IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v162y2018icp526-533.html
   My bibliography  Save this article

High-performance self-powered wireless sensor node driven by a flexible thermoelectric generator

Author

Listed:
  • Kim, Yong Jun
  • Gu, Hyun Mo
  • Kim, Choong Sun
  • Choi, Hyeongdo
  • Lee, Gyusoup
  • Kim, Seongho
  • Yi, Kevin K.
  • Lee, Sang Gug
  • Cho, Byung Jin

Abstract

As industrial environments expand and become more automated, wireless sensor networks are attracting attention as an essential technology for efficient operation and safety. A wireless sensor node (WSN), self-powered by an energy harvester, can significantly reduce maintenance costs as well as the manpower costs associated with the replacement of batteries. Among the many studies on energy harvesting technologies for self-powered WSNs, however, the harvested power has been too low to be practically used in industrial environments. In this work, we demonstrate a self-powered WSN driven by a flexible thermoelectric generator (f-TEG) with a significantly improved degree of practicality. We developed a large-area f-TEG which can be wrapped around heat pipes with various diameters, improving their usability and scalability. A study was conducted to optimize the performance of the f-TEG for a particular WSN application, and an f-TEG fabricated with an area of 140 × 113 mm2 harvested 272 mW of energy from a heat pipe at a temperature of 70 °C. We also tested a complete self-powered WSN system capable of the remote monitoring of the heat pipe temperature, ambient temperature, humidity, CO2 and volatile organic compound concentrations via LoRa communication. The fabricated self-powered WSN system can wirelessly transmit the data at distances as long as 500 m.

