An Internet-of-Things enabled system for road icing detection and prediction

Chen, Z., Xiong, G., Sun, Y. , Li, Y. and Li, Y. (2022) An Internet-of-Things enabled system for road icing detection and prediction. IEEE Internet of Things Journal, 9(20), pp. 20257-20269. (doi: 10.1109/JIOT.2022.3175220)

Abstract

Road icing has become one of the most critical factors threatening traffic safety. This paper proposes an Internet-of-Things(IoT) enabled road icing detection and prediction system. In the proposed system, we first design a low-power icing sensor equipped with IoT function to periodically collect current road status and transmit the sampled data to IoT gateway through Long Range Radio(LoRa). Then we design a simple but effective algorithm deployed on IoT gateway to identify road icing in time. The algorithm is proposed based on the change trend of the sampled data of the road state, and can be adapted to the icing recognition on the road covered with various impurities. Furthermore, we put forward a newly designed deep neural network model called Trans-CGAN to achieve accurate road icing prediction even the positive and negative samples are imbalanced. Through a real system deployment and experiments, the results show that our proposed system can detect the formation of road icing effectively and timely, and shows better prediction performance of road icing than several representative models.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Natural Science Foundation of China under Grant 62071077, Grant 61671096 and Grant U1764263, in part by Scientific and Technological innovation project of scientific research institutions of Chongqing (No.cstc2021jxjl20010), in part by Banan District Scientific and Technological Achievements Transformation and Industrialization Project.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Sun, Dr Yao
• Authors: Chen, Z., Xiong, G., Sun, Y., Li, Y., and Li, Y.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Internet of Things Journal
• Publisher: IEEE
• ISSN: 2327-4662
• ISSN (Online): 2327-4662
• Published Online: 16 May 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Internet of Things Journal 9(20): 20257-20269
• Publisher Policy: Reproduced in accordance with the publisher copyright policy