Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing

XIA Jingming; LIU Yufeng; TAN Ling

doi:10.23919/cje.2022.00.159

Article Contents

Article Navigation > Chinese Journal of Electronics > 2024 > Corrected proof

Jingming XIA, Yufeng LIU, and Ling TAN, “Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–10, 2024 doi: 10.23919/cje.2022.00.159

Citation:

Jingming XIA, Yufeng LIU, and Ling TAN, “Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing,” Chinese Journal of Electronics, vol. 33, no. 3, pp. 1–10, 2024 doi: 10.23919/cje.2022.00.159

Citation:

PDF( 6400 KB)

Joint Optimization of Trajectory and Task Offloading for Cellular-Connected Multi-UAV Mobile Edge Computing

doi: 10.23919/cje.2022.00.159

XIA Jingming^1
,,
LIU Yufeng^2
,,
TAN Ling^{2
,
,}

1.
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
Engineering Research Center of Digital Forensics Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China

More Information

Author Bio:
Jingming XIA was born in Jiangsu Province in 1980. He received the B.S. and M.S. degrees in information engineering and the Ph.D. degree in atmospheric science from the Nanjing University of Information Science and Technology, Nanjing, China, in 2005 and 2012, respectively. He is currently an Professor with the Artificial Intelligence Department, Nanjing University of Information Science and Technology. He is the author of two books and more than 30 articles. His research interests include the application of machine learning and deep learning in meteorology. (Email: xiajingming@nuist.edu.cn)

Yufeng LIU was born in Hebei Province in 1996. She received the B.S. degree in information engineering from Hebei GEO University, Shijiazhuang, China, in 2020. Since September 2020, she has been working towards her M.S. degree in the School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China. Her current research interests include unmannedaerial-vehicle communications, mobile-edge computing, and machine learning. (Email: yufengliu@nuist.edu.cn)

Ling TAN was born in Jiangsu Province in 1979. She received the B.S. and M.S. degrees in information engineering from Nanjing Normal University, in 2005, and the Ph.D. degree in information and network from the Nanjing University of Posts and Telecommunications, Nanjing, in 2012. Since 2020, she has been an Professor with the School of Computer and Software, Nanjing University of Information Science and Technology. She is the author of three books and more than 20 articles. Her research interests include the application of machine learning and deep learning in image processing. (Email: cillatan0@nuist.edu.cn)
Corresponding author: Email: cillatan0@nuist.edu.cn
Received Date: 2022-06-02
Accepted Date: 2022-12-09

Available Online: 2023-01-16

Abstract

Abstract

Since the computing capacity and battery energy of unmanned aerial vehicle (UAV) are constrained, UAV as aerial user is hard to handle the high computational complexity and time-sensitive applications. This paper investigates a cellular-connected multi-UAV network supported by mobile edge computing. Multiple UAVs carrying tasks fly from a given initial position to a termination position within a specified time. To handle the large number of tasks carried by UAVs, we propose a energy cost of all UAVs based problem to determine how many tasks should be offloaded to high-altitude balloons (HABs) for computing, where UAV-HAB association, the trajectory of UAV, and calculation task splitting are jointly optimized. However, the formulated problem has nonconvex structure. Hence, an efficient iterative algorithm by applying successive convex approximation and the block coordinate descent methods is put forward. Specifically, in each iteration, the UAV-HAB association, calculation task splitting, and UAV trajectory are alternately optimized. Especially, for the nonconvex UAV trajectory optimization problem, an approximate convex optimization problem is settled. The numerical results indicate that the scheme of this paper proposed is guaranteed to converge and also significantly reduces the entire power consumption of all UAVs compared to the benchmark schemes.
- Unmanned aerial vehicles,
- High-altitude balloons,
- Mobile edge computing,
- Task splitting,
- Block coordinate descent

FullText(HTML)

References(37)

