Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT

SHAO Sujie; LI Yi; GUO Shaoyong; WANG Chenhui; CHEN Xingyu; QIU Xuesong

doi:10.23919/cje.2021.00.312

Volume 32 Issue 1

Jan. 2023

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SHAO Sujie, LI Yi, GUO Shaoyong, et al., “Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 13-25, 2023, doi: 10.23919/cje.2021.00.312

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SHAO Sujie, LI Yi, GUO Shaoyong, et al., “Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT,” Chinese Journal of Electronics, vol. 32, no. 1, pp. 13-25, 2023, doi: 10.23919/cje.2021.00.312

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Delay and Energy Consumption Oriented UAV Inspection Business Collaboration Computing Mechanism in Edge Computing Based Electric Power IoT

doi: 10.23919/cje.2021.00.312

1.
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Blockchain Research Department, China Electronics Standardization Institute, Beijing 100007, China

Funds: This work was supported by the National Natural Science Foundation of China (62071070) and Test Bed Construction of Industrial Internet Platform in Specific Scenes (New Mode)

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Author Bio:
Sujie SHAO was born in 1985. He received the B.E. and Ph.D. degrees in computer science and technology from Beijing University of Posts and Telecommunications, Beijing, China, in 2007 and 2015, respectively. From 2015 to 2018, he was a Postdoctoral Fellow with Beijing University of Posts and Telecommunications. Since 2018, he has been a Lecturer of State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China. His current research interests include edge computing, wireless communications, Internet of things, smart grid and network management. He was awarded the second prize of science and technology progress in Ningxia. (Email: buptssj@bupt.edu.cn)

Yi LI was born in Sichuan Province, China, in 1996. He received the B.E. degree from North China University of Technology, Beijing, China, in 2016. He is currently pursuing the the M.E. degree in Beijing University of Posts and telecommunications, Beijing, China. His research interests include the Internet of things, edge computing and Internet of things security. (Email: yuluo@bupt.edu.cn)

Shaoyong GUO is with the State Key Laboratory of Networking and Switching Technology, and received the Ph.D. degree at Beijing University of Posts and Telecommunication. His research interests include blockchain application technology, distributed intelligence, edge computing, energy Internet, and so on. His main contribution on industrial Internet data sharing theory and technology, including the sharing data complex connection relationship representation model, data sharing network resource collaborative optimization mechanism, cross-domain data security and trusted sharing service mechanism. He is undertaking many key research and development projects and fund projects, and contributed to a number of pioneering standards proposals in ITU-T. And the systems and devices developed by him have large-scale application. He was awarded the second prize of science and technology progress in Henan and Jiangsu Province respectively, the second prize of Science and Technology Progress Award of China Communication Society, and so on. (Email: syguo@bupt.edu.cn)

Chenhui WANG graduated from Nanjing University of Posts and Telecommunications. As the Deputy Director of the Blockchain Research Department of China Electronics Standardization Institute, mainly engaged in blockchain standardization, system testing, and national research project, etc. He is the Chair of IEEE C/BDL Digital Asset Exchange, the Deputy Secretary-Genernal of IEEE PES PSCCC Power Information Coummunication Blockchain Subcommittee, the editor of JTC1 Digital Currenccy Technical Trend Report. (Email: 1094642873@qq.com)

Xingyu CHEN (corresponding author) received the M.S. degree from Northeastern University, Shenyang, China, in 1998. He is currently an Associate Professor with the Beijing University of Posts and Telecommunication engaged in scientific technology research work in communication networks and computer science. His main research interests include big data, communications software, network management, and business intelligence. (Email: chenxy@bupt.edu.cn)

Xuesong QIU was born in 1973. He received the Ph.D. degree from the Beijing University of Posts and Telecommunications, Beijing, China, in 2000. He is currently a Professor and the Ph.D. Supervisor with the State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications. He has authored about 100 SCI/EI index papers. He presides over a series of key research projects on network and service management, including the projects supported by the National Natural Science Foundation and the National HighTech Research and Development Program of China. (Email: xsqiu@bupt.edu.cn)
Received Date: 2021-08-30
Accepted Date: 2022-04-06

Available Online: 2022-07-08

Publish Date: 2023-01-05

Abstract

Abstract

With the development of Internet of things (IoT) technology and smart grid infrastructure, edge computing has become an effective solution to meet the delay requirements of the electric power IoT. Due to the limitation of battery capacity and data transmission mode of IoT terminals, the business collaboration computing must consider the energy consumption of the terminals. Since delay and energy consumption are the optimization goals of two co-directional changes, it is difficult to find a business collaboration computing mechanism that simultaneously minimizes delay and energy consumption. This paper takes the unmanned aerial vehicle (UAV) inspection business scenario in the electric power IoT based on edge computing as the representative, and proposes a two-stage business collaboration computing mechanism including resources allocation and task allocation to optimize the business delay and energy consumption of UAV by decoupling the complex correlation between resource allocation and task allocation. A steepest descent resource allocation algorithm is proposed. On the basis of resource allocation, an improved multiobjective evolutionary algorithm based on decomposition by dynamically adjusting the size of neighborhood and the cross distribution index is proposed as a task allocation algorithm to minimize energy consumption and business delay. Simulation results show that our algorithms can respectively reduce the business delay and energy consumption by more than 6.4% and 9.5% compared with other algorithms.
- Business collaboration computing,
- Task allocation,
- Resource allocation,
- Electric power IoT

