Mobile Edge Computing for Efficient Vehicle Management in Smart City

Mobile edge computing (MEC) promises to be a useful technology to enable applications in a smart city. However, the task offloading becomes challenging as the MEC infrastructure has limited resources. To solve the challenges of MEC, we have proposed a system architecture with an optimization algorithm to minimize the task delay in MEC. A Genetic Algorithm based task allocation algorithm finds the optimal set of edge servers for task deployment and processing. The proposed system efficiently optimizes the task delay in MEC as analyzed from the experiments performed by taking virtual machines as MEC servers. We show the usefulness of the proposed system by taking an application scenario for efficient vehicle management in a smart city.