VLSI-Integrated Energy Harvesting Architectures for Battery-Free IoT Edge Systems
Keywords:
Ambient energy, Battery-free devices, Edge computing, Energy harvesting, Non-volatile memory, Self-sustaining IoT, Ultra-low-power systems, VLSI designAbstract
The proliferation of the internet of things (IoT) has led to the deployment of billions of interconnected edge devices across diverse domains, including healthcare, agriculture, smart cities, and industrial automation. These devices are required to operate in remote or inaccessible environments, often under strict power constraints. Conventional battery-powered solutions suffer from inherent drawbacks such as limited energy capacity, frequent recharging or replacement requirements, and adverse environmental impact due to large-scale battery disposal. These limitations highlight the urgent need for sustainable and self-sufficient power alternatives.
This research presents a VLSI-based energy-harvesting architecture for self-sustaining IoT edge devices, designed to harvest and efficiently utilize ambient energy sources including solar, radio frequency (RF), thermal gradients, and mechanical vibrations. The proposed system integrates high-efficiency rectifiers, adaptive DC-DC converters, and power management units with an ultra-low-power VLSI processing core operating in sub-threshold regimes. To ensure reliability under intermittent energy supply, the architecture employs non-volatile memory technologies such as FRAM and RRAM, coupled with energy-aware wireless communication modules optimized for low-power data transmission.
Through circuit-level optimization, system modeling, and prototype validation, the design demonstrates continuous operation with minimal external energy support, significantly extending device lifetime compared to conventional battery-dependent nodes. The expected contribution of this work is a scalable, battery-free IoT platform that reduces maintenance costs, enhances environmental sustainability, and accelerates the deployment of truly autonomous edge computing systems.
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