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References(32)

References

[1]	Zhao Nan, Lu Weidang, Sheng Min, et al., “UAV-assisted emergency networks in disasters,” IEEE Wireless Communications, vol.26, no.1, pp.45–51, 2019. doi: 10.1109/MWC.2018.1800160
[2]	Zhao Nan, Cheng Fen, Yu F. Richard, et al., “Caching UAV assisted secure transmission in hyper-dense networks based on interference alignment,” IEEE Transactions on Communication, vol.66, no.5, pp.2281–2294, 2018. doi: 10.1109/TCOMM.2018.2792014
[3]	F. Huang, J. Chen, H. Wang, et al., “UAV-assisted SWIPT in Internet of things with power splitting: Trajectory design and power allocation,” IEEE Access, vol.7, pp.68260–68270, 2019. doi: 10.1109/ACCESS.2019.2918135
[4]	S. Y. Derakhshandeh, Z. Mobini, M. Mohammadi, et al., “UAV-assisted fault location in power distribution systems: An optimization approach,” IEEE Transactions on Smart Grid, vol.10, no.4, pp.4628–4636, 2019. doi: 10.1109/TSG.2018.2865977
[5]	L. Lin, X. Liao, H. Jin, et al., “Computation offloading toward edge computing,” Proceedings of the IEEE, vol.107, no.8, pp.1584–1607, 2019. doi: 10.1109/JPROC.2019.2922285
[6]	H. El-Sayed, S. Sankar, M. Prasad, et al., “Edge of things: The big picture on the integration of edge, IoT and the cloud in a distributed computing environment,” IEEE Access, vol.6, pp.1706–1717, 2017. doi: 10.1109/ACCESS.2017.2780087
[7]	Z. Zhou, C. Zhang, C. Xu, et al., “Energy-efficient industrial Internet of UAVs for power line inspection in smart grid,” IEEE Transactions on Industrial Informatics, vol.14, no.6, pp.2705–2714, 2018. doi: 10.1109/TII.2018.2794320
[8]	L. Jiao, A. M. Tulino, J. Llorca, et al., “Smoothed online resource allocation in multi-tier distributed cloud networks,” IEEE/ACM Transactions on Networking, vol.25, no.4, pp.2556–2570, 2017. doi: 10.1109/TNET.2017.2707142
[9]	H. Dong, N. Wu, G. Feng, and X. Gao, “Research on computing task allocation method based on multi-UAVs collaboration,” in Proceedings of 2020 IEEE International Conference on Smart Internet of Things (SmartIoT), Beijing, China, pp.86–93, 2020.
[10]	Y. Li, S. Zhang, J. Chen, T. Jiang, and F. Ye, “Multi-UAV cooperative mission assignment algorithm based on ACO method,” in Proceedings of 2020 International Conference on Computing, Networking and Communications (ICNC), Big Island, HI, USA, pp.304–308, 2020
[11]	X. Wu, Y. Yin, L. Xu, et al., “Multi-UAV task allocation based on improved genetic algorithm,” IEEE Access, vol.9, pp.100369–100379, 2021. doi: 10.1109/ACCESS.2021.3097094
[12]	M. Li, N. Cheng, J. Gao, et al., “Energy-efficient UAV-assisted mobile edge computing: Resource allocation and trajectory optimization,” IEEE Transactions on Vehicular Technology, vol.69, no.3, pp.3424–3438, 2020. doi: 10.1109/TVT.2020.2968343
[13]	Y. Wang, H. Wang, and X. Wei, “Energy-efficient UAV deployment and task scheduling in multi-UAV edge computing,” in Proceedings of 2020 International Conference on Wireless Communications and Signal Processing (WCSP), Nanjing, China, pp.1147–1152, 2020
[14]	N. Kherraf, H. A. Alameddine, and S. Sharafeddine, “Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks,” IEEE Transactions on Network and Service Management, vol.16, no.2, pp.459–474, 2019. doi: 10.1109/TNSM.2019.2894955
[15]	Z. Haitao, D. Yi, Z. Mengkang, et al., “Multipath transmission workload balancing optimization scheme based on mobile edge computing in vehicular heterogeneous network,” IEEE Access, vol.7, pp.116047–116055, 2019. doi: 10.1109/ACCESS.2019.2934770
[16]	Q. Fan and N. Ansari, “Application aware workload allocation for edge computing-based IoT,” IEEE Internet of Things Journal, vol.5, no.3, pp.2146–2153, 2018. doi: 10.1109/JIOT.2018.2826006
