JOURNAL ARTICLE

A vehicle to vehicle relay-based task offloading scheme in Vehicular Communication Networks

Abstract

Vehicular edge computing (VEC) is a potential field that distributes computational tasks between VEC servers and local vehicular terminals, hence improve vehicular services. At present, vehicles’ intelligence and capabilities are rapidly improving, which will likely support many new and exciting applications. The network resources are well-utilized by exploiting neighboring vehicles’ available resources while mitigating the VEC server’s heavy burden. However, due to the vehicles’ mobility, network topology, and the available computing resources change rapidly, which are difficult to predict. To tackle this problem, we investigate the task offloading schemes by utilizing vehicle to vehicle and vehicle to infrastructure communication modes and exploiting the vehicle’s under-utilized computation and communication resources, and taking the cost and time consumption into account. We present a promising relay task-offloading scheme in vehicular edge computing (RVEC). According to this scheme, the tasks are offloaded in a vehicle to vehicle relay for computation while being transmitted to VEC servers. Numerical results illustrate that the RVEC scheme substantially enhances the network’s overall offloading cost.

Keywords:
Relay Computer science Server Vehicular ad hoc network Edge computing Computer network Scheme (mathematics) Task (project management) Enhanced Data Rates for GSM Evolution Computation Mobile edge computing Distributed computing Wireless ad hoc network Wireless Telecommunications Engineering

Metrics

24
Cited By
3.48
FWCI (Field Weighted Citation Impact)
36
Refs
0.92
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

IoT and Edge/Fog Computing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Vehicular Ad Hoc Networks (VANETs)
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Privacy-Preserving Technologies in Data
Physical Sciences →  Computer Science →  Artificial Intelligence

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