Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (2): 42-49.doi: 10.11896/jsjkx.221100123

• Edge Intelligent Collaboration Technology and Frontier Applications • Previous Articles     Next Articles

Coalition Game-assisted Joint Resource Optimization for Digital Twin-assisted Edge Intelligence

LI Xiaohuan1,2, CHEN Bitao1,2, KANG Jiawen2,3 , YE Jin2   

  1. 1 Guangxi University Key Laboratory of Intelligent Networking, Scenario System(Guilin University of Electronic Technology),Guilin, Guangxi 541004,China
    2 Guangxi Research Institute of Integrated Transportation Big Data,Nanning 530025,China
    3 School of Automation,Guangdong University of Technology,Guangzhou 510006,China
  • Received:2022-11-15 Revised:2023-01-11 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    Joint Funds of the National Natural Science Foundation of China(U22A2054),Key Science and Technology Project of Guangxi(AB20238033) and Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing(GXKL06200103)

PDF (PC)

2023

Abstract: In order to cope with the performance loss caused by temporal-spatial resource dispersion of edge service providers (ESPs) in edge intelligence-driven industrial Internet of Things system,this paper proposes a coalition game-based joint resource allocation scheme assisted by digital twin.Firstly,we design a transferable utility coalition game model consisting of a primary problem of utility maximization of edge devices and a sub-problem of utility maximization of ESPs under the constraints of ESPs' resource limitation including bandwidth,computation and cache capabilities.Then,the original multi-objective problem is transformed into one convex problem with linear constraints.Finally,an alternative optimization method is leveraged for solving the equivalent optimization problem.Simulation results show the effectiveness of the proposed coalition game-assisted scheme for improving system resource utilization,with greater promotion as the number of ESPs grows.This proves that the proposed scheme is more adaptable to large scale edge intelligence systems,compared with traditional Nash equilibrium and grand coalition method.

Key words: Industrial Internet of Things, Edge intelligence, Digital twin, Coalition game, Joint resource allocation

CLC Number: 

