Toward Energy-Autonomous Distributed Intelligence in IoT Automation Networks: From Self-Powered Nodes to Edge–Fog–Cloud Integrated Smart Systems

Energy-autonomous Internet of Things (IoT) nodes are becoming important components of distributed wireless networks used in building automation, industrial monitoring and wider smart systems. Their operation is constrained not only by the amount of harvested and stored energy, but also by sensing activity, communication cost, computational workload and required service quality. This review analyzes these dependencies from a cross-layer perspective linking energy harvesting and power management, field-level IoT nodes, wireless communication technologies, and edge–fog–cloud computing. The main original contribution is a decision-oriented framework for energy-autonomous distributed intelligence, supporting adaptive placement of sensing, processing, inference and coordination functions across field, edge, fog and cloud resources. The analysis shows that energy autonomy cannot be achieved by optimizing individual nodes only. Wireless connectivity, network topology and communication overhead directly affect the feasibility of higher-level processing, while edge and fog resources can reduce field-node load and improve local service continuity. The proposed framework therefore combines energy feasibility, communication conditions, service requirements and coordination scope. The resulting guidelines are particularly relevant to building automation and smart IoT systems, supporting interoperable, adaptive and energy-efficient distributed wireless architectures.