Energy and priority-aware scheduling algorithm for handling delay-sensitive tasks in fog-enabled vehicular networks

Emerging technologies, such as the fifth generation (5G) and the Internet of Things (IoT), increase the communication capabilities of components such as smart vehicles in intelligent transportation systems. Consequently, there is a demand for vehicular services to fulfil the purpose of safe driving and comfort in smart transportation and augmented reality assistants. These vehicular services are delay-sensitive tasks and computation-intensive tasks. Hence, these tasks are not ideal for vehicle processing due to stringent deadlines, finite resource constraints and the battery life of vehicles. Therefore, they are handled by offloading into roadside infrastructures (e.g., roadside units or high power nodes), called fog nodes (FNs), for further processing. However, when the delay-sensitive tasks increase in the network during peak time, the processing of such tasks in FNs poses a challenge regarding meeting deadlines and energy consumption. Therefore, we propose an energy and priority-aware scheduling (EPAS) algorithm to handle the delay-sensitive tasks in the overlap coverage areas of fog-enabled vehicular networks (FEVNs) such that the energy consumption of FNs is reduced while meeting deadlines. Task scheduling among FNs is a multiple 0/1 knapsack, a well-known nondeterministic polynomial (NP)-hard problem. Hence, the EPAS is a greedy-based sub-optimal solution to the task scheduling problem with a finite number of tasks and FNs in FEVNs. The performance of EPAS is evaluated by considering the peak arrival of tasks into the network. The simulation outcomes depict that the EPAS algorithm lowers the FN's energy consumption compared to benchmark algorithms.