A new era for green and intelligent transportation: LoRa meets distributed machine learning
Beijing Institute of Technology Press Co., Ltd
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When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system
view moreCredit: GREEN ENERGY AND INTELLIGENT TRANSPORTATION
In a world where urban traffic congestion and environmental concerns are escalating, innovative solutions are crucial for creating sustainable and efficient transportation systems. A groundbreaking study led by Hongbin Ma from the National Key Lab of Autonomous Intelligent Unmanned Systems at the School of Automation, Beijing Institute of Technology, introduces a pioneering approach that combines Long Range (LoRa) technology with distributed machine learning to optimize network connectivity for green and intelligent transportation systems.
LoRa technology addresses the challenges of outdoor monitoring by dynamically adjusting transmission parameters to enhance communication efficiency and range with minimal power. The study employs innovative spreading factor (SF) and hybrid models, along with K-means and DBSCAN algorithms, to optimize the allocation of end devices (EDs). This approach is particularly effective for electric vehicle (EV) station monitoring, reducing traffic congestion and pollution while ensuring robust communication across different gateway configurations.
3 Integrating machine learning with parameter adjustment models, the research significantly improves network efficiency and reliability. It uses a log-distance path loss model to estimate signal losses and explores bandwidth options and duty cycles to prevent network saturation. The strategies effectively extend the operational life of EDs, achieving a zero packet rejection rate in the hybrid model. The study highlights LoRa's potential to transform urban transportation with scalable solutions that enhance network performance and energy efficiency, addressing key urban challenges.
The implications of this research are profound for urban planners and policymakers. By integrating LoRa technology with distributed machine learning, the network connectivity of green intelligent transportation systems can be optimized. Applying LoRa technology to the monitoring systems of parking lots and EV stations significantly enhances the efficiency of goods tracking and logistics management. By utilizing LoRa EDs and strategically placed LoRa gateways, a seamless tracking infrastructure is established, which not only improves operational efficiency but also provides precise inventory levels and location data. This practical application ensures robust communication under different gateway configurations, reduces traffic congestion and pollution, and extends the operational life of terminal devices.
In the future, this technology is expected to revolutionize urban transportation by paving the way for sustainable and intelligent transportation solutions through enhanced network efficiency and reduced environmental impact. The research emphasizes the importance of reducing computational power consumption and establishing decentralized networks, achieving this through model parallelism and federated learning. By improving pure ALOHA and slotted ALOHA mechanisms and implementing distributed algorithms, the study aims to further enhance the performance of outdoor networks. Techniques such as synchronous and asynchronous gradient descent will be used to improve efficiency and responsiveness, while clustering algorithms will play a key role in reducing computational power requirements.
In summary, integrating LoRa technology into green intelligent transportation systems sets new standards for urban transportation. The research findings highlight the potential of LoRa technology in creating resilient and energy-efficient transportation systems, providing a scalable and effective solution to the growing challenges of traffic management. As major cities worldwide strive to manage traffic and reduce environmental impact, the insights from this research offer a forward-looking perspective on the future of urban transportation, showcasing the transformative potential of combining LoRa technology with distributed machine learning.
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References
Authors: Malak Abid Ali Khan a, Hongbin Ma *a, Arshad Farhad b, Asad Mujeeb c, Imran Khan Mirani d, Muhammad Hamza e
Affiliations:
a National Key Lab of Autonomous Intelligent Unmanned Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
b Department of Computer Science, Namal University, Pakistan
c Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
d Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
e School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Article link: https://www.sciencedirect.com/science/article/pii/S2773153724000562
Journal
Green Energy and Intelligent Transportation
Method of Research
Experimental study
Subject of Research
Not applicable
Article Title
When LoRa meets distributed machine learning to optimize the network connectivity for green and intelligent transportation system
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