Affiliation:
1. Clemson University, Clemson, SC, USA
Abstract
Batteryless sensors promise a sustainable future for sensing, but they face significant challenges when storing and using environmental energy. Incoming energy can fluctuate unpredictably between periods of scarcity and abundance, and device performance depends on both incoming energy and how much a device can store. Existing batteryless devices have used fixed or run-time selectable front-end capacitor banks to meet the energy needs of different tasks. Neither approach adapts well to rapidly changing energy harvesting conditions, nor does it allow devices to store excess energy during times of abundance without sacrificing performance. This article presents Stash, a hardware back-end energy storage technique that allows batteryless devices to charge quickly and store excess energy when it is abundant, extending their operating time and carrying out additional tasks without compromising the main ones. Stash performs like a small capacitor device when small capacitors excel and like a large capacitor device when large capacitors excel, with no additional software complexity and negligible power overhead. We evaluate Stash using two applications—temperature sensing and wearable activity monitoring—under both synthetic solar energy and recorded solar and thermal traces from various human activities. Our results show that Stash increased sensor coverage by up to 15% under variable energy-harvesting conditions when compared to competitor configurations that used fixed small, large, and reconfigurable front-end energy storage.
Publisher
Association for Computing Machinery (ACM)
Reference49 articles.
1. Matrix Industries. [n.d.]. Choose the right Prometheus—Matrix: Self-powered solutions Retrieved from https://www.matrixindustries.com/2118305w
2. Mikhail Afanasov, Naveed Anwar Bhatti, Dennis Campagna, Giacomo Caslini, Fabio Massimo Centonze, Koustabh Dolui, Andrea Maioli, Erica Barone, Muhammad Hamad Alizai, Junaid Haroon Siddiqui et al. 2020. Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus. In Proceedings of the 18th Conference on Embedded Networked Sensor Systems. 368–381.
3. Efficient intermittent computing with differential checkpointing
4. Controlling action space of reinforcement learning-based energy management in batteryless applications;Ahn JunIck;IEEE Internet Things J.,2023
5. Adamica: Adaptive multicore intermittent computing;Akhunov Khakim;Proc. ACM Interact. Mobile Wear. Ubiq. Technol.,2022
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