• Energy Research

We propose a solution for Electric Vehicles (EVs) energy management in smart cities, where a deep learning approach is used to enhance the energy consumption of electric vehicles by trajectory and delay predictions. Two Recurrent Neural Networks are adapted and trained on 60 days of urban traffic. The trained networks show precise prediction of trajectory and delay, even for long prediction intervals. An algorithm is designed and applied on well known energy models for traction and air conditioning. We show how it can prevent from a battery exhaustion. Experimental results combining both RNN and energy models demonstrate the efficiency of the proposed solution in terms of route trajectory and delay prediction, enhancing the energy management.

We propose to utilize rideshare taxis as infrastructure for both communication and computation. Rideshare overlays become hence Mobile Edge Nodes. End-users utilize near rideshare taxis as edge servers to receive video chunks for live video streaming. The set cover problem (SCP) is used to formulate the rideshare taxis coverage optimization inside the city. It provides the maximum number of rideshare taxis that cover end-users routes which guarantee the efficiency of communication services. Simulation results show that the proposed architecture dramatically enhances the quality of service and the overall communication performance in terms of execution time and energy consumption.