分析，与其他几种迁移方案相比，本文方案能够获得最低的总能耗以及最高的公平性，并且该方案在不同 环境下都能够保持自身稳定的性能优势。 参考文献 [1]H.Uddin,M.Gibson,G.A.Safdar,et al.IoT for 5G/B5G applications in smart homes,smart cities,wearables and connected cars ll Proceedings of 24th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD).Limassol,2019:1. [2]J.Sun,X.Yao,S.Wang,et al.Blockchain-based secure storage and access scheme for electronic medical records in IPFS.IEEE Access,2020,8:59389. [3]S.Chen,S.Zhang,X.Zheng,et al.Layered adaptive compression design for efficient data collection in industrial wireless sensor networks.Journal of Network and Computer Applications,2019,129:37. [4]L.Lyu,K.Nandakumar,B.Rubinstein,et al.PPFA:privacy preserving fog-enabled aggregation IEEE Transactions on Industrial Informatics.2018,14(8):3733. [5]S.Chen,L.Yang,C.Zhao,et al.Double-blockchain assisted secure and anonymous data fog-enabled smart grid. Engineering,DOI:10.1016/j.eng.2020.06.018. [6]S.Chen,Z.Wang,H.Zhang,G.Yang,et al.Fog-based optimized Kronecker-supported mpression design for industrial IoT. IEEE Transactions on Sustainable Computing,2020,5(1):95. [7]S.E.Esssalhi,M.R.E.Fenni,H.Chafnaji.Smart energy management bled IoT network /Proceedings of ACM International Conference Proceeding Serie.Morocco,2020:1. [8]M.Jia,Z.Yin,D.Li,et al.Toward improved offloading efficieng ransmission in the loT-cloud by leveraging secure truncating OFDM.IEEE Internet of Things Journal,2019,6(3):4252 [9]F.Bonomi,R.Milito,J.Zhu,et al.Fog computing and its role in the Internet of Things /Proceedings of ACM Mobile Cloud Computing Workshop.Helsinki,2012:13. [10]V.Dastjerdi,R.Buyya.Fog computing:helping the Internet of Things realize its potential.Computer,2016,49(8):112. [11]S.Andreev,V.Petrov,K.Huang,et al.Dense moving fog for intelligent IoT:key challenges and opportunities.IEEE Communications Magazine.2019.57(5):34. [12]Q.Wang,S.Chen.Latency-minimum offloading decision and resource allocation for fog-enabled IoT networks.Transactions on Emerging Telecommunications Technologies,2620,31(12):1. [13]K.Guo,M.Sheng,T.Q.S.Quek.etal.Task offloading and scheduling in fog RAN:a parallel communication and computation perspective.IEEE Wireless Compniton Letters,2020,9(2):215. [14]S.Chen,Y.Zheng,W.Lunet al.Energy-optimal dynamic computation offloading for industrial IoT in fog computing.IEEE Transactions on Green Comynunieations and Netorking,2020,4(2):566. [15]Q.Wu,H.Liu,R.Wang,et ah Delay-sensitive task offloading in the 802.11p-based vehicular fog computing systems.IEEE Itermet of Thingsjayhal 7():773. [16]S.Chen,Z.You,X.Ruan.Privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks, IEEE Access,2020,8172424. [17]M.Mukherjee,V.Kumar,S.Kumar,et al.Computation offloading strategy in heterogeneous fog computing with energy and delay constraints /l Proceedings ofIEEE International Conference on Commnications (ICC).Dublin,2020:1. [18]X.He,Y.Chen,K.K.Chai.Delay-aware energy efficient computation offloading for energy harvesting enabled fog radio access networks /Proceedings of 87th IEEE Vehicular Technology Conference (VTC Spring).Porto,2018:1. [19]X.Zhu,S.Chen,S.Chen,et al.Energy and delay co-aware computation offloading with deep learning in fog computing networks II Proceedings of 38th IEEE International Performance Computing and Communications Conference (IPCCC).London,2019:1. [20]M.Xu,W.Wang,M.Zhang,et al.Joint optimization of energy consumption and time delay in energy-constrained fog computing networks /Proceedings of IEEE Global Communications Conference (GLOBECOM).Waikoloa,2019:1.分析，与其他几种迁移方案相比，本文方案能够获得最低的总能耗以及最高的公平性，并且该方案在不同 环境下都能够保持自身稳定的性能优势。 