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IoT and ML Based Approach for Highway Monitoring and Streetlamp Controlling

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Machine Intelligence and Emerging Technologies (MIET 2022)

Abstract

Excessive speed and violating traffic rules may cause dangerous road accident. Some reports show that around 3700 people die every day due to road accident. Controlling vehicle speed and proper automated street lighting system may mitigate this problem. This work implements an automated internet of things and machine learning based system to control streetlamps with vehicle speed tracking. The developed machine learning model is capable to guesstimate the speed of the vehicle on the highway and report if there is any excessive speed. The automated streetlamp is integrated with the system that can provide proper illumination considering the environment condition. The proposed system showed good results after practical implementation.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

    Mushfiqur Rahman, Md. Faridul Islam Suny, Jerin Tasnim, Md. Sabab Zulfiker, Mohammad Jahangir Alam & Tajim Md. Niamat Ullah Akhund

Authors
  1. Mushfiqur Rahman
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  2. Md. Faridul Islam Suny
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  3. Jerin Tasnim
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  4. Md. Sabab Zulfiker
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  5. Mohammad Jahangir Alam
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  6. Tajim Md. Niamat Ullah Akhund
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Corresponding author

Correspondence to Mushfiqur Rahman .

Editor information

Editors and Affiliations

  1. Noakhali Science and Technology University, Noakhali, Bangladesh

    Md. Shahriare Satu

  2. The University of Queensland, St. Lucia, QLD, Australia

    Mohammad Ali Moni

  3. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  4. Daffodil International University, Dhaka, Bangladesh

    Mohammad Shamsul Arefin

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Rahman, M., Suny, M.F.I., Tasnim, J., Zulfiker, M.S., Alam, M.J., Akhund, T.M.N.U. (2023). IoT and ML Based Approach for Highway Monitoring and Streetlamp Controlling. In: Satu, M.S., Moni, M.A., Kaiser, M.S., Arefin, M.S. (eds) Machine Intelligence and Emerging Technologies. MIET 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 491. Springer, Cham. https://doi.org/10.1007/978-3-031-34622-4_30

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  • DOI: https://doi.org/10.1007/978-3-031-34622-4_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-34621-7

  • Online ISBN: 978-3-031-34622-4

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