Energy-Efficient Edge Computing Scheduling through AI-Enhanced Traffic Prediction in Heterogeneous Wireless Networks (International Journal of Artificial Intelligence Engineering and Systems, 2026)
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.
Keywords: edge computing, energy efficiency, traffic prediction, heterogeneous wireless networks, scheduling, artificial intelligence, sustainability, governance