Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

Qian Wang, Siguang Chen, Meng Wu

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Engineering ›› 2023, Vol. 31 ›› Issue (12) : 127-138. DOI: 10.1016/j.eng.2022.10.014
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Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT

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Abstract

The rapid development of artificial intelligence has pushed the Internet of Things (IoT) into a new stage. Facing with the explosive growth of data and the higher quality of service required by users, edge computing and caching are regarded as promising solutions. However, the resources in edge nodes (ENs) are not inexhaustible. In this paper, we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for IoT, which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls. Specifically, we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN. Furthermore, a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem. In this algorithm, we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training, with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee. Meanwhile, a contribution-based federated aggregation method is developed, in which the aggregation weights of EN gradients are based on their contributions, thereby improving the training effect. Finally, compared with other baseline schemes, the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.

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Keywords

Computation offloading / Caching / Incentive / Blockchain / Federated deep reinforcement learning

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Qian Wang, Siguang Chen, Meng Wu. Incentive-Aware Blockchain-Assisted Intelligent Edge Caching and Computation Offloading for IoT. Engineering, 2023, 31(12): 127‒138 https://doi.org/10.1016/j.eng.2022.10.014

References

[[1]]
Evans D. The Internet of Things:how the next evolution of the Internet is changing everything. Report. San Jose: CISCO; 2011.
[[2]]
S. Chen, X. Zhu, H. Zhang, C. Zhao, G. Yang, K. Wang. Efficient privacy preserving data collection and computation offloading for fog-assisted IoT. IEEE Trans Sustain Comput, 5(4) ( 2020), pp. 526-540
CrossRef Google scholar
[[3]]
Wu F, Liu X, Li H, Fan Q, Zhu L, Wang X, et al. Energy-time efficient task offloading for mobile edge computing in hot-spot scenarios. In:Proceedings of the IEEE International Conference on Communications; 2021 Jun 14-23; Montreal, QC, Canada; 2021.
[[4]]
J. Zhang, X. Hu, Z. Ning, E.C.H. Ngai, L. Zhou, J. Wei, et al.. Joint resource allocation for latency-sensitive services over mobile edge computing networks with caching. IEEE Internet Things J, 6(3) ( 2019), pp. 4283-4294
CrossRef Google scholar
[[5]]
Zhang L, Wu J, Mumtaz S, Li J, Gacanin H,Rodrigues JJPC. Edge-to-edge cooperative artificial intelligence in smart cities with on-demand learning offloading. In:Proceedings of the IEEE Global Communications Conference (GLOBECOM); 2019 Dec 9-13; Waikoloa, HI, USA; 2019.
[[6]]
L. Zhao, K. Yang, Z. Tan, H. Song, A. Al-Dubai, A.Y. Zomaya, et al.. Vehicular computation offloading for industrial mobile edge computing. IEEE Trans Ind Inform, 17(11) ( 2021), pp. 7871-7881
CrossRef Google scholar
[[7]]
F. Zeng, Q. Chen, L. Meng, J. Wu.Volunteer assisted collaborative offloading and resource allocation in vehicular edge computing. IEEE Trans Intell Transp Syst, 22(6) ( 2021), pp. 3247-3357
CrossRef Google scholar
[[8]]
Z. Zhao, C. Feng, H.H. Yang, X. Luo.Federated-learning-enabled intelligent fog radio access networks: fundamental theory, key techniques, and future trends. IEEE Wirel Commun, 27(2) ( 2020), pp. 22-28
CrossRef Google scholar
[[9]]
X. Huang, S. Leng, S. Maharjan, Z. Yan.Multi-agent deep reinforcement learning for computation offloading and interference coordination in small cell networks. IEEE Trans Veh Technol, 70(9) ( 2021), pp. 9282-9293
CrossRef Google scholar
[[10]]
S. Chen, Y. Zheng, W. Lu, V. Varadarajan, K. Wang.Energy-optimal dynamic computation offloading for industrial IoT in fog computing. IEEE Trans Green Commun Netw, 4(2) ( 2020), pp. 566-576
CrossRef Google scholar
[[11]]
R. Malik, M. Vu.On-request wireless charging and partial computation offloading in multi-access edge computing systems. IEEE Trans Wirel Commun, 20(10) ( 2021), pp. 6665-6679
CrossRef Google scholar
[[12]]
Liu Y, He Q, Zheng D, Zhang M, Chen F, Zhang B.Data caching optimization in the edge computing environment. In:Proceedings of the IEEE International Conference on Web Services (ICWS); 2019 Jul 8-13; Milan, Italy; 2019. p. 99-106.
[[13]]
Chen Z, Zhou Z. Dynamic task caching and computation offloading for mobile edge computing. In: Proceedings of the IEEE Global Communications Conference; 2020 Dec 7-11; Taipei, China; 2020.
[[14]]
S. Bi, L. Huang, Y.J.A. Zhang.Joint optimization of service caching placement and computation offloading in mobile edge computing systems. IEEE Trans Wirel Commun, 19(7) ( 2020), pp. 4947-4963
