Joint optimization of latency and energy consumption for mobile edge computing based proximity detection in road networks

被引：3

作者：

Zhao, Tongyu ^{[1
,2
]}

Liu, Yaqiong ^{[1
,2
]}

Shou, Guochu ^{[1
,2
]}

Yao, Xinwei ^{[3
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, Beijing 100876, Peoples R China

[2] Beijing Lab Adv Informat Networks, Beijing Key Lab Network Syst Architecture & Conve, Beijing 100876, Peoples R China

[3] Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou, Peoples R China

来源：

CHINA COMMUNICATIONS | 2022年 / 19卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Servers; Roads; Image edge detection; Computer architecture; Costs; Energy consumption; Strips; proximity detection; mobile edge computing; road networks; constrained multiobjective optimization;

D O I：

10.23919/JCC.2022.04.020

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene, which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing (MEC) into a constrained multiobjective optimization problem (CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

引用

页码：274 / 290

页数：17

共 28 条

[11]

Kupper A., 2006, ACM SIG- MOBILE Mobile Computing and Communications Review, V10, P1

[12]

Li J, 2018, IEEE WCNC, DOI 10.1109/WCNC.2018.8377343

[13]

Liu Y., 2013, P 21 ACM SIGSPATIAL, P124

[14] Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things [J].

Liu, Yaqiong ;

Peng, Mugen ;

Shou, Guochu ;

Chen, Yudong ;

Chen, Siyu .

IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (08) :6722-6747

[15] Deep Learning Prognostics for Lithium-Ion Battery Based on Ensembled Long Short-Term Memory Networks [J].

Liu, Yuefeng ;

Zhao, Guangquan ;

Peng, Xiyuan .

IEEE ACCESS, 2019, 7 :155130-155142

[16] Mobile Edge Computing: A Survey on Architecture and Computation Offloading [J].

Mach, Pavel ;

Becvar, Zdenek .

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2017, 19 (03) :1628-1656

[17]

Song YL, 2020, IEEE INT CONF COMMUN, P145, DOI [10.1109/icccworkshops49972.2020.9209932, 10.1109/ICCCWorkshops49972.2020.9209932]

[18]

Treu G., 2005, P JOINT WORK SHOP PO

[19]

Treu G., 2006, Proceedings of the 4th ACM international workshop on Mobility management and wireless access, MobiWac'06, P75

[20] Multi-objective optimization of laminated composite beam structures using NSGA-II algorithm [J].

Vo-Duy, T. ;

Duong-Gia, D. ;

Ho-Huu, V. ;

Vu-Do, H. C. ;

Nguyen-Thoi, T. .

COMPOSITE STRUCTURES, 2017, 168 :498-509

← 1 2 3 →