参 考 文 献 [1] H. Uddin, M. Gibson, G. A. Safdar, et al. IoT for 5G/B5G applications in smart homes, smart cities, wearables and connected cars // Proceedings of 24th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). Limassol, 2019: 1. [2] J. Sun, X. Yao, S. Wang, et al. Blockchain-based secure storage and access scheme for electronic medical records in IPFS. IEEE Access, 2020, 8: 59389. [3] S. Chen, S. Zhang, X. Zheng, et al. Layered adaptive compression design for efficient data collection in industrial wireless sensor networks. Journal of Network and Computer Applications, 2019, 129: 37. [4] L. Lyu, K. Nandakumar, B. Rubinstein, et al. PPFA: privacy preserving fog-enabled aggregation in smart grid. IEEE Transactions on Industrial Informatics, 2018, 14(8): 3733. [5] S. Chen, L. Yang, C. Zhao, et al. Double-blockchain assisted secure and anonymous data aggregation for fog-enabled smart grid. Engineering, DOI: 10.1016/j.eng.2020.06.018. [6] S. Chen, Z. Wang, H. Zhang, G. Yang, et al. Fog-based optimized Kronecker-supported compression design for industrial IoT. IEEE Transactions on Sustainable Computing, 2020, 5(1): 95. [7] S. E. Esssalhi, M. R. E. Fenni, H. Chafnaji. Smart energy management for fog-enabled IoT network // Proceedings of ACM International Conference Proceeding Serie. Morocco, 2020: 1. [8] M. Jia, Z. Yin, D. Li, et al. Toward improved offloading efficiency of data transmission in the IoT-cloud by leveraging secure truncating OFDM. IEEE Internet of Things Journal, 2019, 6(3): 4252. [9] F. Bonomi, R. Milito, J. Zhu, et al. Fog computing and its role in the Internet of Things // Proceedings of ACM Mobile Cloud Computing Workshop. Helsinki, 2012: 13. [10] V. Dastjerdi, R. Buyya. Fog computing: helping the Internet of Things realize its potential. Computer, 2016, 49(8): 112. [11] S. Andreev, V. Petrov, K. Huang, et al. Dense moving fog for intelligent IoT: key challenges and opportunities. IEEE Communications Magazine, 2019, 57(5): 34. [12] Q. Wang, S. Chen. Latency-minimum offloading decision and resource allocation for fog-enabled IoT networks. Transactions on Emerging Telecommunications Technologies, 2020, 31(12): 1. [13] K. Guo, M. Sheng, T. Q. S. Quek, et al. Task offloading and scheduling in fog RAN: a parallel communication and computation perspective. IEEE Wireless Communications Letters, 2020, 9(2): 215. [14] S. Chen, Y. Zheng, W. Lu, et al. Energy-optimal dynamic computation offloading for industrial IoT in fog computing. IEEE Transactions on Green Communications and Networking, 2020, 4(2): 566. [15] Q. Wu, H. Liu, R. Wang, et al. Delay-sensitive task offloading in the 802.11p-based vehicular fog computing systems. IEEE Internet of Things Journal, 7(1): 773. [16] S. Chen, Z. You, X. Ruan. Privacy and energy co-aware data aggregation computation offloading for fog-assisted IoT networks, IEEE Access, 2020, 8: 72424. [17] M. Mukherjee, V. Kumar, S. Kumar, et al. Computation offloading strategy in heterogeneous fog computing with energy and delay constraints // Proceedings of IEEE International Conference on Communications (ICC). Dublin, 2020: 1. [18] X. He, Y. Chen, K. K. Chai. Delay-aware energy efficient computation offloading for energy harvesting enabled fog radio access networks // Proceedings of 87th IEEE Vehicular Technology Conference (VTC Spring). Porto, 2018: 1. [19] X. Zhu, S. Chen, S. Chen, et al. Energy and delay co-aware computation offloading with deep learning in fog computing networks // Proceedings of 38th IEEE International Performance Computing and Communications Conference (IPCCC). London, 2019: 1. [20] M. Xu, W. Wang, M. Zhang, et al. Joint optimization of energy consumption and time delay in energy-constrained fog computing networks // Proceedings of IEEE Global Communications Conference (GLOBECOM). Waikoloa, 2019: 1. 录用稿件，非最终出版稿