Optimization of the Task Allocation Process in VEC with the GWO Bioinspired Algorithm

Abstract

Vehicular Edge Computing (VEC) helps intelligent transportation systems deliver information and process data efficiently, at low latency. However, with the continuous exponential increases in number of interconnected intelligent vehicles, managing massive amounts of data generated in vehicular networks becomes a great challenge. This work proposes ATARY, a method for optimizing task allocation processes in VECs using the Grey Wolf optimization (GWO) algorithm. GWO has been especially adapted to model VEC task allocation as wolves' hunting behaviour. Through a number of vehicle mobility and communication simulations, we show that ATARY is more efficient than some of the most widely used state-of-the-art mechanisms in number of allocated tasks, denied/lost services and resource usage.