Enhancing trust and efficiency in mobile edge computing with hybrid black widow-based updated jellyfish search optimization for task scheduling
DOI:
https://doi.org/10.65746/jbrha116Keywords:
mobile edge computing (MEC); task scheduling, offloading strategy; black widow optimization (BW); updated jellyfish search (UJS); resource allocation; energy efficiency; federated learning (FL); trust evaluationAbstract
Task scheduling in Mobile Edge Computing (MEC) is a challenging multi-objective optimization problem, where conflicting goals such as minimizing latency, reducing energy consumption, and controlling execution cost must be achieved under uncertain and unreliable resource conditions. This study proposes a Federated Learning (FL)-based trust evaluation framework combined with a novel hybrid metaheuristic, the Black Widow-Updated Jellyfish Search (BW-UJS) algorithm. By integrating the exploration-exploitation mechanisms of Black Widow Optimization with the adaptive search behavior of Updated Jellyfish Search, the proposed method enhances decision-making in complex scheduling environments. The problem is formulated as a multi-objective optimization model that incorporates trust constraints to ensure reliable resource provider selection. Computational experiments conducted on synthetic MEC offloading scenarios demonstrate that BW-UJS consistently outperforms benchmark algorithms (Original, Offload, MUCAO, FLO), achieving up to 2.1 % improvement in energy efficiency, 3 % reduction in execution cost, and 0.01 % decrease in delay. The findings highlight the effectiveness of BW-UJS as a robust optimization approach for task scheduling in distributed systems. Future work will focus on extending the method with real-time adaptive learning mechanisms to address dynamic and large-scale network conditions.
References
1. Xie B, Cui H. Deep reinforcement learning-based dynamical task offloading for mobile edge computing: B. Xie, H. Cui. The Journal of Supercomputing. 2025; 81(1): 35. doi: 10.1007/s11227-024-06603-x
2. Djebrouni Y, Benarba N, Touat O, et al. Bias mitigation in federated learning for edge computing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2024; 7(4): 1–35. doi: 10.1145/3631455
3. Bai J, Dong H, Bouguettaya A. Fedq-trust: Efficient data-driven trust prediction for mobile edge-based iot systems. ArXiv Preprint ArXiv:2404.18356, 2024. doi: 10.48550/arXiv.2404.18356
4. Zhao F, Ying C, Zhang Y, et al. “Dynamic offloading and resource scheduling for mobile-edge computing with energy harvesting devices.” IEEE Transactions on Network and Service Management. 2021; 18(2): 2154–2165. doi: 10.1109/TNSM.2021.3069993
5. Zhou H, Jiang K, Liu X, et al. Deep reinforcement learning for energy-efficient computation offloading in mobile-edge computing. IEEE Internet of Things Journal. 2021; 9(2): 1517–1530. doi: 10.1109/JIOT.2021.3091142
6. Deng X, Liu J, Wang L, et al. A trust evaluation system based on reputation data in mobile edge computing network. Peer-to-Peer Networking and Applications. 2020; 13(5): 1744–1755. doi: 10.1007/s12083-020-00889-3
7. Cao K, Liu Y, Meng G, et al. An overview on edge computing research. IEEE Access. 2020; 8: 85714–85728. doi: 10.1109/ACCESS.2020.2991734
8. Kong X, Wu Y, Wang H, et al. Edge computing for internet of everything: A survey. IEEE Internet of Things Journal. 2022; 9(23): 23472–23485.
