Digital Twin Assisted Edge Computing Task Offloading and Resource Allocation Strategy in Industrial Internet of Things

doi:10.12141/j.issn.1000-565X.240262

Abstract

Abstract:

In the industrial Internet of Things, the reliability of mobile edge computing largely depends on the wireless channel conditions. In order to process the influence of imperfect channel state information to the system, this paper proposed a digital twin assisted mobile edge computing energy consumption optimization method. For the task offloading problem in industrial Internet of Things, a digital twin model of devices and channels in the edge computing system was established. Considering imperfect channel state information, the joint optimization of offloading decisions, transmission power, channel resources, and computational resources is performed with the aim of minimizing the total system energy consumption. To deal with the proposed nonlinear non convex problem of mixed integers, the probabilistic delay constraint was transformed and the original problem was decomposed into two sub-problems, and a joint optimization algorithm with the assistance of digital twins based on continuous convex approximation was proposed. Firstly, the original problem was relaxed to obtain resource allocation schemes and task offloading priorities. Then, the offloading priority of each terminal device was sorted in descending order. The complete task offloading scheme was obtained by solving the iterative optimization problem. Finally, simulation results show that, compared to other benchmark schemes, the proposed computational offloading optimization scheme significantly reduces the total energy consumption of the system.

Key words: mobile edge computing, task offloading, digital twin network, imperfect channel state information

CLC Number:

TN929.5

LI Song, LI Yiming, LI Shun. Digital Twin Assisted Edge Computing Task Offloading and Resource Allocation Strategy in Industrial Internet of Things[J]. Journal of South China University of Technology(Natural Science Edition), 2025, 53(3): 88-96.

