This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Aqusa Fatima,
Almas,
Shahab Saquib Sohail,
- Professor, Department of CSE, SEST, Jamia Hamdard, New Delhi, India
- Professor, Department of CSE, SEST, Jamia Hamdard, New Delhi, India
- Professor, Department of CSE, SEST, Jamia Hamdard, New Delhi, India
Abstract
The rapid expansion of Internet of Things (IoT) applications has introduced significant challenges in managing computational workloads across distributed edge environments. Edge- IoT systems are characterized by limited computational capacity, dynamic task arrivals, and strict latency constraints. Traditional heuristic-based scheduling techniques often fail to adapt to fluctuating workloads and heterogeneous resource availability. This study proposes a machine learning-based task scheduling and resource optimization framework for Edge-IoT systems. The proposed model leverages predictive analytics to dynamically allocate computational tasks based on system state parameters, including CPU utilization, memory availability, task size, and network latency. A supervised learning model is employed to predict optimal task placement decisions, minimizing execution delay and maximizing resource utilization. Experimental evaluation demonstrates that the proposed approach reduces overall latency and improves system efficiency compared to conventional scheduling strategies. The findings highlight the effectiveness of integrating intelligent decision-making mechanisms within edge computing architectures to support scalable and real-time IoT applications.
Keywords: Edge Computing; Internet of Things; Task Scheduling; Machine Learning; Resource Optimization
References
1. Sheng S, Chen P, Chen Z, Wu L, Yao Y. Deep reinforcement learning-based task scheduling in IoT edge computing. Sensors. 2021 Feb 28;21(5):1666. 2. Mali S, Zeng F, Adhikari D, Ullah I, Al-Khasawneh MA, Alfarraj O, Alblehai F. Federated reinforcement learning-based dynamic resource allocation and task scheduling in edge for IoT applications. Sensors. 2025 Mar 30;25(7):2197. 3. Ramezani Shahidani F, Ghasemi A, Toroghi Haghighat A, Keshavarzi A. Task scheduling in edge-fog-cloud architecture: a multi-objective load balancing approach using reinforcement learning algorithm. Computing. 2023 Jun;105(6):1337-59. 4. Merenda M, Porcaro C, Iero D. Edge machine learning for ai-enabled iot devices: A review. Sensors. 2020 Apr 29;20(9):2533. 5. Satyanarayana M. The emergence of edge computing. Computer. 2017 Jan;50(1):30–39. 6. Mao Y, You C, Zhang J, Huang K, Letaief KB. A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials. 2017;19(4):2322–2358. 7. Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet of Things Journal. 2016;3(5):637–646. 8. Chiang M, Zhang T. Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal. 2016;3(6):854–864. 9. Mach P, Becvar Z. Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials. 2017;19(3):1628–1656. 10. Abbas N, Zhang Y, Taherkordi A, Skeie T. Mobile edge computing: A survey. IEEE Internet of Things Journal. 2018;5(1):450–465. 11. Li K, Xu G, Zhao G, Dong Y, Wang D. Cloud task scheduling based on load balancing ant colony optimization. IEEE International Conference on Computer Science and Network Technology. 2011;3:3–9. 12. Tsai CW, Lai CF, Chiang MC, Yang LT. Data mining for Internet of Things: A survey. IEEE Communications Surveys & Tutorials. 2014;16(1):77–97. 13. Xu X, Li W, Huang T, Xue Y, Qi L, Dou W. A distributed task scheduling algorithm based on deep reinforcement learning for edge computing. Future Generation Computer Systems. 2020;107:37–47. 14. Chen X, Jiao L, Li W, Fu X. Efficient multi-user computation offloading for mobile- edge cloud computing. IEEE/ACM Transactions on Networking. 2016;24(5):2795–2808. 15. Wang S, Zhang X, Zhang Y, Wang L, Yang J, Wang W. A survey on mobile edge networks: Convergence of computing, caching and communications. IEEE Access. 2017;5:6757–6779. 16. He Y, Yu FR, Zhao N, Yin H, Zhang H, Leung VCM. Software-defined networks with mobile edge computing and caching for smart cities: A big data deep reinforcement learning approach. IEEE Communications Magazine. 2017;55(12):31–37. 17. Liu Y, Yu FR, Li X, Ji H, Leung VCM. Distributed resource allocation and computation offloading in fog and cloud networks with non-orthogonal multiple access. IEEE Transactions on Vehicular Technology. 2018;67(12):12137–12151. 18. Taleb T, Samdanis K, Mada B, Flinck H, Dutta S, Sabella D. On multi-access edge computing: A survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Communications Surveys & Tutorials. 2017;19(3):1657–1681. 19. Deng R, Lu R, Lai C, Luan T, Liang H. Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal. 2016;3(6):1171–1181. 20. Chen M, Hao Y. Task offloading for mobile edge computing in software defined ultra- dense network. IEEE Journal on Selected Areas in Communications. 2018;36(3):587–597. 21. Wang C, Liang C, Yu FR, Chen Q, Tang L. Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Transactions on Wireless Communications. 2017;16(8):4924–4938. 22. Liu C, Cao J, Zhang Y, Liu K. A reinforcement learning-based resource allocation approach for cloud and edge computing environments. IEEE Access. 2019;7:117925–117936. 23. Zhang W, Wen Y, Wu D. Energy-efficient scheduling policy for collaborative execution in mobile cloud computing. IEEE INFOCOM. 2013;190–194. 24. Merlino G, Longo F, Puliafito A. Cloud-based IoT data analysis: A survey. Future Generation Computer Systems. 2018;79:1–19.
Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 10/06/2026 | |
| Accepted | 19/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 112 Days |