[17]	R. Deng, R. Lu, C. Lai, et al., “Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption,” IEEE Internet of Things Journal, vol.3, no.6, pp.1171–1181, 2016. doi: 10.1109/JIOT.2016.2565516
[18]	W. Yu, Y. Huang, and A. Garcia-Ortiz, “Distributed optimal on-line task allocation algorithm for wireless sensor networks,” IEEE Sensors Journal, vol.18, no.1, pp.446–458, 2018. doi: 10.1109/JSEN.2017.2768659
[19]	B. Yang, W. K. Chai, Z. Xu, et al., “Cost-efficient NFV-enabled mobile edge-cloud for low latency mobile applications,” IEEE Transactions on Network and Service Management, vol.15, no.1, pp.475–488, 2018. doi: 10.1109/TNSM.2018.2790081
[20]	C. Zhan, H. Hu, X. Sui, Z. Liu, and D. Niyato, “Completion time and energy optimization in the UAV-enabled mobile-edge computing system,” IEEE Internet of Things Journal, vol.7, no.8, pp.7808–7822, 2020. doi: 10.1109/JIOT.2020.2993260
[21]	C Zhou, W Wu, H He, et al., “Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN,” IEEE Transactions on Wireless Communications, vol.20, no.2, pp.911–925, 2021. doi: 10.1109/TWC.2020.3029143
[22]	M. Guo, L. Li, and Q. Guan, “Energy-efficient and delay-guaranteed workload allocation in IoT-edge-cloud computing systems,” IEEE Access, vol.7, pp.78685–78697, 2019. doi: 10.1109/ACCESS.2019.2922992
[23]	P. Wang, C. Yao, Z. Zheng, et al., “Joint task assignment, transmission, and computing resource allocation in multilayer mobile edge computing systems,” IEEE Internet of Things Journal, vol.6, no.2, pp.2872–2884, 2019. doi: 10.1109/JIOT.2018.2876198
[24]	Q. Li, J. Zhao, Y. Gong, et al., “Energy-efficient computation offloading and resource allocation in fog computing for Internet of everything,” China Communications, vol.16, no.3, pp.32–41, 2019. doi: 10.12676/j.cc.2019.03.004
[25]	M. Guo, Q. Guan, and W. Ke, “Optimal scheduling of containers in queueing cloud computing systems with a heterogeneous workload,” IEEE Access, vol.6, pp.15178–15191, 2018. doi: 10.1109/ACCESS.2018.2801319
[26]	B. Omoniwa, R. Hussain, M. Adil, et al., “An optimal relay scheme for outage minimization in fog-based Internet-of-things (IoT) networks,” IEEE Internet of Things Journal, vol.6, no.2, pp.3044–3-545, 2019. doi: 10.1109/JIOT.2018.2878609
[27]	X. Zhang, Y. Zhou, Q. Zhang, et al., “Problem specific MOEA/D for barrier coverage with wireless sensors,” IEEE Transactions on Cybernetics, vol.47, no.11, pp.3854–3865, 2017. doi: 10.1109/TCYB.2016.2585745
[28]	X. Ma, Q. Zhang, G. Tian, et al., “On Tchebycheff decomposition approaches for multiobjective evolutionary optimization,” IEEE Transaction on Evolutionary Computation, vol.22, no.2, pp.226–244, 2018. doi: 10.1109/TEVC.2017.2704118
[29]	B. Jia, H. Hu, Y. Zeng, T. Xu, et al., “Double-matching resource allocation strategy in fog computing networks based on cost efficiency,” Journal of Communications and Networks, vol.20, no.3, pp.237–246, 2018. doi: 10.1109/JCN.2018.000036
[30]	S. Li, Q. Ni, Y. Sun, et al., “Energy-efficient resource allocation for industrial cyber-physical IoT systems in 5G era,” IEEE Transacions on Industrial Informatics, vol.14, no.6, pp.2618–2628, 2018. doi: 10.1109/TII.2018.2799177
[31]	L. Prestes, M. R. Delgado, R. Lüders, et al., “Boosting the performance of MOEA/D-DRA with a multi-objective hyper-heuristic based on irace and UCB method for heuristic selection,” in Proceedings of IEEE Congress on Evolutionary Computation (CEC), Rio deJaneiro, Brazil, pp.1–8, 2018
[32]	H. Farzin, M. Fotuhi-Firuzabad, M. Moeini-Aghtaie, et al., “A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids,” IEEE Transactions on Smart Grid, vol.8, no.1, pp.117–127, 2017. doi: 10.1109/TSG.2016.2598678