The smart e-bike ecosystem integrates internet of things and artificial intelligence

The smart e-bike ecosystem, a combination of internet of things (IoT) and artificial intelligence (AI), has transformed urban mobility. This study aims to shed light on the transformative potential of the smart e-bike ecosystem in the context of urban transportation solutions. It includes real-time navigation, crash detection, and a smart electric drive to encourage sustainable practices and reduce reliance on traditional vehicles. The use of smart locks and parking beacon systems creates a safe and efficient urban infrastructure, encouraging e-bike use. This approach reduces traffic congestion and carbon emissions. IoT frameworks in smart e-bikes improve the user experience and contribute to urban mobility solutions. Real-time monitoring of critical parameters, such as battery levels, speed, and maintenance requirements, keeps riders informed and safe at all times. IoT-enabled features, such as navigation assistance, shorten travel times and improve the efficiency of urban transportation systems. The evolution of smart e-bikes is consistent with the anticipated improvements of 6G networks, which promise to transform communication infrastructures. AI-powered features such as real-time navigation and crash detection make rides safer. The use of smart electric drives and cloud server technology promotes a data-driven approach to transportation. Future research and development should look into the use of advanced localization techniques to improve user experience while addressing accuracy and energy consumption issues.

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Publication Details

Journal
Computer Science and Information Technologies
Published
2025-09-09
DOI
https://doi.org/10.11591/csit.v6i3.p307-314
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

The smart e-bike ecosystem integrates internet of things and artificial intelligence

Hendril Satrian Purnama, Tole Sutikno
Computer Science and Information Technologies
Traffic Prediction and Management Techniques
article

The smart e-bike ecosystem integrates internet of things and artificial intelligence

Hendril Satrian Purnama, Tole Sutikno
article en

Abstract

The smart e-bike ecosystem, a combination of internet of things (IoT) and artificial intelligence (AI), has transformed urban mobility. This study aims to shed light on the transformative potential of the smart e-bike ecosystem in the context of urban transportation solutions. It includes real-time navigation, crash detection, and a smart electric drive to encourage sustainable practices and reduce reliance on traditional vehicles. The use of smart locks and parking beacon systems creates a safe and efficient urban infrastructure, encouraging e-bike use. This approach reduces traffic congestion and carbon emissions. IoT frameworks in smart e-bikes improve the user experience and contribute to urban mobility solutions. Real-time monitoring of critical parameters, such as battery levels, speed, and maintenance requirements, keeps riders informed and safe at all times. IoT-enabled features, such as navigation assistance, shorten travel times and improve the efficiency of urban transportation systems. The evolution of smart e-bikes is consistent with the anticipated improvements of 6G networks, which promise to transform communication infrastructures. AI-powered features such as real-time navigation and crash detection make rides safer. The use of smart electric drives and cloud server technology promotes a data-driven approach to transportation. Future research and development should look into the use of advanced localization techniques to improve user experience while addressing accuracy and energy consumption issues.

Computer Science and Information TechnologiesVol. 6(3)
Universitas Ahmad Dahlan (ID), Embedded Systems (United States) (US)
Openalex Percentile: Top 73%
Traffic Prediction and Management Techniques
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The smart e-bike ecosystem integrates internet of things and artificial intelligence — Hendril Satrian Purnama, Tole Sutikno · Computer Science and Information Technologies (2025) | TGRS Research Map | TGRS