secure functioning of vehicles is directly proportional to their numbers. This research proposes a Vehicle Authentication based Resilient Routing Algorithm with Dynamic Task Allocation (VARRA-DTA) Scheme for selecting the optimal and secured route in VANET with effective task allocation strategy that reduces the latency in the network. The proposed model exhibits better results when contrasted with the traditional models. The proposed model achieved 98.7% accuracy in task scheduling, 99.1% accuracy in route selection and 98.8% accuracy in vehicle authentication. The proposed model exhibits better results in secured route selection and in dynamic task allocation.