References

[1]	D. Wang, J. Tian, H. X. Zhang, et al., “Task offloading and trajectory scheduling for UAV-enabled MEC networks: An optimal transport theory perspective,” IEEE Wireless Communications Letters, vol. 11, no. 1, pp. 150–154, 2022. doi: 10.1109/LWC.2021.3122957
[2]	J. N. Chen, S. Y. Chen, S. Y. Luo, et al., “An intelligent task offloading algorithm (iTOA) for UAV edge computing network,” Digital Communications and Networks, vol. 6, no. 4, pp. 433–443, 2020. doi: 10.1016/j.dcan.2020.04.008
[3]	G. Y. Wu and J. C. Gu, “Remote interference source localization: A multi-UAV-based cooperative framework,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 442–455, 2022. doi: 10.1049/cje.2021.00.310
[4]	B. Li, Z. S. Fei, and Y. Zhang, “UAV communications for 5G and beyond: Recent advances and future trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, 2019. doi: 10.1109/JIOT.2018.2887086
[5]	L. Yang, H. P. Yao, J. J. Wang, et al., “Multi-UAV-enabled load-balance mobile-edge computing for IoT networks,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6898–6908, 2020. doi: 10.1109/JIOT.2020.2971645
[6]	H. D. Li, F. Fang, and Z. G. Ding, “Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks,” Digital Communications and Networks, vol. 6, no. 3, pp. 241–252, 2020. doi: 10.1016/j.dcan.2020.05.005
[7]	M. Wang, S. Shi, S. S. Gu, et al., “Q-learning based computation offloading for multi-UAV-enabled cloud-edge computing networks,” IET Communications, vol. 14, no. 15, pp. 2481–2490, 2020. doi: 10.1049/iet-com.2019.1184
[8]	X. H. Gu, G. A. Zhang, M. X. Wang, et al., “UAV-aided energy-efficient edge computing networks: Security offloading optimization,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4245–4258, 2022. doi: 10.1109/JIOT.2021.3103391
[9]	L. Sun, L. T. Wan, and X. P. Wang, “Learning-based resource allocation strategy for industrial IoT in UAV-enabled MEC systems,” IEEE Transactions on Industrial Informatics, vol. 17, no. 7, pp. 5031–5040, 2021. doi: 10.1109/TII.2020.3024170
[10]	B. Y. Liu, Y. Y. Wan, F. H. Zhou, et al., “Resource allocation and trajectory design for MISO UAV-assisted MEC networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 5, pp. 4933–4948, 2022. doi: 10.1109/TVT.2022.3140833
[11]	G. S. Yang, L. Hou, X. Y. He, et al., “Offloading time optimization via Markov decision process in mobile-edge computingd,” IEEE Internet of Things Journal, vol. 8, no. 4, pp. 2483–2493, 2021. doi: 10.1109/JIOT.2020.3033285
[12]	B. D. Shang and L. J. Liu, “Mobile-edge computing in the sky: Energy optimization for air-ground integrated networks,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 7443–7456, 2020. doi: 10.1109/JIOT.2020.2987600
[13]	X. F. Chen, C. Wu, Z. Liu, et al., “Computation offloading in beyond 5G networks: A distributed learning framework and applications,” IEEE Wireless Communications, vol. 28, no. 2, pp. 56–62, 2021. doi: 10.1109/MWC.001.2000296
[14]	Y. Q. Li, X. Wang, X. Y. Gan, et al., “Learning-aided computation offloading for trusted collaborative mobile edge computing,” IEEE Transactions on Mobile Computing, vol. 19, no. 12, pp. 2833–2849, 2020. doi: 10.1109/TMC.2019.2934103
[15]	L. Xiao, X. Z. Lu, T. W. Xu, et al., “Reinforcement learning-based mobile offloading for edge computing against jamming and interference,” IEEE Transactions on Communications, vol. 68, no. 10, pp. 6114–6126, 2020. doi: 10.1109/TCOMM.2020.3007742
[16]	J. F. Liu, L. X. Li, F. C. Yang, et al., “Minimization of offloading delay for two-tier UAV with mobile edge computing,” in Proceedings of the 15th International Wireless Communications & Mobile Computing Conference, Tangier, Morocco, pp.1534–1538, 2019.
[17]	X. B. Cao, P. Yang, M. Alzenad, et al., “Airborne communication networks: A survey,” IEEE Journal on Selected Areas in Communications, vol. 36, no. 9, pp. 1907–1926, 2018. doi: 10.1109/JSAC.2018.2864423
[18]	Z. Z. Xu, C. L. Chen, Y. Guo, et al., “Ballooning: An agent-based search strategy in wireless sensor and actor networks,” IEEE Communications Letters, vol. 15, no. 9, pp. 944–946, 2011. doi: 10.1109/LCOMM.2011.072011.110543
[19]	M. Mozaffari, W. Saad, M. Bennis, et al., “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2334–2360, 2019. doi: 10.1109/COMST.2019.2902862
