Architecture exploration of multi-source energy harvester for IoT nodes

Gléonec, Philip-Dylan; Ardouin, Jérémy; Gautier, Matthieu; Berder, Olivier

doi:10.1109/onlinegreencom.2016.7805402

Cited by 18 publications

(23 citation statements)

References 7 publications

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“…Figure 1B). Sustainability can be achieved through either technical improvements via means such as optimization of energy efficiency (Gleonec et al, 2017;Mazinani and Davarzani, 2017), or innovative soft management/incentivebased approaches (Bakker and Ritts, 2018). Most of the reviewed studies identified with a sustainability element were associated with explicit and direct human benefits, such as monitoring a particular resource (e.g., Wada et al, 2007), protecting property or livelihoods (e.g., Lopes Pereira et al, 2014) or were agricultural in nature and focused on resource use to maximize yields (e.g., Geipel et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias toward studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualizations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focused on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts.

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Section: Resultsmentioning

confidence: 99%

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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“…Appl. 2020, 10, 11 3 of 11 overall efficiency. Moreover, diodes are non-linear devices that work as a variable impedance that changes with input power itself, making it more difficult to design an efficient matching network.…”

Section: Design Methods and Optimizationmentioning

confidence: 99%

“…In recent years, the great use of low-power autonomous systems and sensors [1][2][3][4][5][6] increased the need for self-sustainable devices, which are capable of harvesting and using energy from the environment, particularly for those that need a continuous power supply (such as human health monitoring systems) [7][8][9][10][11][12][13]. These devices, such as low-voltage front-ends for photomultiplier [4] or monitoring systems for buildings with a low power consumption [6], can use energy scavenged from the environment, which is typically poor but enough to ensure the system functionality.…”

Section: Introductionmentioning

confidence: 99%

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Rectifiers’ Design and Optimization for a Dual-Channel RF Energy Harvester

Colaiuda

Ulisse

Ferri

2020

JLPEA

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This paper presents the design and implementation of two front-ends for RF (Radio Frequency) energy harvesting, comparing them with the commercial one—P2110 by Powercast Co. (Pittsburgh, PA, USA) Both devices are implemented on a discrete element board with microstrip lines combined with lumped elements and are optimized for two different input power levels (−10 dBm and 10 dBm, respectively), at the GSM900 frequencies. The load has been fixed at 5kΩ, after a load-pull analysis on systems. The rectifiers stages implement two different Schottky diodes in two different topologies: a single diode and a 2-stage Dickson’s charge pump. The second one is compared with the P2110 by generating RF fields at 915 MHz with the Powercast Powerspot. The main aim of this work is to design simple and efficient low-cost devices, which can be used as a power supply for low-power autonomous sensors, with better performances than the current solutions of state-of-the-art equipment, providing an acceptable voltage level on the load. Measurements have been conducted for input power range −20 dBm up to 10 dBm; the best power conversion efficiency (PCE) is obtained with the second design, which reaches a value of 70% at 915 MHz. In particular, the proposed device exhibited better performance compared to the P2110 commercial device, allowing a maximum distance of operation of up to 22 meters from the dedicated RF power source, making it suitable even for IoT (Internet of Things) applications.

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“…A new trend in energy harvesting is to provide the node with the ability to be powered by several energy sources [4], [5]. In such multi-source energy harvesting systems, dedicated EMs must be designed to operate with the different sources.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection Framework for Multi-Source Energy Harvesting Wireless Sensor Networks

Lagha

Aoudia

Gautier

et al. 2018

2018 IEEE 87th Vehicular Technology Conference (VTC Spring)

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Energy harvesting technologies are constantly evolving to help power sensor network nodes. Ranging from miniature power solar panels to micro wind turbines, nodes still express a deep need to harvest energies in order to keep both good performance level and energy autonomy. Recently, the simultaneous use of multiple sources has been proposed to tackle the time-varying characteristics of certain sources that can induce energy scarcity period and thus alter the node performance. In this context, this paper presents a methodology aimed at classifying the energy sources to choose the most efficient energy manager. As sensor nodes are embedded devices, it is necessary to ensure a balance between computational effort and classification accuracy. Feature extraction and selection phases can be processed and analyzed offline before deployment, and only a subset of features will be needed by the nodes to achieve efficient energy management. Simulations on real energy traces show that the proposed approach achieves classification accuracy higher than 95% through the computation of 4 features only.

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Architecture exploration of multi-source energy harvester for IoT nodes

Cited by 18 publications

References 7 publications

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Rectifiers’ Design and Optimization for a Dual-Channel RF Energy Harvester

Feature Selection Framework for Multi-Source Energy Harvesting Wireless Sensor Networks

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