MEC (Mobile Edge Computing)-based energy-sensing unloading energy delay compromise proposal under Internet of vehicles

Abstract

The Internet of vehicles distributes computing tasks between a remote cloud and a local vehicle-mounted terminal to improve vehicle services. In order to further reduce the delay and transmission costof computing unloading, the invention provides a cloud-based MEC (Mobile Edge Computing) unloading framework. MEC brings the computing capacity to a mobile network edge close to intelligent mobile equipment; compared with local computing, the MEC contributes to saving energy, but leads to network load increase and transmission delay. In order to research a balance between the energy consumption and the delay, the invention provides an energy-sensing unloading scheme. The scheme is to co-optimize communication and computing resource distribution under limited energy and sensitive delay. In thetext, a multi-cell MEC network scene is considered. Residual energy of a vehicle battery is introduced into a definition of a weighing factor of the energy consumption and the delay. For an MINLP (Mixed Integer Non-Linearity Problem) of the computing unloading and resource distribution, an original NP (Network Performance) difficult problem is decoupled into problems on seeking power and subcarrier distribution and unloading tasks.

Description

technical field [0001] The invention relates to the technical field of wireless short-distance communication in an Internet of Vehicles system, in particular to a computing offloading and resource allocation scheme based on mobile edge computing. Background technique [0002] With the rapid development of wireless communication technology, networked vehicles can utilize vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication forms to form a vehicle ad hoc network (VANET). Large-scale mobile applications are usually served with the assistance of an on-board unit (OBU). However, the limited computing power of OBU brings new challenges to VANET. To effectively address this issue, offloading computing tasks to the cloud with wireless access is considered a promising approach. Such computationally intensive applications require higher computing power and more energy than traditional applications. Typically, vehicle users have limited computing resources (eg,...

AI Technical Summary

Problems solved by technology

[0004] Due to the limited computing resources in the MEC server and severe interference between networks, all tasks cannot be offloaded to the MEC server

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