Towards an Extensible and Scalable Energy Harvesting Wireless Sensor Network Simulation Framework

Allen, James; Forshaw, Matthew; Thomas, Nigel

doi:10.1145/3053600.3053610

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…There exist numerous similar efforts for emulating the behavior of energy harvesting in different environments [13,19,39,40]. 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. They also emphasize the need for more accurate modeling, simulation, and emulation techniques for TPCs, which we provide here.…”

Section: Related Workmentioning

confidence: 99%

Demystifying Energy Consumption Dynamics in Transiently powered Computers

Ahmed

Nawaz

Bakar

et al. 2020

ACM Trans. Embed. Comput. Syst.

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Transiently powered computers (TPCs) form the foundation of the battery-less Internet of Things, using energy harvesting and small capacitors to power their operation. This kind of 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 . Such a deceptively minor observation is overlooked in existing literature. Systems are thus designed and parameterized in overly conservative ways, missing on a number of optimizations. We rather 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 prove its use in two settings. First, we develop EPIC, a compile-time energy analysis tool. We use it 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, it avoids unnecessary program changes that hurt energy efficiency. Next, we extend the MSPsim emulator and explore its use in parameterizing a different TPC system support. The improvements in energy efficiency yield up to more than 1000% time speedup to complete a fixed workload.

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Section: Related Workmentioning

confidence: 99%

Demystifying Energy Consumption Dynamics in Transiently powered Computers

Ahmed

Nawaz

Bakar

et al. 2020

ACM Trans. Embed. Comput. Syst.

View full text Add to dashboard Cite

show abstract

“…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. They emphasize the need for more accurate modeling, simulation, and emulation techniques for TPCs, which we provide here.…”

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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“…A set of transformations was used that allowed a network performance analysis. Other related works [20][21][22] proposed different frameworks that dealt with the simulation of energy-harvesting WSNs. However, these approaches do not support MDE terminologies such as models and model transformations.…”

Section: Related Workmentioning

confidence: 99%

A model-based transformation framework for designing and analyzing wireless sensor networks

Saida¹,

Kacem²,

BenSaleh³

et al. 2018

Turk J Elec Eng & Comp Sci

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Wireless sensor networks (WSNs) comprise resource-constrained (e.g., memory, processing, and energy) sensor nodes that are deployed in different areas and are able to monitor environmental conditions. Simulation has been widely used in order to evaluate the network performance. In this context, software designers need to evaluate, refine, and validate high-level models for WSN applications at early stages of development via simulation tools, in particular for WSN applications supporting energy-harvesting devices. In the present paper, we propose a model-based transformation framework that allows the modeling and simulation of a WSN system supporting energy-harvesting capabilities. In this proposal, we start from a high-level specification based on the UML/MARTE profile, which describes an energyharvesting WSN node. Then a model-to-text (M2T) transformation allows us to generate simulation scripts for analysis purposes by focusing on energy consumption.

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Towards an Extensible and Scalable Energy Harvesting Wireless Sensor Network Simulation Framework

Cited by 7 publications

References 19 publications

Demystifying Energy Consumption Dynamics in Transiently powered Computers

Demystifying Energy Consumption Dynamics in Transiently powered Computers

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

A model-based transformation framework for designing and analyzing wireless sensor networks

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