Wearable battery-less wireless sensor network with electromagnetic energy harvesting system

This study presents the design and implementation of a battery-less wearable wireless sensor network (WSN) powered by a compact electromagnetic (EM) energy harvesting system. Aimed at enabling maintenance-free and long-term operation of sensor nodes, the system leverages energy generated from human motion, such as walking or running, to power a wireless sensor mote without relying on conventional batteries.

The proposed system includes a custom-designed EM energy harvester optimized to capture low-frequency body-induced vibrations (1–3 Hz), which are typical of natural human movements. These vibrations are converted into electrical energy using a coil-and-magnet-based harvester. The generated energy is rectified using a low-threshold Dickson rectifier and stored in a buffer capacitor before being regulated by a buck DC-DC converter to supply power to a MICAz sensor node.

A key innovation of this work lies in its adaptive sensing strategy, where the data transmission rate is dynamically adjusted based on the real-time energy level stored in the buffer capacitor. This Energy-Level Adjusting (ELA) architecture allows sensor nodes to extend their operational lifetime by modulating their activity according to the availability of harvested energy. Experimental results show that the node can successfully adjust its sensing and transmission intervals between 5 to 60 seconds, depending on whether the user is running, walking, or idle.

The system was validated in both individual and multi-node WSN deployments, demonstrating stable performance even under limited energy harvesting conditions. In real-time experiments, the system maintained consistent operation while being worn by individuals on treadmills and during regular activity, proving its viability for wearable IoT applications such as personal health monitoring, fitness tracking, and emergency localization. The approach also has potential for animal tracking and low-frequency vibration environments, such as offshore monitoring.

By eliminating the need for battery replacement and enabling adaptive power management, this work contributes a sustainable and scalable solution for next-generation wearable sensor networks. The system’s low-cost and compact design, along with its ability to operate autonomously, opens new opportunities for long-term deployment of smart sensing platforms in dynamic and mobile environments.

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