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Energy Neutral Design Framework for Supercapacitor-based Autonomous Wireless Sensor Networks

Abstract : To design autonomous wireless sensor networks (WSNs) with a theoretical infinite lifetime, energy harvesting (EH) techniques have been recently considered as promising approaches. Ambient sources can provide everlasting additional energy for WSN nodes and exclude their dependence on battery. In this article, an efficient energy harvesting system which is compatible with various environmental sources, such as light, heat, or wind energy, is proposed. Our platform takes advantage of double-level capacitors not only to prolong system lifetime but also to enable robust booting from the exhausting energy of the system. Simulations and experiments show that our multiple-energy-sources converter (MESC) can achive booting time in order of seconds. Although capacitors have virtual recharge cycles, they suffer higher leakage compared to rechargeable batteries. Increasing their size can decrease the system performance due to leakage energy. Therefore, an energy-neutral design framework providing a methodology to determine the minimum size of those storage devices satisfying energy-neutral operation (ENO) and maximizing system quality-of-service (QoS) in EH nodes, when using a given energy source, is proposed. Experiments validating this framework are performed on a real WSN platform with both photovoltaic cells and thermal generators in an indoor environment. Moreover, simulations on OMNET++ show that the energy storage optimized from our design framework is utilized up to 93.86%.
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Contributor : Sophie Gaffé-Clément Connect in order to contact the contributor
Submitted on : Saturday, September 27, 2014 - 7:18:30 AM
Last modification on : Tuesday, October 19, 2021 - 11:58:51 PM



Trong Nhan Le, Alain Pegatoquet, Olivier Berder, Olivier Sentieys, Arnaud Carer. Energy Neutral Design Framework for Supercapacitor-based Autonomous Wireless Sensor Networks. ACM Journal on Emerging Technologies in Computing Systems, Association for Computing Machinery, 2015, 12 (2), pp.1--21 Article 19. ⟨10.1145/2787512⟩. ⟨hal-01069098⟩



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