IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v15y2019i8p1550147719867072.html
   My bibliography  Save this article

Deep learning in the fog

Author

Listed:
  • Andrzej Sobecki
  • Julian SzymaÅ„ski
  • David Gil
  • Higinio Mora

Abstract

In the era of a ubiquitous Internet of Things and fast artificial intelligence advance, especially thanks to deep learning networks and hardware acceleration, we face rapid growth of highly decentralized and intelligent solutions that offer functionality of data processing closer to the end user. Internet of Things usually produces a huge amount of data that to be effectively analyzed, especially with neural networks, demands high computing capabilities. Processing all the data in the cloud may not be sufficient in cases when we need privacy and low latency, and when we have limited Internet bandwidth, or it is simply too expensive. It poses a challenge for creating a new generation of fog computing that supports artificial intelligence and selects the architecture appropriate for an intelligent solution. In this article, we show from four perspectives, namely, hardware, software libraries, platforms, and current applications, the landscape of components used for developing intelligent Internet of Things solutions located near where the data are generated. This way, we pinpoint the odds and risks of artificial intelligence fog computing and help in the process of selecting suitable architecture and components that will satisfy all requirements defined by the complex Internet of Things systems.

Suggested Citation

  • Andrzej Sobecki & Julian SzymaÅ„ski & David Gil & Higinio Mora, 2019. "Deep learning in the fog," International Journal of Distributed Sensor Networks, , vol. 15(8), pages 15501477198, August.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:8:p:1550147719867072
    DOI: 10.1177/1550147719867072
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1550147719867072
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1550147719867072?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
    ---><---

    References listed on IDEAS

    as
    1. Daniel J. Fagnant & Kara M. Kockelman, 2018. "Dynamic ride-sharing and fleet sizing for a system of shared autonomous vehicles in Austin, Texas," Transportation, Springer, vol. 45(1), pages 143-158, January.
    2. Xiaolei Ma & Haiyang Yu & Yunpeng Wang & Yinhai Wang, 2015. "Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-17, March.
    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. Shuanfeng Zhao & Chao Wang & Pei Wei & Qingqing Zhao, 2020. "Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    2. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    3. Xu Kuang & Fuquan Zhao & Han Hao & Zongwei Liu, 2019. "Assessing the Socioeconomic Impacts of Intelligent Connected Vehicles in China: A Cost–Benefit Analysis," Sustainability, MDPI, vol. 11(12), pages 1-28, June.
    4. Dubey, Subodh & Sharma, Ishant & Mishra, Sabyasachee & Cats, Oded & Bansal, Prateek, 2022. "A General Framework to Forecast the Adoption of Novel Products: A Case of Autonomous Vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 63-95.
    5. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    6. Li, Dun & Huang, Youlin & Qian, Lixian, 2022. "Potential adoption of robotaxi service: The roles of perceived benefits to multiple stakeholders and environmental awareness," Transport Policy, Elsevier, vol. 126(C), pages 120-135.
    7. Kum Fai Yuen & Do Thi Khanh Huyen & Xueqin Wang & Guanqiu Qi, 2020. "Factors Influencing the Adoption of Shared Autonomous Vehicles," IJERPH, MDPI, vol. 17(13), pages 1-17, July.
    8. Saeed, Tariq Usman & Burris, Mark W. & Labi, Samuel & Sinha, Kumares C., 2020. "An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Muhammad Aqib & Rashid Mehmood & Ahmed Alzahrani & Iyad Katib & Aiiad Albeshri & Saleh M. Altowaijri, 2019. "Rapid Transit Systems: Smarter Urban Planning Using Big Data, In-Memory Computing, Deep Learning, and GPUs," Sustainability, MDPI, vol. 11(10), pages 1-33, May.
    10. Liu, Zhiyong & Li, Ruimin & Dai, Jingchen, 2022. "Effects and feasibility of shared mobility with shared autonomous vehicles: An investigation based on data-driven modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 206-226.
    11. Stefan Illgen & Michael Höck, 2020. "Establishing car sharing services in rural areas: a simulation-based fleet operations analysis," Transportation, Springer, vol. 47(2), pages 811-826, April.
    12. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 48(4), pages 1733-1765, August.
    13. Zwick, Felix & Kuehnel, Nico & Hörl, Sebastian, 2022. "Shifts in perspective: Operational aspects in (non-)autonomous ride-pooling simulations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 300-320.
    14. Li, Yuanyuan & Liu, Yang, 2021. "Optimizing flexible one-to-two matching in ride-hailing systems with boundedly rational users," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    15. Kum Fai Yuen & Grace Chua & Xueqin Wang & Fei Ma & Kevin X. Li, 2020. "Understanding Public Acceptance of Autonomous Vehicles Using the Theory of Planned Behaviour," IJERPH, MDPI, vol. 17(12), pages 1-19, June.
    16. Yu, Qing & Li, Weifeng & Zhang, Haoran & Chen, Jinyu, 2022. "GPS data in taxi-sharing system: Analysis of potential demand and assessment of fuel consumption based on routing probability model," Applied Energy, Elsevier, vol. 314(C).
    17. Mohammad Peyman & Pedro J. Copado & Rafael D. Tordecilla & Leandro do C. Martins & Fatos Xhafa & Angel A. Juan, 2021. "Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems," Energies, MDPI, vol. 14(19), pages 1-26, October.
    18. Shaheen, Susan PhD & Cohen, Adam & Farrar, Emily, 2019. "Carsharing's Impact and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt2f5896tp, Institute of Transportation Studies, UC Berkeley.
    19. María J. Alonso-González & Oded Cats & Niels van Oort & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 0. "What are the determinants of the willingness to share rides in pooled on-demand services?," Transportation, Springer, vol. 0, pages 1-33.
    20. Vij, Akshay & Ryan, Stacey & Sampson, Spring & Harris, Susan, 2020. "Consumer preferences for on-demand transport in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 823-839.

    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:sae:intdis:v:15:y:2019:i:8:p:1550147719867072. 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: SAGE Publications (email available below). General contact details of provider: .

    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.