Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC

TL;DR

This paper enhances energy efficiency in Smart Internet of Vehicles by integrating MEC, SWIPT, MIMO, and full-duplex technologies, optimizing resource allocation through a novel iterative scheme.

Contribution

It introduces a joint optimization framework for energy efficiency in MEC-enabled IoV systems using a new AIIS algorithm for nonconvex problems.

Findings

AIIS outperforms benchmark schemes in energy efficiency.

Joint optimization improves vehicle battery life and data processing.

MIMO and FD technologies enhance spectrum utilization.

Abstract

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes (eVNs) upload and download data through an anchor node (AN) which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer (SWIPT) technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output (MIMO) and full-duplex (FD) technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.