Energy Harvesting Sensor Nodes: Survey and Implications

Sudevalayam, Sujesha; Kulkarni, Purushottam

doi:10.1109/surv.2011.060710.00094

Cited by 1,739 publications

(1,090 citation statements)

References 69 publications

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“…Thus, energy harvesting is a promising approach for the emerging IoT [4]. Practically, energy can be harvested from environmental sources, namely, thermal, solar, vibration, and wireless radio-frequency (RF) energy sources [5].…”

Section: Introductionmentioning

confidence: 99%

Wireless energy harvesting for the Internet of Things

et al. 2015

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Internet of Things (IoT) is an emerging computing concept that describes a structure in which everyday physical objects, each provided with unique identifiers, are connected to the Internet without requiring human interaction.Long-term and self-sustainable operation are key components for realization of such a complex network, and entail energy-aware devices that are potentially capable of harvesting their required energy from ambient sources. Among different energy harvesting methods such as vibration, light and thermal energy extraction, wireless energy harvesting (WEH) has proven to be one of the most promising solutions by virtue of its simplicity, ease of implementation and availability. In this article, we present an overview of enabling technologies for efficient WEH, analyze the life-time of WEH-enabled IoT devices, and briefly study the future trends in the design of efficient WEH systems and research challenges that lie ahead.

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Section: Introductionmentioning

confidence: 99%

Wireless energy harvesting for the Internet of Things

et al. 2015

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“…Energy harvesting: New technologies have been developed to enable sensors to harvest energy from their surrounding environment such as solar, wind and kinetic energy [5]. Compared to traditional sensors, rechargeable motes can operate continuously and, theoretically, for an unlimited length of time.…”

Section: Chargingmentioning

confidence: 99%

“…However, the authors mainly focus on duty cycling and data-reduction approaches. There also exist several technique-specific surEmail addresses: tifenn.rault@utc.fr (Tifenn Rault), abdelmadjid.bouabdallah@utc.fr (Abdelmadjid Bouabdallah), yacine.challal@utc.fr (Yacine Challal) veys that concentrate on only one energy-efficient mechanism (like energy-efficient routing protocols, data aggregation techniques, energy harvesting approaches [3][4][5]) since every category of solution often represents a whole research area in itself.…”

Section: Introductionmentioning

confidence: 99%

Energy efficiency in wireless sensor networks: A top-down survey

Rault¹,

Bouabdallah²,

Challal³

2014

Computer Networks

802

452

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The design of sustainable wireless sensor networks (WSNs) is a very challenging issue. On the one hand, energyconstrained sensors are expected to run autonomously for long periods. However, it may be cost-prohibitive to replace exhausted batteries or even impossible in hostile environments. On the other hand, unlike other networks, WSNs are designed for specific applications which range from small-size healthcare surveillance systems to large-scale environmental monitoring. Thus, any WSN deployment has to satisfy a set of requirements that differs from one application to another. In this context, a host of research work has been conducted in order to propose a wide range of solutions to the energysaving problem. This research covers several areas going from physical layer optimization to network layer solutions. Therefore, it is not easy for the WSN designer to select the efficient solutions that should be considered in the design of application-specific WSN architecture.We present a top-down survey of the trade-offs between application requirements and lifetime extension that arise when designing wireless sensor networks. We first identify the main categories of applications and their specific requirements. Then we present a new classification of energy-conservation schemes found in the recent literature, followed by a systematic discussion as to how these schemes conflict with the specific requirements. Finally, we survey the techniques applied in WSNs to achieve trade-off between multiple requirements, such as multi-objective optimisation.

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“…The benefits of using supercapacitors are many, like extended battery life, less electromagnetic interferences and better operation of analogic electronics. They are explained at length in the literature [6,7].…”

Section: Introductionmentioning

confidence: 99%

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

et al. 2018

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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.

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Energy Harvesting Sensor Nodes: Survey and Implications

Cited by 1,739 publications

References 69 publications

Wireless energy harvesting for the Internet of Things

Wireless energy harvesting for the Internet of Things

Energy efficiency in wireless sensor networks: A top-down survey

Supercapacitor Electro-Mathematical and Machine Learning Modelling for Low Power Applications

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