Energy-Efficient Edge Computing Scheduling through AI-Enhanced Traffic Prediction in Heterogeneous Wireless Networks
Keywords:
edge computing, energy efficiency, traffic prediction, heterogeneous wireless networks, scheduling, artificial intelligence, sustainability, governanceAbstract
The rapid proliferation of mobile devices and the Internet of Things has placed immense pressure on wireless network infrastructures, demanding ultra-low latency, high bandwidth, and energy-efficient computation. Edge computing has emerged as a critical paradigm to alleviate backhaul congestion and provide responsive services by placing computational resources in close proximity to end users. However, the inherent heterogeneity of next-generation wireless networks, encompassing diverse radio access technologies, fluctuating traffic patterns, and resource-limited edge nodes, introduces significant challenges for scheduling tasks in an energy-aware manner. This paper investigates a system-level framework that integrates artificial intelligence-driven traffic prediction with edge scheduling mechanisms to achieve energy-efficient operation across heterogeneous wireless environments. By anticipating spatiotemporal traffic dynamics through advanced predictive models, edge orchestrators can proactively allocate workloads, steer task offloading, and dynamically scale resource usage, thereby minimizing energy consumption while preserving stringent quality-of-service constraints. The discussion focuses on architectural design principles, structural trade-offs between predictive accuracy and computational overhead, robustness against uncertainty, fairness in resource allocation among competing tenants, and the broader governance and sustainability implications of embedding learning-based decision processes into critical communication infrastructures. The analysis draws on cross-domain insights from large-scale data center management, cyber-physical systems, and adaptive control theory to illuminate pathways toward self-optimizing, green edge ecosystems. A governance-aware perspective underscores the need for transparent, accountable mechanisms when AI models influence infrastructure operations that underpin societal functions. The paper avoids mathematical formalization and instead provides deep conceptual exploration of how AI-enhanced prediction can reshape the energy footprint of edge computing in an increasingly heterogeneous networked world.
References
1. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
2. Mao, Y., You, C., Zhang, J., Huang, K., & Letaief, K. B. (2017). A survey on mobile edge computing: The communication perspective. IEEE Communications Surveys & Tutorials, 19(4), 2322–2358.
3. Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018). Mobile edge computing: A survey. IEEE Internet of Things Journal, 5(1), 450–465.
4. Chen, Y., Zhang, N., Zhang, Y., Chen, X., Wu, W., & Shen, X. S. (2020). Energy efficient dynamic offloading in mobile edge computing via deep reinforcement learning. IEEE Transactions on Mobile Computing, 20(5), 1772–1784.
5. Agiwal, M., Roy, A., & Saxena, N. (2016). Next generation 5G wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 18(3), 1617–1655.
6. Wang, J., Tang, J., Xu, Z., Wang, Y., Xue, G., Xing, Y., & Yang, D. (2017). Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach. IEEE Journal on Selected Areas in Communications, 35(9), 1924–1937.
7. Zhang, C., & Patras, P. (2018). Long-term mobile traffic forecasting using deep spatio-temporal neural networks. In Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing (pp. 231–240). ACM.
8. Li, Y., Yu, R., Shahabi, C., & Liu, Y. (2018). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In International Conference on Learning Representations.
9. Wang, C., Liang, C., Yu, F. R., Chen, Q., & Tang, L. (2017). Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Transactions on Wireless Communications, 16(8), 4924–4938.
10. Zhang, J., Xia, W., Yan, F., & Shen, L. (2018). Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access, 6, 54752–54763.
11. Wang, S., Wang, T., & Wang, X. (2022). Graph neural networks for wireless communications: From basics to applications. IEEE Communications Surveys & Tutorials, 24(1), 686–723.
12. Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021). A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2114–2124). ACM.
13. Bai, L., Yao, L., Li, C., Wang, X., & Wang, C. (2020). Adaptive graph convolutional recurrent network for traffic forecasting. In Advances in Neural Information Processing Systems, 33.
14. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems, 30.
15. Huang, L., Bi, S., & Zhang, Y. J. (2020). Deep reinforcement learning for online computation offloading in wireless powered mobile-edge computing networks. IEEE Transactions on Mobile Computing, 19(11), 2581–2593.
16. Mao, Y., Zhang, J., & Letaief, K. B. (2016). Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE Journal on Selected Areas in Communications, 34(12), 3590–3605.
17. Zhang, C., Zhang, H., Qiao, J., Li, Z., & Alouini, M. S. (2025). TIDES: Traffic Intelligence with DeepSeek Enhanced Spatial Temporal Prediction. IEEE Journal on Selected Areas in Communications.
18. Deng, R., Lu, R., Lai, C., Luan, T. H., & Liang, H. (2016). Optimal workload allocation in fog-cloud computing toward balanced delay and power consumption. IEEE Internet of Things Journal, 3(6), 1171–1181.
19. Beloglazov, A., & Buyya, R. (2012). Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurrency and Computation: Practice and Experience, 24(13), 1397–1420.
20. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
