Intelligent and Efficient Resource Management for Large-Scale IoT Systems

The rapid expansion of the Internet of Things (IoT) has resulted in large-scale, heterogeneous, and resource-constrained ecosystems spanning smart cities, industrial automation, healthcare, and intelligent transportation. Modern IoT environments comprise diverse devices with varying computing capabilities, communication patterns, energy budgets, and reliability requirements. As the number of connected devices continues to grow, resource management becomes a critical bottleneck affecting the performance, scalability, and sustainability of IoT deployments. Traditional centralized approaches struggle to meet the increasing complexity and real-time demands posed by dynamic workloads and volatile network conditions.



Meanwhile, edge and fog computing have emerged as key enablers to complement cloud services by providing low-latency processing, localized intelligence, and better privacy preservation. However, the distributed nature of these infrastructures introduces new challenges in workload placement, energy-aware scheduling, adaptive communication, and cross-layer optimization. Recent advances in AI-driven decision making, reinforcement learning, distributed analytics, and digital-twin-based modeling offer promising tools to enhance IoT resource efficiency and autonomic management. Despite these developments, significant gaps remain in achieving scalable, reliable, and context-aware resource coordination, especially under stringent QoS constraints and energy limitations.



This Research Topic aims to gather state-of-the-art research addressing these pressing challenges and advancing the next generation of intelligent IoT resource management methodologies.



The goal of this Research Topic is to showcase innovative theories, architectures, and intelligent techniques that enhance the performance, efficiency, and resilience of resource management in large-scale IoT systems. We aim to highlight approaches that improve resource utilization, reduce energy consumption, support adaptive decision making, and enable scalable coordination across heterogeneous IoT, edge, and cloud infrastructures.



We invite contributions that explore novel strategies, algorithms, and system designs for resource management in IoT environments. Relevant topics include:



Intelligent resource scheduling and workload orchestration



Energy-efficient sensing, computation, and communication



AI-driven resource optimization and predictive modeling



Computation offloading and edge–cloud collaboration



QoS-aware and latency-sensitive management mechanisms



Network resource optimization for 5G/6G-enabled IoT



Lightweight middleware, runtime systems, and cloud-native IoT platforms



Security-, trust-, and privacy-aware resource management



Case studies and benchmark evaluations in real-world IoT applications



Authors should emphasize scalability, reproducibility, and practical insights into managing heterogeneous and dynamic IoT infrastructures. Interdisciplinary studies combining IoT with edge AI, cyber-physical systems, or distributed learning are particularly encouraged. This Research Topic aims to build a comprehensive understanding of emerging solutions that support efficient, adaptive, and dependable IoT resource management.