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Dataset for: A Control Flow for Transiently-Powered Energy Harvesting Sensor Systems

Dataset for: A Control Flow for Transiently-Powered Energy Harvesting Sensor Systems
Dataset for: A Control Flow for Transiently-Powered Energy Harvesting Sensor Systems
Dataset supporting the paper: Domenico Balsamo, Oktay Cetinkaya, Alberto Rodriguez Arreola, Samuel C. B. Wong, Geoff V. Merrett, Alex S. Weddell "A Control Flow for Transienttly-Powered Energy Harvesting Sensor Systems". IEEE Sensors Journal Transient computing enables application execution to be performed despite power outages. Although it handles the non-deterministic nature of energy harvesting (EH), sensor systems envisioned by the IoT seek more cost- and volume-effective solutions, which are better tailored to application requirements. Additionally, a major drawback of transient computing, keeping track of time, hinders its widespread adoption in the IoT. To overcome these challenges, this paper proposes a control flow for sensor systems by combining two state-of-the-art transient computing schemes in an energy-aware manner, underpinned by a strategy for timekeeping. It enables application execution to be reliably performed even under the most severe EH conditions, with an improved cost and volume efficiency, i.e., smaller energy storage. Benefiting from the combination of the two schemes, dynamic adjustment of system performance is achieved, while the time is accurately tracked. To illustrate the applicability of this flow to actual sensor systems, two case studies: a bicycle trip computer and a step counter, are presented. Empirical results reveal that, even with a tiny amount of energy harvested ('tens of uJ), our proposed approach can meet application requirements with smaller storage, i.e., 40% and 66% reduction in required capacitance for the presented case studies.
University of Southampton
Wong, Samuel Chang Bing
0eb232d1-161b-4f6d-bf2b-514704c29e69
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Weddell, Alexander
3d8c4d63-19b1-4072-a779-84d487fd6f03
Cetinkaya, Oktay
6cb457a5-77b8-415d-b524-9e8728c35f0a
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Rodriguez Arreola, Alberto
e20f97e9-b616-47de-9f37-f4a445e0adac
Wong, Samuel Chang Bing
0eb232d1-161b-4f6d-bf2b-514704c29e69
Merrett, Geoff
89b3a696-41de-44c3-89aa-b0aa29f54020
Weddell, Alexander
3d8c4d63-19b1-4072-a779-84d487fd6f03
Cetinkaya, Oktay
6cb457a5-77b8-415d-b524-9e8728c35f0a
Balsamo, Domenico
fa2dc20a-e3da-4d74-9070-9c61c6a471ba
Rodriguez Arreola, Alberto
e20f97e9-b616-47de-9f37-f4a445e0adac

Wong, Samuel Chang Bing, Merrett, Geoff, Weddell, Alexander, Cetinkaya, Oktay, Balsamo, Domenico and Rodriguez Arreola, Alberto (2020) Dataset for: A Control Flow for Transiently-Powered Energy Harvesting Sensor Systems. University of Southampton doi:10.5258/SOTON/D1364 [Dataset]

Record type: Dataset

Abstract

Dataset supporting the paper: Domenico Balsamo, Oktay Cetinkaya, Alberto Rodriguez Arreola, Samuel C. B. Wong, Geoff V. Merrett, Alex S. Weddell "A Control Flow for Transienttly-Powered Energy Harvesting Sensor Systems". IEEE Sensors Journal Transient computing enables application execution to be performed despite power outages. Although it handles the non-deterministic nature of energy harvesting (EH), sensor systems envisioned by the IoT seek more cost- and volume-effective solutions, which are better tailored to application requirements. Additionally, a major drawback of transient computing, keeping track of time, hinders its widespread adoption in the IoT. To overcome these challenges, this paper proposes a control flow for sensor systems by combining two state-of-the-art transient computing schemes in an energy-aware manner, underpinned by a strategy for timekeeping. It enables application execution to be reliably performed even under the most severe EH conditions, with an improved cost and volume efficiency, i.e., smaller energy storage. Benefiting from the combination of the two schemes, dynamic adjustment of system performance is achieved, while the time is accurately tracked. To illustrate the applicability of this flow to actual sensor systems, two case studies: a bicycle trip computer and a step counter, are presented. Empirical results reveal that, even with a tiny amount of energy harvested ('tens of uJ), our proposed approach can meet application requirements with smaller storage, i.e., 40% and 66% reduction in required capacitance for the presented case studies.

Spreadsheet
Data_Set_Step_Counter.xlsx - Dataset
Available under License Creative Commons Attribution.
Download (6MB)
Spreadsheet
Cycling_Dataset.xlsx - Dataset
Available under License Creative Commons Attribution.
Download (627kB)
Text
D1364_readme.txt - Text
Available under License Creative Commons Attribution.
Download (1kB)

More information

Published date: 4 May 2020

Identifiers

Local EPrints ID: 440644
URI: http://eprints.soton.ac.uk/id/eprint/440644
PURE UUID: ced2c49c-b857-4c8f-a967-a24a89fede9a
ORCID for Geoff Merrett: ORCID iD orcid.org/0000-0003-4980-3894
ORCID for Alexander Weddell: ORCID iD orcid.org/0000-0002-6763-5460

Catalogue record

Date deposited: 12 May 2020 16:48
Last modified: 28 May 2020 00:30

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