CrossRef Google scholar
[[15]]
G. Zhang, S. Zhang, W. Zhang, Z. Shen, L. Wang.Joint service caching, computation offloading and resource allocation in mobile edge computing systems. IEEE Trans Wirel Commun, 20(8) ( 2021), pp. 5288-5300
CrossRef Google scholar
[[16]]
Ma X, Zhou A, Zhang S, Wang S.Cooperative service caching and workload scheduling in mobile edge computing. In:Proceedings of the IEEE Conference on Computer Communications; 2020 Jul 6-9; Toronto, ON, Canada; 2020. p. 2076-85.
[[17]]
S. Zhong, S. Guo, H. Yu, Q. Wang. Cooperative service caching and computation offloading in multi-access edge computing. Comput Netw, 189 ( 2021), Article 107916
[[18]]
H. Feng, S. Guo, L. Yang, Y. Yang.Collaborative data caching and computation offloading for multi-service mobile edge computing. IEEE Trans Veh Technol, 70(9) ( 2021), pp. 9408-9422
CrossRef Google scholar
[[19]]
P. Yuan, S. Shao, L. Geng, X. Zhao. Caching hit ratio maximization in mobile edge computing with node cooperation. Comput Netw, 200 ( 2021), Article 108507
[[20]]
Y. Liu, C. Xu, Y. Zhan, Z. Liu, J. Guan, H. Zhang. Incentive mechanism for computation offloading using edge computing: a stackelberg game approach. Comput Netw, 129 ( 2017), pp. 399-409
[[21]]
W. Hou, H. Wen, N. Zhang, J. Wu, W. Lei, R. Zhao.Incentive-driven task allocation for collaborative edge computing in industrial Internet of Things. IEEE Internet Things J, 9(`) ( 2022), pp. 706-718
CrossRef Google scholar
[[22]]
Wang Q, Guo S, Wang Y, Yang Y.Incentive mechanism for edge cloud profit maximization in mobile edge computing. In:Proceedings of the IEEE International Conference on Communications (ICC); 2019 May 20-24; Shanghai, China; 2019.
[[23]]
S. Luo, X. Chen, Z. Zhou, X. Chen, W. Wu.Incentive-aware micro computing cluster formation for cooperative fog computing. IEEE Trans Wirel Commun, 19(4) ( 2020), pp. 2643-2657
CrossRef Google scholar
[[24]]
T. Zhang, X. Fang, Y. Liu, G.Y. Li, W. Xu.D2D-enabled mobile user edge caching: a multi-winner auction approach. IEEE Trans Veh Technol, 68(12) ( 2019), pp. 12314-12328
CrossRef Google scholar
[[25]]
Zarandi S, Tabassum H.Federated double deep Q-learning for joint delay and energy minimization in IoT networks. In:Proceedings of the IEEE International Conference on Communications Workshops (ICC Workshops); 2021 Jun 14-23; Montreal, QC, Canada; 2021.
[[26]]
X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, M. Chen.In-edge AI: intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw, 33(5) ( 2019), pp. 156-165
CrossRef Google scholar
[[27]]
J. Ren, H. Wang, T. Hou, S. Zheng, C. Tang.Federated learning-based computation offloading optimization in edge computing-supported Internet of Things. IEEE Access, 7 ( 2019), pp. 69194-69201
CrossRef Google scholar
[[28]]
L. Cui, X. Su, Z. Ming, Z. Chen, S. Yang, Y. Zhou, et al.. CREAT: blockchain-assisted compression algorithm of federated learning for content caching in edge computing. IEEE Internet Things J, 9(16) ( 2022), pp. 14151-14161
CrossRef Google scholar
[[29]]
S. Yu, X. Chen, Z. Zhou, X. Gong, D. Wu.When deep reinforcement learning meets federated learning: intelligent multitimescale resource management for multiaccess edge computing in 5G ultradense network. IEEE Internet Things J, 8(4) ( 2021), pp. 2238-2251
CrossRef Google scholar
[[30]]
S. Chen, L. Yang, C. Zhao, V. Varadarajan, K. Wang. Double-blockchain assisted secure and anonymous data aggregation for fog-enabled smart grid. Engineering, 8 ( 2022), pp. 159-169
[[31]]
M. Hefeeda, O. Saleh. Traffic modeling and proportional partial caching for peer-to-peer systems. IEEE/ACM Trans Netw, 16 (6) ( 2008), pp. 1447-1460
[[32]]
McMahan HB, Moore E, Ramage D, Hampson S, Arcas BA.Communication-efficient learning of deep networks from decentralized data. In:Proceedings of the International Conference on Artificial Intelligence and Statistics; 2017 Apr 20-22; Fort Lauderdale, FL, USA; 2017. p. 1273-82.
[[33]]
X. Wang, C. Wang, X. Li, V.C.M. Leung, T. Taleb.Federated deep reinforcement learning for Internet of Things with decentralized cooperative edge caching. IEEE Internet Things J, 7(10) 2020), pp. 9441-9455
CrossRef Google scholar
[[34]]
Y.H. Guo, Z.C. Zhao, K. He, S.W. Lai, J.J. Xia, L.S. Fan. Efficient and flexible management for Industrial Internet of Things: a federated learning approach. Comput Netw, 192(4) ( 2021), Article 108122
Funding
National Natural Science Foundation of China(61971235); the China Postdoctoral Science Foundation(2018M630590); the Jiangsu Planned Projects for Postdoctoral Research Funds(2021K501C); the 333 High-level Talents Training Project of Jiangsu Province; the 1311 Talents Plan of Nanjing University of Posts and Telecommunications, and the Jiangsu Planned for Postgraduate Research Innovation(KYCX22_1017)
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