  • TP301
[1]SISINNI E,SAIFULLAH A,HAN S,et al.Industrial internet of things:Challenges,opportunities,and directions[J].IEEE Transactions on Industrial Informatics,2018,14(11):4724-4734.
[2]ZHAO Y,HOU J,YU C,et al.Study and Application of Industrial Big Data in Production Management and Control[J].Computer Science,2019,46(S1):45-51.
[3]TONG X,ZHANG Z,JIN C,et al.Blockchain for End-Edge-Cloud Architecture:A Survey [J].Chinese Journal of Compu-ters,2021,44(12):2345-2366.
[4]MAHMOOD A,BELTRAMELLI L,ABEDIN S F,et al.Industrial IoT in 5G-and-beyond networks:Vision,architecture,and design trends[J].IEEE Transactions on Industrial Informatics,2022,18(6):4122-4137.
[5]CHEN L,GAO L,REN J,et al.Adaptive Bitrate Streaming for Energy-Efficiency Mobile Augmented Reality[J].Computer Science,2022,49(1):194-203.
[6]LIU M,YANG K,ZHAO N,et al.Intelligent signal classification in industrial distributed wireless sensor networks based industrial internet of things[J].IEEE Transactions on Industrial Informatics,2020,17(7):4946-4956.
[7]FAN S,WU J,TIAN H.Federated learning resource manage-ment for energy-constrained industrial IoT devices [J].Journal on Communications,2022,43(8):65-77.
[8]YUE X,SHI L,ZHAO Y,et al.Dynamic Resource Allocation Strategy for Flink Iterative Jobs [J].Journal of Software,2022,33(3):985-1004.
[9]JI L,HE S,WU W,et al.Dynamic Network Slicing Orchestration for Remote Adaptation and Configuration in Industrial IoT[J].IEEE Transactions on Industrial Informatics,2022,18(6):4297-4307.
[10]CHEN J,WU Q,XU Y,et al.Joint task assignment and spectrum allocation in heterogeneous UAV communication networks:A coalition formation game-theoretic approach[J].IEEE Transactions on Wireless Communications,2020,20(1):440-452.
[11]SIAR H,IZADI M.Offloading Coalition Formation for Scheduling Scientific Workflow Ensembles in Fog Environments[J].Journal of Grid Computing,2021,19(3):1-20.
[12]XU Z,ZHOU L,DAI H,et al.Energy-Aware CollaborativeService Caching in a 5G-Enabled MEC with Uncertain Payoffs[J].IEEE Transactions on Communications,2022,70(2):1058-1071.
[13]DAI Y,ZHANG K,MAHARJAN S,et al.Deep reinforcement learning for stochastic computation offloading in digital twin networks[J].IEEE Transactions on Industrial Informatics,2020,17(7):4968-4977.
[14]LU Y,HUANG X,ZHANG K,et al.Low-latency federatedlearning and blockchain for edge association in digital twin empowered 6G networks[J].IEEE Transactions on Industrial Informatics,2020,17(7):5098-5107.
[15]GUO D,ZHONG R Y,RONG Y,et al.Synchronization of Shop-Floor Logistics and Manufacturing Under IIoT and Digital Twin-Enabled Graduation Intelligent Manufacturing System[J].IEEE Transactions on Cybernetics,2021,9(18):16835-16849.
[16]YANG B,CAO X,BASSEY J,et al.Computation offloading in multi-access edge computing:A multi-task learning approach[J].IEEE Transactions on Mobile Computing,2020,20(9):2745-2762.
[17]XIA S,YAO Z,LI Y,et al.Online distributed offloading and computing resource management with energy harvesting for he-terogeneous MEC-enabled IoT[J].IEEE Transactions on Wireless Communications,2021,20(10):6743-6757.
[18]LI F,YAO H,DU J,et al.Stackelberg game-based computation offloading in social and cognitive industrial Internet of Things[J].IEEE Transactions on Industrial Informatics,2020,16(8):5444-5455.
[19]WU M,YE D,DING J,et al.Incentivizing differentially private federated learning:A multidimensional contract approach[J].IEEE Internet of Things Journal,2021,8(13):10639-10651.
[20]XU Y,XIAO M,TAN H,et al.Incentive Mechanism for Diffe-rentially Private Federated Learning in Industrial IoT[J].IEEE Transactions on Industrial Informatics,2022,18(10):6927-6939.
[21]FENG C,WEN F,YOU S,et al.Coalitional game-based transac-tive energy management in local energy communities[J].IEEE Transactions on Power Systems,2019,35(3):1729-1740.
[22]MU C,WANG K,NI Z,et al.Cooperative differential game-based optimal control and its application to power systems[J].IEEE Transactions on Industrial Informatics,2019,16(8):5169-5179.
[23]LU Y,MAHARJAN S,ZHANG Y.Adaptive edge association for wireless digital twin networks in 6G[J].IEEE Internet of Things Journal,2021,8(22):16219-16230.
[24]LI D,LAN M,HU Y.Energy-saving service management technology of internet of things using edge computing and deep learning[J].Complex & Intelligent Systems,2022,8(5):1-13.
[25]CHEN L,SHEN C,ZHOU P,et al.Collaborative service placement for edge computing in dense small cell networks[J].IEEE Transactions on Mobile Computing,2019,20(2):377-390.
[26]HAN Z,NIYATO D,SAAD W,et al.Game theory in wireless and communication networks:theory,models,and applications[M].Cambridge University Press,2012.
[27]ZHANG J,HU X,NING Z,et al.Joint resource allocation for latency-sensitive services over mobile edge computing networks with caching[J].IEEE Internet of Things Journal,2018,6(3):4283-4294.
[28]ADITYA M V S,SHRIVASTAVA C,KASBEKAR G S.Coalitional Game Framework for Content Distribution Using Device-to-Device Communication[J].IEEE Transactions on Vehicular Technology,2021,70(5):4907-4923.
[1] SHANG Yuye, YUAN Jiabin. Task Offloading Method Based on Cloud-Edge-End Cooperation in Deep Space Environment [J]. Computer Science, 2023, 50(2): 80-88.
[2] Yifei ZOU, Senmao QI, Cong'an XU, Dongxiao YU. Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on Multi-armed Bandit [J]. Computer Science, 2023, 50(2): 13-22.
[3] WANG Xiangwei, HAN Rui, Chi Harold LIU. Hierarchical Memory Pool Based Edge Semi-supervised Continual Learning Method [J]. Computer Science, 2023, 50(2): 23-31.
[4] LIU Xing-guang, ZHOU Li, LIU Yan, ZHANG Xiao-ying, TAN Xiang, WEI Ji-bo. Construction and Distribution Method of REM Based on Edge Intelligence [J]. Computer Science, 2022, 49(9): 236-241.
[5] Ran WANG, Jiang-tian NIE, Yang ZHANG, Kun ZHU. Clustering-based Demand Response for Intelligent Energy Management in 6G-enabled Smart Grids [J]. Computer Science, 2022, 49(6): 44-54.
[6] ZHANG Xiao-mei, CAO Ying, LOU Ping, JIANG Xue-mei, YAN Jun-wei, LI Da. Lossless Data Compression Method Based on Edge Computing [J]. Computer Science, 2022, 49(11A): 210500195-6.
[7] HU Zhao-xia, HU Hai-zhou, JIANG Cong-fengand WAN Jian. Workload Characteristics Based Performance Optimization for Edge Intelligence [J]. Computer Science, 2022, 49(11): 266-276.
[8] LI Bei-bei, SONG Jia-rui, DU Qing-yun, HE Jun-jiang. DRL-IDS:Deep Reinforcement Learning Based Intrusion Detection System for Industrial Internet of Things [J]. Computer Science, 2021, 48(7): 47-54.
[9] WANG Wei-hong, CHEN Zhen-yu. Intelligent Manufacturing Security Model Based on Improved Blockchain [J]. Computer Science, 2021, 48(2): 295-302.
[10] YANG Wei, WANG Qin, WAN Ya-dong and HE Jie. Design of Secure Multi-hop Time Synchronization Protocol for IEEE802.15.4e [J]. Computer Science, 2017, 44(3): 175-181.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.