Resource allocation for efficient AI inference in wireless sensing edge networks
Tanveer Ahmad, Asma Abbas Hassan Elnour, Muhammad Usman Hadi, Kiran Khurshid, Xue Jun Li, Weiwei Jiang · Computer Communications · 2025
Integrating AI inference into wireless sensing edge networks presents notable challenges due to limited resources, changing environments, and diverse devices. In this study, we proposed a novel resource allocation framework that enhances energy efficiency, reduces latency, and ensures fairness across distributed edge nodes for AI inference. The framework models a multi-objective optimization problem that reflects the interdependence of computation, communication, and energy at each device. We also develop a decentralized algorithm based on dual decomposition and projected gradient ascent, by using local data. The extensive simulations demonstrate that our proposed method reduces the average inference latency by 31.4% and energy consumption by 27.8% compared to the greedy and round-robin techniques. The system utility is improved by up to 59.2%, and fairness, measured using Jain’s index, remains within 8% of the ideal. Additionally, throughput analysis further confirms that our approach gains up to 49 tasks/sec, outperforming existing strategies by more than 40%. These findings show that the resource-aware AI inference approach is scalable, energy-efficient, and appropriate for real-time use in multi-user wireless edge networks. • Proposes a decentralized resource allocation framework for AI inference in wireless sensing edge networks. • Jointly optimizes latency, energy consumption, and fairness under dynamic workloads. • Employs a dual decomposition-based algorithm with minimal control signaling for scalability. • Achieves up to 31.4% lower latency and 27.8% lower energy use compared to greedy and round-robin schemes. • Maintains Jain’s fairness index above 0.95 while improving system utility by up to 59.2.