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All issues Volume 31 / No 1 (February 2026) Wuhan Univ. J. Nat. Sci., 31 1 (2026) 69-78 References
Open Access
Issue
Wuhan Univ. J. Nat. Sci.
Volume 31, Number 1, February 2026
Page(s) 69 - 78
DOI https://doi.org/10.1051/wujns/2026311069
Published online 06 March 2026
  1. Schurgot M R, Wang M, Conway A E, et al. A dispersed computing architecture for resource-centric computation and communication[J]. IEEE Communications Magazine, 2019, 57(7): 13-19. [Google Scholar]
  2. Wu H J, Liu F, Liu B, et al. Dispersed computing: Technologies, applications and challenges[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(5): 721-730(Ch). [Google Scholar]
  3. Sun Y X. Enhancing Anonymity Systems Under Network and User Dynamics[D]. Princeton: Princeton University, 2020. [Google Scholar]
  4. Michel O, Sonchack J, Keller E, et al. Packet-level analytics in software without compromises[C]//USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 2013). Santa Clara: USENIX Association, 2013: 1-6. [Google Scholar]
  5. Zaki Y, Pötsch T, Ahmad T. Application-level congestion control: overcoming TCP's limitations in cellular networks[C]//Proceedings of the 2018 USENIX Annual Technical Conference (USENIX ATC 2018). Boston: USENIX Association, 2018: 1-14. [Google Scholar]
  6. Hui H W. Research on Cybersecurity Model and Algorithm in Dispersed Computing Environment[D]. Beijing: University of Science and Technology Beijing, 2023(Ch). [Google Scholar]
  7. Ghosh P, Nguyen Q, Krishnamachari B. Container orchestration for dispersed computing[C]//Proceedings of the 5th International Workshop on Container Technologies and Container Clouds. New York: ACM, 2019: 19-24. [Google Scholar]
  8. Yang H G, Li G, Sun G Y, et al. Dispersed computing for tactical edge in future wars: Vision, architecture, and challenges[J]. Wireless Communications and Mobile Computing, 2021, 2021(1): 8899186. [Google Scholar]
  9. An X M, Fan R F, Hu H, et al. Joint task offloading and resource allocation for IoT edge computing with sequential task dependency[J]. IEEE Internet of Things Journal, 2022, 9(17): 16546-16561. [Google Scholar]
  10. Li Y H, Jiang C S. Distributed task offloading strategy to low load base stations in mobile edge computing environment[J]. Computer Communications, 2020, 164: 240-248. [Google Scholar]
  11. Song S D, Ma S Y, Zhao J M, et al. Cost-efficient multi-service task offloading scheduling for mobile edge computing[J]. Applied Intelligence, 2022, 52(4): 4028-4040. [Google Scholar]
  12. Kuang Z F, Chen Q L, Li L F, et al. Multi-user edge computing task offloading scheduling and resource allocation based on deep reinforcement learning[J]. Chinese Journal of Computers, 2022, 45(4): 812-824(Ch). [Google Scholar]
  13. Sacco A, Esposito F, Marchetto G, et al. Sustainable task offloading in UAV networks via multi-agent reinforcement learning[J]. IEEE Transactions on Vehicular Technology, 2021, 70(5): 5003-5015. [Google Scholar]
  14. Seid A M, Boateng G O, Mareri B, et al. Multi-agent DRL for task offloading and resource allocation in multi-UAV enabled IoT edge network[J]. IEEE Transactions on Network and Service Management, 2021, 18(4): 4531-4547. [Google Scholar]
  15. Niu Z C, Liu H, Lin X M, et al. Task scheduling with UAV-assisted dispersed computing for disaster scenario[J]. IEEE Systems Journal, 2022, 16(4): 6429-6440. [Google Scholar]
  16. Poylisher A, Cichocki A, Guo K, et al. Tactical Jupiter: Dynamic scheduling of dispersed computations in tactical MANETs[C]//MILCOM 2021 IEEE Military Communications Conference (MILCOM). New York: IEEE, 2021: 102-107. [Google Scholar]
  17. Yang C S, Avestimehr A S, Pedarsani R. Communication-aware scheduling of serial tasks for dispersed computing[C]//2018 IEEE International Symposium on Information Theory (ISIT). New York: IEEE, 2018: 1226-1230. [Google Scholar]
  18. Hu D Y, Krishnamachari B. Throughput optimized scheduler for dispersed computing systems[C]//2019 7th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud). New York: IEEE, 2019: 76-84. [Google Scholar]
  19. Wang Q, Mei X X, Liu H, et al. Energy-aware non-preemptive task scheduling with deadline constraint in DVFS-enabled heterogeneous clusters[J]. IEEE Transactions on Parallel and Distributed Systems, 2022, 33(12): 4083-4099. [Google Scholar]
  20. Wang Y Y, Huang J R. Efficient space-time signal processing scheme of frequency synchronization and positioning for sensor networks[J]. Sensors, 2023, 23(4): 2115. [Google Scholar]
  21. Yang G S, Wang B Y, He X Y, et al. Competition-congestion-aware stable worker-task matching in mobile crowd sensing[J]. IEEE Transactions on Network and Service Management, 2021, 18(3): 3719-3732. [Google Scholar]
  22. Ma G F, Li H R, Wang X W, et al. Mobility-aware task splitting and computation resource allocation for distributed multi-access edge computing enabled vehicular network[C]//2021 International Conference on Mechanical, Aerospace and Automotive Engineering. New York: ACM, 2021: 164-170. [Google Scholar]
  23. Wang G Y, Yu X B, Xu F C, et al. Task offloading and resource allocation for UAV-assisted mobile edge computing with imperfect channel estimation over Rician fading channels[J]. EURASIP Journal on Wireless Communications and Networking, 2020, 2020(1): 169. [Google Scholar]
  24. Guo K, Gao R F, Xia W C, et al. Online learning based computation offloading in MEC systems with communication and computation dynamics[J]. IEEE Transactions on Communications, 2021, 69(2): 1147-1162. [Google Scholar]
  25. Chen C, Zeng Y N, Li H, et al. A multihop task offloading decision model in MEC-enabled Internet of vehicles[J]. IEEE Internet of Things Journal, 2023, 10(4): 3215-3230. [Google Scholar]
  26. Topcuoglu H, Hariri S, Wu M Y. Performance-effective and low-complexity task scheduling for heterogeneous computing[J]. IEEE Transactions on Parallel and Distributed Systems, 2002, 13(3): 260-274. [Google Scholar]
  27. Holland J H. Genetic algorithms[J]. Scientific American, 1992, 267(1): 66-73. [CrossRef] [PubMed] [Google Scholar]

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