HarvWSNet: A co-simulation framework for energy harvesting wireless sensor networks

Didioui, Amine; Bernier, Carolynn; Morche, Dominique; Sentieys, Olivier

doi:10.1109/iccnc.2013.6504192

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…However, it is complex to estimate the behaviour of batteries when these devices are used as a power supply for sensor nodes. Particularly, WSN simulators do not often consider the use of this type of technology in simulations . As a consequence, their battery or energy models do not accurately describe the behaviour of battery charging and discharging processes over time, which in turn gives rise to substantial differences in the lifetime of sensor nodes, making it difficult to tolerate power failures in simulations or real‐world deployments.…”

Section: Resultsmentioning

confidence: 99%

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

et al. 2020

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Energy limitation is one of the major bottlenecks during the operation of many emerging applications, such as electric vehicles, water and gas meters and a number of sensors used in the context of the Internet of Things and cyberphysical systems. Energy harvesting techniques have arisen as a promising solution to minimize the energy issues found in these types of application domains. In energy harvesting systems, a critical challenge is the need to use battery models capable of accurately estimating both the input and output power of batteries. This article proposes a temperature-dependent analytical battery model capable of estimating some output quantitiesfor example, state of charge, voltage and lifetimeof batteries that use energy harvesting technologies. This model was validated by comparing its analytical results with a dataset called the Randomized Battery Usage Data Set, which is available at the data repository of the National Aeronautics and Space Administration (NASA) website. It is also presented a proof-of-concept application, demonstrating that the use of these technologies can serve as an effective means to extend the operating time of batteries, resulting in significant benefits for a number of applications. K E Y W O R D Swireless sensor network, battery modelling, energy harvesting, thermal effect

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Section: Resultsmentioning

confidence: 99%

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

et al. 2020

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“…The authors explicitly mention the use of static power supply and clock speed models. There exist numerous similar efforts for emulating the behavior of energy harvesting in different environments [13,19,37,38]. Allen et al [2] compare many of these with each other, and discuss their limitations with regard to the representation of energy harvesting dynamics and power consumption modeling.…”

Section: Background and Related Workmentioning

confidence: 99%

The betrayal of constant power × time: finding the missing Joules of transiently-powered computers

Ahmed

Bakar

Bhatti

et al. 2019

Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems

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Transiently-powered computers (TPCs) lay the basis for a battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This power supply is characterized by extreme variations in supply voltage, as capacitors charge when harvesting energy and discharge when computing. We experimentally find that these variations cause marked fluctuations in clock speed and power consumption, which determine energy efficiency. We demonstrate that it is possible to accurately model and concretely capitalize on these fluctuations. We derive an energy model as a function of supply voltage and develop EPIC, a compiletime energy analysis tool. We use EPIC to substitute for the constant power assumption in existing analysis techniques, giving programmers accurate information on worst-case energy consumption of programs. When using EPIC with existing TPC system support, run-time energy efficiency drastically improves, eventually leading up to a 350% speedup in the time to complete a fixed workload. Further, when using EPIC with existing debugging tools, programmers avoid unnecessary program changes that hurt energy efficiency. CCS Concepts • Computer systems organization → Embedded and cyber-physical systems.

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“…HarvWSNet is based on WSNet and MATLAB. Users may build multi-node network scenarios while imposing a concise description for node energy harvesting, management subsystem, and its time-varying environmental parameters (Didioui, 2013). 13.…”

Section: Usn Emulation Environmentsmentioning

confidence: 99%

Ubiquitous sensor network simulation and emulation environments: A survey

Sharif

Sadeghi‐Niaraki

2017

Journal of Network and Computer Applications

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Recent human effort has been directed at expanding pervasive smart environments. For this, ubiquitous computing technology is introduced to provide all users with any service, anytime, anywhere, with any device, and under any network. However, high cost, long time consumption, extensive effort, and in some cases irrevocability are the main challenges and difficulties for developing ubiquitous systems. Therefore, one solution is to initially simulate, analyze, and validate practices prior to deploying sensing and computational devices in the real world. Simulation, as a performance evaluation technique, has attracted attentions due to its speed, cost-effectiveness, repeatability, scalability, flexibility, and ease of implementation. Moreover, emulation, as a hybrid method, not only offers most simulation advantages but also benefits from tight control of implementation, as well as a certain degree of realistic results. Both simulators and emulators are significant tools for enhancing the understanding of ubiquitous sensor networks (USNs) through testing and analyzing several scenarios prior to actual sensor placements. In this regard, this paper surveys 130 simulation and emulation environments and frameworks, which were originally designed and adapted for USN. Of these 130, the 22 that have been widely used, regularly updated, and well supported by their developers are compared based on multifarious criteria. Finally, several studies that had favorably compared the performance of simulators and/or emulators are examined. We believe the present research findings will be helpful for students and researchers to pick an appropriate simulator/emulator, and for software developers and those who are keen on producing their own environment.

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HarvWSNet: A co-simulation framework for energy harvesting wireless sensor networks

Cited by 17 publications

References 7 publications

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

An analytical model to estimate the state of charge and lifetime for batteries with energy harvesting capabilities

The betrayal of constant power × time: finding the missing Joules of transiently-powered computers

Ubiquitous sensor network simulation and emulation environments: A survey

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