Energy-efficient computation offloading and caching in vehicular networks

Abstract

Connected Autonomous Vehicles (CAVs) have been considered as a promising technology that provides various smart transportation applications such as autonomous driving, traffic flow prediction, smart authentication systems, and congestion control. These smart applications have computation-intensive tasks which require the processing of a large amount of data generated from various sensors attached to vehicles. The vehicle having less availability of computational resources (referred as a client vehicle) sends computation requests to its nearby roadside unit (RSU) for processing. There are two computing platforms in vehicular networks - i) Vehicular edge computing: RSU acts as an edge server and computes the tasks locally, ii) Vehicular fog computing: RSU offloads computation-intensive tasks to the fog server for further processing. In both computing platforms, one of the most important problems is to reduce the energy consumption of renewable energy-powered RSUs in computing or offloading computation-intensive tasks. In vehicular fog computing, there are two kinds of fog servers - static and dynamic fog servers. Static fog servers are placed at fixed locations inside their corresponding fog zones. Dynamic fog servers include moving or parked vehicles having high processing capabilities. RSUs can be in contact with several fog servers (static and dynamic) at a time and offload computation-intensive tasks to any fog server for processing. Hence, fog servers may have uneven computation load due to varying offloading rates across the road segments which, in turn, increases the latency in the request fulfillment. In this thesis, we propose an energy-efficient scheduling technique across RSUs (ee-IRSA), a federated deep Q-learning-based offloading technique (FedDOVe), and a mobility-aware caching technique (MobiCache) to minimize the energy consumption across RSUs and achieve uniformity in the distribution of computation load across fog servers. The simulation results show that our proposed techniques reduce the energy consumption of RSUs, and improve the load balancing across fog servers as compared to existing techniques.