IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v14y2022i12p363-d994657.html
   My bibliography  Save this article

TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review

Author

Listed:
  • Nikolaos Schizas

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Aristeidis Karras

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Christos Karras

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

  • Spyros Sioutas

    (Computer Engineering and Informatics Department, University of Patras, 26504 Patras, Greece)

Abstract

The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies.

Suggested Citation

  • Nikolaos Schizas & Aristeidis Karras & Christos Karras & Spyros Sioutas, 2022. "TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review," Future Internet, MDPI, vol. 14(12), pages 1-45, December.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:12:p:363-:d:994657
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/14/12/363/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/14/12/363/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yong-Hong Kuo & Andrew Kusiak, 2019. "From data to big data in production research: the past and future trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4828-4853, August.
    2. Luiz Oliveira & Joel J. P. C. Rodrigues & Sergei A. Kozlov & Ricardo A. L. Rabêlo & Victor Hugo C. de Albuquerque, 2019. "MAC Layer Protocols for Internet of Things: A Survey," Future Internet, MDPI, vol. 11(1), pages 1-42, January.
    3. Zezhou Wu & Mingyang Jiang & Heng Li & Xiaoling Zhang, 2021. "Mapping the Knowledge Domain of Smart City Development to Urban Sustainability: A Scientometric Study," Journal of Urban Technology, Taylor & Francis Journals, vol. 28(1-2), pages 29-53, April.
    Full references (including those not matched with items on IDEAS)

    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. Mona Treude & Ralf Schüle & Hans Haake, 2022. "Smart Sustainable Cities—Case Study Südwestfalen Germany," Sustainability, MDPI, vol. 14(10), pages 1-16, May.
    2. Ray Qing Cao & Dara G. Schniederjans & Vicky Ching Gu, 2021. "Stakeholder sentiment in service supply chains: big data meets agenda-setting theory," Service Business, Springer;Pan-Pacific Business Association, vol. 15(1), pages 151-175, March.
    3. Ailian Zhang & Mengmeng Pan, 2020. "“Smart Process” of Medical Innovation: The Synergism Based on Network and Physical Space," IJERPH, MDPI, vol. 17(11), pages 1-17, May.
    4. Han, Yongming & Cao, Lian & Guo, Qing & Geng, Zhiqiang & Yang, Weiyang & Fan, Jinzhen & Liu, Min, 2024. "Economy and carbon dioxide emissions effects of energy structures in China: Evidence based on a novel AHP-SBMDEA model," Energy, Elsevier, vol. 290(C).
    5. Mohammad Mansour & Amal Gamal & Ahmed I. Ahmed & Lobna A. Said & Abdelmoniem Elbaz & Norbert Herencsar & Ahmed Soltan, 2023. "Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions," Energies, MDPI, vol. 16(8), pages 1-39, April.
    6. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    7. Dutta, Pankaj & Choi, Tsan-Ming & Somani, Surabhi & Butala, Richa, 2020. "Blockchain technology in supply chain operations: Applications, challenges and research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    8. Khan, Waqar Ahmed & Ma, Hoi-Lam & Ouyang, Xu & Mo, Daniel Y., 2021. "Prediction of aircraft trajectory and the associated fuel consumption using covariance bidirectional extreme learning machines," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    9. Nitin S. Solke & Pritesh Shah & Ravi Sekhar & T. P. Singh, 2022. "Machine Learning-Based Predictive Modeling and Control of Lean Manufacturing in Automotive Parts Manufacturing Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(1), pages 89-112, March.
    10. Abdallah Y. Alma’aitah & Mohammad A. Massad, 2021. "Reader–Tag Commands via Modulation Cutoff Intervals in RFID Systems," Future Internet, MDPI, vol. 13(9), pages 1-13, September.
    11. Li, Mingxing & Huang, George Q., 2021. "Production-intralogistics synchronization of industry 4.0 flexible assembly lines under graduation intelligent manufacturing system," International Journal of Production Economics, Elsevier, vol. 241(C).
    12. Qinghong Cui & Ruirui Wei & Rong Huang & Xiancun Hu & Guangbin Wang, 2022. "The Effect of Perceived Risk on Public Participation Intention in Smart City Development: Evidence from China," Land, MDPI, vol. 11(9), pages 1-14, September.
    13. Alnoor Bhimani, 2020. "Digital data and management accounting: why we need to rethink research methods," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 31(1), pages 9-23, April.
    14. Georgios Tsoumanis & Asterios Papamichail & Vasileios Dragonas & George Koufoudakis & Constantinos T. Angelis & Konstantinos Oikonomou, 2020. "Implementation of a Topology Independent MAC (TiMAC) Policy on a Low-Cost IoT System," Future Internet, MDPI, vol. 12(5), pages 1-13, May.
    15. Osinga, Sjoukje A. & Paudel, Dilli & Mouzakitis, Spiros A. & Athanasiadis, Ioannis N., 2022. "Big data in agriculture: Between opportunity and solution," Agricultural Systems, Elsevier, vol. 195(C).
    16. Sharina Tajul Urus & Intan Waheedah Othman & Zarinah Abdul Rasit & Noraizah Abu Bakar & Sharifah Nazatul Faiza Syed Mustapha Nazri, 2023. "Beyond the Hype of Big Data Analytics Deployment: Conceptualization and Challenges Epistemology," Business and Economic Research, Macrothink Institute, vol. 13(2), pages 74-111, December.
    17. Qinghong Cui & Run Chen & Ruirui Wei & Xiancun Hu & Guangbin Wang, 2023. "Smart Mega-City Development in Practice: A Case of Shanghai, China," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    18. Sun, Xuting & Kuo, Yong-Hong & Xue, Weili & Li, Yanzhi, 2024. "Technology-driven logistics and supply chain management for societal impacts," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    19. Hisham Alidrisi, 2021. "Measuring the Environmental Maturity of the Supply Chain Finance: A Big Data-Based Multi-Criteria Perspective," Logistics, MDPI, vol. 5(2), pages 1-24, April.
    20. Bai Liu & Shuyan Guo & Bin Ding, 2020. "Technical Blossom in Medical Care: The Influence of Big Data Platform on Medical Innovation," IJERPH, MDPI, vol. 17(2), pages 1-17, January.

    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:gam:jftint:v:14:y:2022:i:12:p:363-:d:994657. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.