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An Indoor Test Methodology for Solar-Powered Wireless Sensor Networks

Authors:

Joanna Slota-Newson,

Stephen A. JarvisAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 16, Issue 3

Article No.: 82, Pages 1 - 25

https://doi.org/10.1145/2994604

Published: 28 March 2017 Publication History

Abstract

Repeatable and accurate tests are important when designing hardware and algorithms for solar-powered wireless sensor networks (WSNs). Since no two days are exactly alike with regard to energy harvesting, tests must be carried out indoors. Solar simulators are traditionally used in replicating the effects of sunlight indoors; however, solar simulators are expensive, have lighting elements that have short lifetimes, and are usually not designed to carry out the types of tests that hardware and algorithm designers require. As a result, hardware and algorithm designers use tests that are inaccurate and not repeatable (both for others and also for the designers themselves). In this article, we propose an indoor test methodology that does not rely on solar simulators. The test methodology has its basis in astronomy and photovoltaic cell design. We present a generic design for a test apparatus that can be used in carrying out the test methodology. We also present a specific design that we use in implementing an actual test apparatus. We test the efficacy of our test apparatus and, to demonstrate the usefulness of the test methodology, perform experiments akin to those required in projects involving solar-powered WSNs. Results of the said tests and experiments demonstrate that the test methodology is an invaluable tool for hardware and algorithm designers working with solar-powered WSNs.

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Cited By

Awasthi KKumar M(2022)Survey of Supervised Machine Learning Techniques in Wireless Sensor NetworkAdvances in VLSI, Communication, and Signal Processing10.1007/978-981-19-2631-0_18(201-214)Online publication date: 5-Oct-2022
https://doi.org/10.1007/978-981-19-2631-0_18
Chander B(2022)Artificial Neural Networks and Support Vector Machine for IoTArtificial Intelligence-based Internet of Things Systems10.1007/978-3-030-87059-1_3(77-103)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-87059-1_3
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Index Terms

An Indoor Test Methodology for Solar-Powered Wireless Sensor Networks

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 16, Issue 3

Special Issue on Embedded Computing for IoT, Special Issue on Big Data and Regular Papers

August 2017

610 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3072970

Editor:
Sandeep K. Shukla
Indian Institute of Technology, India

Issue’s Table of Contents

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 28 March 2017

Accepted: 01 September 2016

Revised: 01 September 2016

Received: 01 June 2015

Published in TECS Volume 16, Issue 3

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UK Technology Strategy Board (TSB) Emerging Technologies Programme
Republic of the Philippines' Engineering Research and Development for Technology (ERDT) Program

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Cited By

Awasthi KKumar M(2022)Survey of Supervised Machine Learning Techniques in Wireless Sensor NetworkAdvances in VLSI, Communication, and Signal Processing10.1007/978-981-19-2631-0_18(201-214)Online publication date: 5-Oct-2022
https://doi.org/10.1007/978-981-19-2631-0_18
Chander B(2022)Artificial Neural Networks and Support Vector Machine for IoTArtificial Intelligence-based Internet of Things Systems10.1007/978-3-030-87059-1_3(77-103)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-87059-1_3
Kim DAhn JShin JCha H(2021)Ray Tracing-based Light Energy Prediction for Indoor Batteryless SensorsProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34480865:1(1-27)Online publication date: 30-Mar-2021
https://dl.acm.org/doi/10.1145/3448086
Raut AKhandait S(2021)Machine Learning Algorithms in WSNs and its Applications2021 International Conference on Computational Intelligence and Computing Applications (ICCICA)10.1109/ICCICA52458.2021.9697319(1-5)Online publication date: 26-Nov-2021
https://doi.org/10.1109/ICCICA52458.2021.9697319
Amutha JSharma SSharma S(2021)Strategies based on various aspects of clustering in wireless sensor networks using classical, optimization and machine learning techniquesComputer Science Review10.1016/j.cosrev.2021.10037640:COnline publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100376
Ilchev SAndreev RIlcheva Z(2021)Autonomous Microcontroller System for Sensor Data Gathering Relying on Solar-Power and UltracapacitorsWireless Personal Communications: An International Journal10.1007/s11277-021-08828-y121:3(2393-2405)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11277-021-08828-y
Sudha Singh YSehrawat HJaglan V(2021)Approach of Machine Learning Algorithms to Deal with Challenges in Wireless Sensor NetworkSoft Computing: Theories and Applications10.1007/978-981-16-1740-9_31(375-395)Online publication date: 31-Jul-2021
https://doi.org/10.1007/978-981-16-1740-9_31
Pant YSharma R(2020)RPSO Optimization with machine learning in WSN2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC50313.2020.9315774(105-110)Online publication date: 6-Nov-2020
https://doi.org/10.1109/PDGC50313.2020.9315774
Geissdoerfer KChwalisz MZimmerling MGanti RJiang XPicco GZhou X(2019)ShepherdProceedings of the 17th Conference on Embedded Networked Sensor Systems10.1145/3356250.3360042(83-95)Online publication date: 10-Nov-2019
https://dl.acm.org/doi/10.1145/3356250.3360042
Lata SMehfuz S(2019)Machine Learning based Energy Efficient Wireless Sensor Network2019 International Conference on Power Electronics, Control and Automation (ICPECA)10.1109/ICPECA47973.2019.8975526(1-5)Online publication date: Nov-2019
https://doi.org/10.1109/ICPECA47973.2019.8975526

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