9. Abbas N, Zhang Y, Taherkordi A, et al. Mobile edge computing: A survey. IEEE Internet of Things Journal. 2017; 5(1): 450–465. doi: 10.1109/JIOT.2017.2750180
10. Liang Z, Liu Y, Lok T M, et al. Multi-cell mobile edge computing: Joint service migration and resource allocation. IEEE Transactions on Wireless Communications. 2021; 20(9): 5898–5912. doi: 10.1109/TWC.2021.3070974
11. Lin Q, Jiang S, Zhen Z, et al. Fed-PEMC: A privacy-enhanced federated deep learning algorithm for consumer electronics in mobile edge computing. IEEE Transactions on Consumer Electronics. 2024; 70(1): 4073–4086. doi: 10.1109/TCE.2024.3351648
12. Mazloomi F, Shah Heydari S, El-Khatib K. Trust-based knowledge sharing among federated learning servers in vehicular edge computing. Proceedings of the Int’l ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications; 2023. pp. 9–15. doi: 10.1145/3616392.3624701
13. Xiang T, Bi Y, Chen X, et al. Federated learning with dynamic epoch adjustment and collaborative training in mobile edge computing. IEEE Transactions on Mobile Computing. 2023; 23(5): 4092–4106. doi: 10.1109/TMC.2023.3288392
14. Kuang Z, Li L, Gao J, et al. Partial offloading scheduling and power allocation for mobile edge computing systems. IEEE Internet of Things Journal. 2019; 6(4): 6774–6785. doi: 10.1109/JIOT.2019.2911455
15. Chen Y, Zhang N, Zhang Y, et al. TOFFEE: Task offloading and frequency scaling for energy efficiency of mobile devices in mobile edge computing. IEEE Transactions on Cloud Computing. 2019; 9(4): 1634–1644. doi: 10.1109/TCC.2019.2923692
16. Mao Y, Zhang J, Letaief K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications. 2016; 34(12): 3590–3605. doi: 10.1109/JSAC.2016.2611964
17. Jiang H, Dai X, Xiao Z, et al. Joint task offloading and resource allocation for energy-constrained mobile edge computing. IEEE Transactions on Mobile Computing. 2022; 22(7): 4000–4015. doi: 10.1109/TMC.2022.3150432
18. Wang J, Li Z, Liu H, et al. A trust-based computation offloading framework in mobile cloud-edge computing networks. IEEE Transactions on Mobile Computing. 2025; 24(6): 5370–5385. doi: 10.1109/TMC.2025.3530480
19. Feng L, Liao C, Shi Y, et al. Explainable and energy-efficient selective ensemble learning in mobile edge computing systems. IEEE Transactions on Network and Service Management. 2025. doi: 10.1109/TNSM.2025.3539830
20. Wang S, Urgaonkar R, Zafer M, et al. Dynamic service migration in mobile edge computing based on Markov decision process. IEEE/ACM Transactions on Networking. 2019; 27(3): 1272–1288. doi: 10.1109/TNET.2019.2916577
21. Ahmed E, Rehmani M H. Mobile edge computing: opportunities, solutions, and challenges. Future Generation Computer Systems. 2017; 70: 59–63. doi: 10.1016/j.future.2016.09.015
22. Ye Y, Li S, Liu F, et al. EdgeFed: Optimized federated learning based on edge computing. IEEe Access. 2020; 8: 209191–209198. doi: 10.1109/ACCESS.2020.3038287
23. Lakra A K, Suhag D. CrisisConnect: A blockchain-driven edge computing approach in mobile crowdsensing system. Data Science & Exploration in Artificial Intelligence. CRC Press; 2025: 297–304. doi: 10.1201/9781003589273-44
24. Li Y, Wang S. An energy-aware edge server placement algorithm in mobile edge computing. 2018 IEEE International conference on edge computing (EDGE). IEEE; 2018. pp. 66–73. doi: 10.1109/EDGE.2018.00016
25. Naouri A, Wu H, Nouri N A, et al. A novel framework for mobile-edge computing by optimizing task offloading. IEEE Internet of Things Journal. 2021; 8(16): 13065–13076. doi: 10.1109/JIOT.2021.3064225
26. Chen D, Xie L J, Kim B G, et al. Federated learning based mobile edge computing for augmented reality applications. 2020 international conference on computing, networking and communications (ICNC). IEEE; 2020. pp. 767–773. doi: 10.1109/ICNC47757.2020.9049708
Downloads
Published
Data Availability Statement
not applicable
Issue
Section
License
Copyright (c) 2026 Christina Ranjitham Manoharan, Angela Jennifa Sujana Jesudoss
This work is licensed under a Creative Commons Attribution 4.0 International License.
