Energy Harvesting Techniques for Self-Powered Industrial IoT Sensor Nodes

The rapid expansion of Industrial Internet of Things (IIoT) applications in harsh and remote industrial environments has driven the demand for self-powered, maintenance-free sensor nodes. This research explores advanced energy harvesting techniques tailored for selfsustaining Industrial IoT sensor nodes, enabling prolonged operational lifetimes and reduced dependency on wired power or frequent battery replacements. The study focuses on the integration of multi-source energy harvesting-comprising vibration-based piezoelectric harvesters, thermoelectric generators (TEGs) utilizing industrial heat gradients, RF energy scavenging from ambient signals, and indoor photovoltaic (PV) systems-to supply ultra-low power electronics in real-time monitoring systems. An adaptive power management unit (PMU) with maximum power point tracking (MPPT) and energy-aware scheduling algorithms is designed to optimize power utilization across variable load conditions. Performance evaluations in a simulated industrial setting demonstrate that hybrid energy harvesting systems can consistently deliver milliwatt-level power, sufficient to support sensing, processing, and short-range wireless communication (e.g., LoRa, ZigBee). The proposed framework significantly enhances sensor node autonomy, minimizes system downtime, and supports scalable deployment across Industry 4.0 and 5.0 infrastructures. This work provides a robust foundation for designing sustainable, smart manufacturing environments with minimal environmental and operational footprints.