Energy Harvesting for Smart Sensing System and IoT

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (31 December 2017) | Viewed by 8593

Special Issue Editor

ETH Zurich
Interests: wireless sensor networks; Smart Sensors and the Internet of Things; wake up radio; power management; energy harvesters
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Battery-operated smart sensing systems are one of the most important technologies for the Internet of Things (IoT). The most challenging issue of IoT, when deployed in the field, is the limited lifetime of battery-operated devices. Energy Harvesting (EH) technology is one of the most promising solutions to overcome the short lifetime of such smart devices. In the last decade, EH has matured as a technology and has found use in many application scenarios, such as smart grids, smart homes and wireless sensor networks. Recently, advances have been made in miniaturizing EH devices to even supply wearable devices by exploiting ambient energy in the form of motion, thermal gradients, light, and electromagnetic radiation. However, harvesting energy from the environment for powering small form factor IoT sensing devices (i.e., unobtrusive or wearable) is more challenging due to strict constraints in terms of size, weight, and cost. For this Special Issue, we welcome high-quality submissions that describe original and unpublished research contributions advancing the frontiers on small form factor energy harvesting technologies, devices, and techniques for smart sensing IoT devices and embedded systems, with particular emphasis on wearable devices.

Dr. Michele Magno
Guest Editor

Manuscript Submission Information

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Keywords

  • Novel energy harvesting hardware, devices, systems, or techniques
  • Applications of energy harvesting systems, with a special emphasis on wearables devices, health monitoring, indoor/outdoor monitoring, and control and the Internet of Things
  • Architecture, algorithms, and protocols that improve energy efficiency
  • Tools to model, to simulate or to measure power consumption
  • Power-aware sensing systems
  • Solar, RF, thermal, kinetic energy harvesting
  • Self-sustaining wearable devices
  • Energy neutral systems and power management
  • Low power electronic for energy harvesting system, such as micro watt communication, always on zero power sensing
  • Advances in energy storage for pervasive systems, including micro fuel cells
  • Alternative power sources for embedded applications, such as novel nuclear, chemical, or biological sources

Published Papers (1 paper)

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Research

17 pages, 30488 KiB  
Article
Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications
by Borja Pozo, Jose Ignacio Garate, Susana Ferreiro, Izaskun Fernandez and Erlantz Fernandez de Gorostiza
Electronics 2018, 7(4), 44; https://doi.org/10.3390/electronics7040044 - 29 Mar 2018
Cited by 22 | Viewed by 8164
Abstract
Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and [...] Read more.
Low power electronic systems, whenever feasible, use supercapacitors to store energy instead of batteries due to their fast charging capability, low maintenance and low environmental footprint. To decide if supercapacitors are feasible requires characterising their behaviour and performance for the load profiles and conditions of the target. Traditional supercapacitor models are electromechanical, require complex equations and knowledge of the physics and chemical processes involved. Models based on equivalent circuits and mathematical equations are less complex and could provide enough accuracy. The present work uses the latter techniques to characterize supercapacitors. The data required to parametrize the mathematical model is obtained through tests that provide the capacitors charge and discharge profiles under different conditions. The parameters identified are life cycle, voltage, time, temperature, moisture, Equivalent Series Resistance (ESR) and leakage resistance. The accuracy of this electro-mathematical model is improved with a remodelling based on artificial neuronal networks. The experimental data and the results obtained with both models are compared to verify and weigh their accuracy. Results show that the models presented determine the behaviour of supercapacitors with similar accuracy and less complexity than electromechanical ones, thus, helping scaling low power systems for given conditions. Full article
(This article belongs to the Special Issue Energy Harvesting for Smart Sensing System and IoT)
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