[20]	Y. M. Liu, D. Grace, and P. D. Mitchell, “Exploiting platform diversity for GoS improvement for users with different high altitude platform availability,” IEEE Transactions on Wireless Communications, vol. 8, no. 1, pp. 196–203, 2009. doi: 10.1109/T-WC.2009.070676
[21]	A. Ibrahim and A. S. Alfa, “Using lagrangian relaxation for radio resource allocation in high altitude platforms,” IEEE Transactions on Wireless Communications, vol. 14, no. 10, pp. 5823–5835, 2015. doi: 10.1109/TWC.2015.2443095
[22]	C. Zhan, H. Hu, Z. Liu, et al., “Multi-UAV-enabled mobile-edge computing for time-constrained IoT applications,” IEEE Internet of Things Journal, vol. 8, no. 20, pp. 15553–15567, 2021. doi: 10.1109/JIOT.2021.3073208
[23]	Y. Liu, S. L. Xie, and Y. Zhang, “Cooperative offloading and resource management for UAV-enabled mobile edge computing in power IoT system,” IEEE Transactions on Vehicular Technology, vol. 69, no. 10, pp. 12229–12239, 2020. doi: 10.1109/TVT.2020.3016840
[24]	Z. Y. Yang, S. Z. Bi, and Y. J. A. Zhang, “Dynamic trajectory and offloading control of UAV-enabled MEC under user mobility,” in Proceedings of IEEE International Conference on Communications Workshops, Montreal, QC, Canada, pp.1–6, 2021.
[25]	X. W. Cao, J. Xu, and R. Zhang, “Mobile edge computing for cellular-connected UAV: Computation offloading and trajectory optimization,” in Proceedings of the 19th International Workshop on Signal Processing Advances in Wireless Communications, Kalamata, Greece, pp.1–5, 2018.
[26]	A. A. A. Ateya, A. Muthanna, R. Kirichek, et al., “Energy- and latency-aware hybrid offloading algorithm for UAVs,” IEEE Access, vol. 7, pp. 37587–37600, 2019. doi: 10.1109/ACCESS.2019.2905249
[27]	M. Hua, Y. M. Huang, Y. Sun, et al., “Energy optimization for cellular-connected UAV mobile edge computing systems,” in Proceedings of IEEE International Conference on Communication System, Chengdu, China, pp.1–6, 2018.
[28]	Z. H. Lv, J. J. Hao, and Y. J. Guo, “Energy minimization for MEC-enabled cellular-connected UAV: Trajectory optimization and resource scheduling,” in Proceedings of IEEE Conference on Computer Communications Workshops, Toronto, ON, Canada, pp.478–483, 2020.
[29]	M. Hua, Y. M. Huang, Y. Wang, et al., “Energy optimization for cellular-connected multi-UAV mobile edge computing systems with multi-access schemes,” Journal of Communications and Information Networks, vol. 3, no. 4, pp. 33–44, 2018. doi: 10.1007/s41650-018-0035-0
[30]	C. W. Wang, Y. L. Cui, D. H. Deng, et al., “Trajectory optimization and power allocation scheme based on DRL in energy efficient UAV-aided communication networks,” Chinese Journal of Electronics, vol. 31, no. 3, pp. 397–407, 2022. doi: 10.1049/cje.2021.00.314
[31]	Y. W. Huang, J. Xu, L. Qiu, et al., “Cognitive UAV communication via joint trajectory and power control,” in Proceedings of IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications, Kalamata, Greece, pp.1–5, 2018.
[32]	N. N. Ei, S. W. Kang, M. Alsenwi, et al., “Multi-UAV-assisted MEC system: Joint association and resource management framework,” in Proceedings of the International Conference on Information Networking, Jeju Island, Korea (South), pp.213–218, 2021.
[33]	Y. Xu, T. K. Zhang, D. C. Yang, et al., “Joint resource and trajectory optimization for security in UAV-assisted MEC systems,” IEEE Transactions on Communications, vol. 69, no. 1, pp. 573–588, 2021. doi: 10.1109/TCOMM.2020.3025910
[34]	T. Bai, J. J. Wang, Y. Ren, et al., “Energy-efficient computation offloading for secure UAV-edge-computing systems,” IEEE Transactions on Vehicular Technology, vol. 68, no. 6, pp. 6074–6087, 2019. doi: 10.1109/TVT.2019.2912227
[35]	Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Transactions on Wireless Communications, vol. 18, no. 4, pp. 2329–2345, 2019. doi: 10.1109/TWC.2019.2902559
[36]	S. Yue, J. Ren, N. Qiao, et al., “TODG: Distributed task offloading with delay guarantees for edge computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 7, pp. 1650–1665, 2022. doi: 10.1109/TPDS.2021.3123535
[37]	D. W. Matolak and R. Y. Sun, “Air-ground channel characterization for unmanned aircraft systems-part Ⅲ: The suburban and near-urban environments,” IEEE Transactions on Vehicular Technology, vol. 66, no. 8, pp. 6607–6618, 2017. doi: 10.1109/TVT.2017.2659651