Design of Resource Management and Energy-Efficient Task Scheduling Algorithms for Fog-Empowered Vehicular Networks

Abstract

The delay-sensitive applications, such as self-driving, intelligent transportation, nav igation, and augmented reality assistance, can be evolved in vehicular ad-hoc networks (VANETs) using one of the leading paradigms, fog computing (FC). The demand for these vehicular services has increased due to the emergence of fifth-generation (5G) technology. By blending FC and 5G technologies, the service quality can be improved in intelligent transportation system (ITS) of smart cities. Intelligent vehicles are connected to the road side infrastructure, such as high power nodes (HPNs) and roadside units (RSUs), also called fog nodes (FNs), to obtain on-demand services. These FNs possess finite resources and can provide services to limited vehicles. However, when vehicles reach the network spike in demand, the FNs become impuissant in furnishing services in the existing solutions. As a result, there is a significant reduction in the network service capability and throughput and an increase in the FNs’ energy consumption. Therefore, we propose resource management algorithms such as dynamic resource management (DRM), efficient resource orchestration (ERO) and energy-efficient resource allocation (EERA) to harmonize the resource blocks (RBs) allocation among FNs. Then, to coordinate the allocation of RBs among FNs, the allocated RBs of vehicles in the overlap coverage regions are reduced. This reduction is done by migrating RBs between pairs of FNs to offload upstream services. The problem of reducing RBs among FNs is formulated as integer linear programming (ILP), and its NP-hardness is determined by reducing it from the seminar assignment problem. The proposed algorithm, DRM, considers the set of vehicles in overlapped coverage regions of FNs and communicates with those corresponding FNs. Then, it migrates the RBs of the set of vehicles between pairs of FNs to minimize the allocated RBs. As a result, the network’s service capability is enhanced. The proposed algorithm, ERO, maximizes the network’s throughput by partitioning the FN coverage region into restricted and non restricted coverage regions. Then, it coordinates the allocation of RBs among FNs by reducing RBs for vehicles in the non-restricted coverage regions. A minimum priority queue is constructed using the occupied capacity of FNs to perform optimal migration between pairs of FNs. However, as the vehicles that reach the network grow, FNs’ energy iii consumption increases. Consequently, FNs become futile in delivering services. Therefore, to handle this issue, we present an EERA algorithm to harmonize RB allocation among FNs to reduce the energy utilization of FNs. The proposed algorithm, EERA, relocates the assigned RBs of vehicles in overlap coverage regions amid pairs of FNs, such that the allocated RBs of FNs and energy consumption of FNs are minimized. In ITS, FNs (i.e., HPNs and RSUs) are operated with batteries. FNs are deployed such that the coverage region of each FN intersects with the neighbouring FN(s) to provide services in remote areas where consistent power sources are unavailable. Vehicles in such regions offload delay-sensitive tasks into FNs to get services. However, when the number of vehicles arriving into the network grows over peak hours, the energy dissipation of FNs for processing tasks increases. Consequently, energy-limited FNs become ineffective in delivering services without efficient task scheduling. Therefore, we present reinforcement learning (RL) based energy-efficient and delay-aware (EEDA) task scheduling among FNs in the intersecting regions to reduce the energy dissipation of FNs. The RL agent is trained for different vehicle arrival rates to schedule tasks in a suitable FN of the intersecting areas. The proposed algorithms, DRM, ERO and EERA, are simulated extensively in terms of service capability, serviceability, availability, throughput, energy consumption of FNs and resource utilization. In addition, the simulation results are analogized with benchmark algorithms, such as dynamic resource orchestration (DRO), signal aware (SA), DRO+SA, adaptive resource balance (ARB), minimum cost flow (MCF) and random order (RO), as per their applicability. Similarly, the EEDA algorithm is evaluated by considering FN energy usage, FN response time, and vehicles’ sojourn time in intersecting regions to meet task delay constraints. The simulation outcomes are compared with the priority-aware semi-greedy (PSG), earliest deadline first (EDF), and first come, first serve (FCFS).