Energy-Aware AI-Enabled Intelligent Sensing Systems for IoT and Edge–Cloud Architectures

AI-enabled intelligent sensing systems are increasingly used in Internet of Things (IoT) applications, edge–cloud environments, industrial automation, and cyber-physical systems. Their design, however, requires simultaneous consideration of sensing performance, computational demand, communication requirements, and limited energy resources. Much of the existing literature addresses these aspects separately, although their interactions often determine the performance and autonomy of the complete sensing platform. This review examines energy-aware AI-enabled sensing systems from a system-level perspective, with particular attention to the trade-offs that arise under resource-constrained operating conditions. Representative studies spanning intelligent sensing, embedded intelligence, edge–cloud computing, wireless sensor networks, AIoT, and energy-aware system design are analyzed to identify common architectural principles and design constraints. Four recurring system-level trade-offs are considered, namely measurement accuracy versus energy consumption, latency versus energy savings, local computation versus communication load, and model fidelity versus computational efficiency. The review further develops a multidimensional taxonomy of energy-aware sensing architectures based on energy source, operational mode, processing model, and communication strategy. These elements are integrated into a common framework linking sensing performance, computational requirements, communication demand, and energy allocation. Particular attention is also given to hybrid and multimodal sensing, distributed intelligence, TinyML, hardware-aware model deployment, and adaptive resource allocation. Remaining challenges include scalability, operation under changing sensing conditions, cross-platform evaluation, trustworthy embedded AI, and the transition from laboratory demonstrations to long-term deployment. The resulting system-level perspective provides a basis for designing intelligent sensing platforms in which sensing, computation, communication, and energy resources are considered jointly rather than optimized in isolation.