Joint Wireless Resource and Computation Offloading Optimization for Energy Efficient Internet of Vehicles

Abstract

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability.

Task Allocation Methods and Optimization Techniques in Edge Computing: A Systematic Review of the Literature

“…Context-aware task allocation [52] DFD DAG [69] GT IoT, 5G [78] LPA MEC Energy-efficient task allocation [68] MAPE-K IoT [11] Lyapunov MEC [19] Bi-Level MEC [25] DRL IoV [30] QT 5G [43] PSO IoT [47] ILP MEC [71] JTORA MEC [73] hybrid RF-FSO Industrial IoT Dynamic task allocation [13] MPSO VVECNs, VANETs [79] MH IoV [20] AA Fog [23] DP MEC [41] DRL IoT [44] ECTA EC [46] AA Fog [49] ILP [56] GA, GEN IoT [60] PSO EC [64] BPSO Table 1. Cont.…”

Computing Offloading Decision Based on Adaptive Estimation of Distribution Algorithm in Internet of Vehicles

“…Furthermore, A hybrid computing strategy for the mobile edge computing is provided (Wang L, et al 2021) to minimize the energy consumption of terminal devices, and a hybrid online mothed for computing offload adopting deep learning method is presented. To meet the intensive computation demands, an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision through decoupling it into subproblems and leveraging the block coordinate descent (D. Pliatsios, P. Sarigiannidis, T. Lagkas, et al 2022). Xiong et al (2020) designed an optimization algorithm for the allocation of network resources and computing resources to minimize the transmission delay and computation time.…”

Task Offloading in Multi-Hop Relay-Aided Multi-Access Edge Computing

“…in terms of their computing capability, storage capacity, and battery power [4], [5]. Moreover, through effective computation offloading in MEC, end-to-end (e2e) latency for emerging capability-demanding or latency-sensitive applications can be drastically reduced, ultimately providing high quality-ofservices to end users [6], [7].…”