Suggested Citation

  • Kim, Yong Jun & Gu, Hyun Mo & Kim, Choong Sun & Choi, Hyeongdo & Lee, Gyusoup & Kim, Seongho & Yi, Kevin K. & Lee, Sang Gug & Cho, Byung Jin, 2018. "High-performance self-powered wireless sensor node driven by a flexible thermoelectric generator," Energy, Elsevier, vol. 162(C), pages 526-533.
  • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:526-533
    DOI: 10.1016/j.energy.2018.08.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218315962
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.08.064?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andrew Whitmore & Anurag Agarwal & Li Xu, 2015. "The Internet of Things—A survey of topics and trends," Information Systems Frontiers, Springer, vol. 17(2), pages 261-274, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ko, Jinyoung & Cheon, Seong-Yong & Kang, Yong-Kwon & Jeong, Jae-Weon, 2022. "Design of a thermoelectric generator-assisted energy harvesting block considering melting temperature of phase change materials," Renewable Energy, Elsevier, vol. 193(C), pages 89-112.
    2. Lineykin, Simon & Sitbon, Moshe & Kuperman, Alon, 2021. "Design and optimization of low-temperature gradient thermoelectric harvester for wireless sensor network node on water pipelines," Applied Energy, Elsevier, vol. 283(C).
    3. Daniel Sanin-Villa, 2022. "Recent Developments in Thermoelectric Generation: A Review," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    4. Thi Kim Tuoi, Truong & Van Toan, Nguyen & Ono, Takahito, 2022. "Self-powered wireless sensing system driven by daily ambient temperature energy harvesting," Applied Energy, Elsevier, vol. 311(C).
    5. Rui Quan & Tao Li & Yousheng Yue & Yufang Chang & Baohua Tan, 2020. "Experimental Study on a Thermoelectric Generator for Industrial Waste Heat Recovery Based on a Hexagonal Heat Exchanger," Energies, MDPI, vol. 13(12), pages 1-14, June.
    6. Irene Cappelli & Stefano Parrino & Alessandro Pozzebon & Alessio Salta, 2021. "Providing Energy Self-Sufficiency to LoRaWAN Nodes by Means of Thermoelectric Generators (TEGs)-Based Energy Harvesting," Energies, MDPI, vol. 14(21), pages 1-17, November.
    7. Lineykin, Simon & Maslah, Kareem & Kuperman, Alon, 2020. "Manufacturer-data-only-based modeling and optimized design of thermoelectric harvesters operating at low temperature gradients," Energy, Elsevier, vol. 213(C).
    8. Ashraf Virk, Mati-ur-Rasool & Mysorewala, Muhammad Faizan & Cheded, Lahouari & Aliyu, AbdulRahman, 2022. "Review of energy harvesting techniques in wireless sensor-based pipeline monitoring networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    9. Giacomo Peruzzi & Alessandro Pozzebon, 2020. "A Review of Energy Harvesting Techniques for Low Power Wide Area Networks (LPWANs)," Energies, MDPI, vol. 13(13), pages 1-24, July.
    10. Joung, Jaewon & Cheon, Seong-Yong & Kang, Yong-Kwon & Kim, Minseong & Park, Junseok & Jeong, Jae-Weon, 2023. "Impact of external electric resistance on the power generation in the thermoelectric energy harvesting blocks," Renewable Energy, Elsevier, vol. 212(C), pages 779-791.
    11. Borhani, S.M. & Hosseini, M.J. & Pakrouh, R. & Ranjbar, A.A. & Nourian, A., 2021. "Performance enhancement of a thermoelectric harvester with a PCM/Metal foam composite," Renewable Energy, Elsevier, vol. 168(C), pages 1122-1140.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arfi, Wissal Ben & Nasr, Imed Ben & Kondrateva, Galina & Hikkerova, Lubica, 2021. "The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    2. Hong Jiang & Shuyu Sun & Hongtao Xu & Shukuan Zhao & Yong Chen, 2020. "Enterprises' network structure and their technology standardization capability in Industry 4.0," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(4), pages 749-765, July.
    3. Chae, Bongsug (Kevin), 2018. "The Internet of Things (IoT): A Survey of Topics and Trends using Twitter Data and Topic Modeling," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190376, International Telecommunications Society (ITS).
    4. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    5. Filiou, Despoina & Kesidou, Effie & Wu, Lichao, 2023. "Are smart cities green? The role of environmental and digital policies for Eco-innovation in China," World Development, Elsevier, vol. 165(C).
    6. Damminda Alahakoon & Rashmika Nawaratne & Yan Xu & Daswin Silva & Uthayasankar Sivarajah & Bhumika Gupta, 2023. "Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities," Information Systems Frontiers, Springer, vol. 25(1), pages 221-240, February.
    7. Vasja Roblek & Maja Meško & Alojz Krapež, 2016. "A Complex View of Industry 4.0," SAGE Open, , vol. 6(2), pages 21582440166, June.
    8. Peter M. Bednar & Christine Welch, 0. "Socio-Technical Perspectives on Smart Working: Creating Meaningful and Sustainable Systems," Information Systems Frontiers, Springer, vol. 0, pages 1-18.
    9. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    10. Yao, Meifang & Di, He & Zheng, Xianrong & Xu, Xiaobo, 2018. "Impact of payment technology innovations on the traditional financial industry: A focus on China," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 199-207.
    11. Payam Hanafizadeh & Parastou Hatami & Morteza Analoui & Amir Albadvi, 2021. "Business model innovation driven by the internet of things technology, in internet service providers’ business context," Information Systems and e-Business Management, Springer, vol. 19(4), pages 1175-1243, December.
    12. Qinglan Liu & Adriana Hofmann Trevisan & Miying Yang & Janaina Mascarenhas, 2022. "A framework of digital technologies for the circular economy: Digital functions and mechanisms," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2171-2192, July.
    13. Eryarsoy, Enes & Kilic, Huseyin Selcuk & Zaim, Selim & Doszhanova, Marzhan, 2022. "Assessing IoT challenges in supply chain: A comparative study before and during- COVID-19 using interval valued neutrosophic analytical hierarchy process," Journal of Business Research, Elsevier, vol. 147(C), pages 108-123.
    14. Federica Cena & Luca Console & Assunta Matassa & Ilaria Torre, 2019. "Multi-dimensional intelligence in smart physical objects," Information Systems Frontiers, Springer, vol. 21(2), pages 383-404, April.
    15. Pan Wang & Ricardo Valerdi & Shangming Zhou & Ling Li, 2015. "Introduction: Advances in IoT research and applications," Information Systems Frontiers, Springer, vol. 17(2), pages 239-241, April.
    16. Rui Xu & Changqing Wu & Shengying Zhu & Baodong Fang & Wei Wang & Lida Xu & Wu He, 2017. "A rapid maneuver path planning method with complex sensor pointing constraints in the attitude space," Information Systems Frontiers, Springer, vol. 19(4), pages 945-953, August.
    17. Payam Hanafizadeh & Ferdos Hatami Lankarani & Shahrokh Nikou, 2022. "Perspectives on management theory’s application in the internet of things research," Information Systems and e-Business Management, Springer, vol. 20(4), pages 749-787, December.
    18. Shang, Juan & Li, Pengfei & Li, Ling & Chen, Yong, 2018. "The relationship between population growth and capital allocation in urbanization," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 249-256.
    19. Jens Passlick & Sonja Dreyer & Daniel Olivotti & Lukas Grützner & Dennis Eilers & Michael H. Breitner, 2021. "Predictive maintenance as an internet of things enabled business model: A taxonomy," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(1), pages 67-87, March.
    20. Salvatore T. March & Gary D. Scudder, 2019. "Predictive maintenance: strategic use of IT in manufacturing organizations," Information Systems Frontiers, Springer, vol. 21(2), pages 327-341, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:162:y:2018:i:c:p:526-533. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.