Figures/Tables 5

References 26

1	GONG Y， YAO H， WANG J，et al ．Edge intelligence-driven joint offloading and resource allocation for future 6G industrial internet of things［J］．IEEE Transactions on Network Science and Engineering，2024，11（6）：5644-5655.
2	姚锡凡，蓝宏宇，陶韬，等．基于云雾结合的工件深度学习识别问题研究［J］．华南理工大学学报（自然科学版），2019，47（12）：1-8.
	YAO Xifan， LAN Hongyu， TAO Tao，et al ．Deep-learning recognition of workpieces based on cloud and fog computing［J］．Journal of South China University of Technology（Natural Science Edition），2019，47（12）：1-8.
3	ADREANI L， COLOMBO C， FANFANI M，et al ．Rendering 3D city for smart city digital twin［C］∥ Proceedings of 2022 IEEE International Conference on Smart Computing．Helsinki：IEEE，2022：183-185.
4	CHIANG M， ZHANG T ．Fog and IoT：an overview of research opportunities［J］．IEEE Internet of Things Journal，2016，3（6）：854-864.
5	BASTUG E， BENNIS M， DEBBAH M ．Living on the edge：the role of proactive caching in 5G wireless networks［J］．IEEE Communications Magazine，2014，52（8）：82-89.
6	MAO Y， YOU C， ZHANG J ．A survey on mobile edge computing：the communication perspective［J］．IEEE Communications Surveys & Tutorials，2017，19（4）：2322-2358.
7	ZHANG W， WEN Y， GUAN K，et al ．Energy-optimal mobile cloud computing under stochastic wireless channel［J］．IEEE Transactions on Wireless Communications，2013，12（9）：4569-4581.
8	WANG Y， SHENG M， WANG X，et al ．Mobile-edge computing： partial computation offloading using dynamic voltage scaling［J］．IEEE Transactions on Communications， 2016，64（10）：4268-4282.
9	ALE L， KING S A， ZHANG N，et al ．D3PG：dirichlet DDPG for task partitioning and offloading with constrained hybrid action space in mobile-edge computing［J］．IEEE Internet of Things Journal，2022，9（19）：19260-19272.
10	张海波，李虎，陈善学，等．超密集网络中基于移动边缘计算的任务卸载和资源优化［J］．电子与信息学报，2019，41（5）：1194-1201.
	ZHANG Haibo， LI Hu， CHEN Shanxue，et al ．Computing offloading and resource optimization in ultra-dense networks with mobile edge computation［J］．Journal of Electronics & Information Technology，2019，41（5）：1194-1201.
11	张先超，任天时，赵耀，等．移动边缘计算时延与能耗联合优化方法［J］．电子科技大学学报，2022，51（5）：737-742.
	ZHANG Xianchao， REN Tianshi， ZHAO Yao，et al ．Joint optimization method of energy consumption and time delay for mobile edge computing［J］．Journal of University of Electronic Science and Technology of China，2022，51（5）：737-742.
12	WANG F， QIN R， LI J，et al ．Parallel societies：a computing perspective of social digital twins and virtual-real interactions［J］．IEEE Transactions on Computational Social Systems，2020，7（1）：2-7.
13	JIANG L， ZHENG H， TIAN H，et al ．Cooperative federated learning and model update verification in blockchain-empowered digital twin edge networks［J］．IEEE Internet of Things Journal，2022，9（13）：11154-11167.
14	SUN W， ZHANG H， WANG R，et al ．Reducing offloading latency for digital twin edge networks in 6G［J］．IEEE Transactions on Vehicular Technology，2020，69（10）：12240-12251.
15	LIU T， TANG L， WANG W，et al ．Digital-twin-assisted task offloading based on edge collaboration in the digital twin edge network［J］．IEEE Internet of Things Journal，2022，9（2）：1427-1444.
16	CUI H， ZHANG R， SONG L，et al ．Capacity analysis of bidirectional AF relay selection with imperfect channel state information［J］．IEEE Wireless Communications Letters，2013，2（3）：255-258.
17	LIU W， WANG H， ZHANG X，et al ．Joint trajectory design and resource allocation in UAV-enabled heterogeneous MEC systems［J］．IEEE Internet of Things Journal，2024，11（19）：30817-30832.
18	SHEN Y， WANG C， ZANG W，et al ．Outage constrained max-min secrecy rate optimization for IRS-aided SWIPT systems with artificial noise［J］．IEEE Internet of Things Journal，2024，11（6）：9814-9828.
19	WANG J， FENG D， ZHANG S，et al ．Joint computation offloading and resource allocation for MEC-enabled IoT systems with imperfect CSI［J］．IEEE Internet of Things Journal，2021，8（5）：3462-3475.
20	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，2021，17（7）：4968-4977.
21	VAN HUYNH D， KHOSRAVIRAD S R， MASARACCHIA A，et al ．Edge intelligence-based ultra-reliable and low-latency communications for digital twin-enabled metaverse［J］．IEEE Wireless Communications Letters，2022，11（8）：1733-1737.
22	DINH T Q， TANG J， LA Q D，et al ．Offloading in mobile edge computing：task allocation and computational frequency scaling［J］．IEEE Transactions on Communications，2017，65（8）：3571-3584.
23	LIU A， LAU V K N， KANANIAN B ．Stochastic successive convex approximation for non-convex constrained stochastic optimization［J］．IEEE Transactions on Signal Processing，2019，67（16）：4189-4203.
24	YANG Y， SCUTARI G， PALOMAR D P，et al ．A parallel decomposition method for nonconvex stochastic multi-agent optimization problems［J］．IEEE Transactions on Signal Processing，2016，64（11）：2949-2964.
25	DU J， ZHAO L， FENG J，et al ．Computation offloading and resource allocation in mixed fog/cloud computing systems with min-max fairness guarantee［J］．IEEE Transactions on Communications，2018，66（4）：1594-1608.
26	ZHANG H， LIU X， XU Y，et al ．Partial offloading and resource allocation for MEC-assisted vehicular networks［J］．IEEE Transactions on Vehicular Technology，2024，73（1）：1276-1288.