Smart Gravel or Smart Wireless Sensors in Rail: A Comprehensive Review

In this paper, we explore the use of wireless smart sensors in railway systems, focusing on the concept of "smart gravel" -a system that embeds wireless sensors within the ballast to monitor track conditions.We first provide an overview over the landscape by highlighting reviews from other authors on the topic.We then explore the topics embedded AI, and energy harvesting in the rail domain by reviewing existing literature.Following we are discussing works from other authors which came the closest to realising the idea of smart gravel.Through a comprehensive review of the existing literature, we identify the key challenges in the field, including the need for open datasets in research, more and longer field tests and the adoption of MEMS sensors in the railway industry.Possible research gaps according to our findings are the development of dependable smart gravel systems as well as bringing federated learning into the picture for railway monitoring.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.3850/978-981-94-3281-3_esrel-sra-e2025-p2159-cd
Primary Topic
Advanced Fiber Optic Sensors
Type
article
Field-Weighted Citation Impact
0.00
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article

Smart Gravel or Smart Wireless Sensors in Rail: A Comprehensive Review

J. M. Cordes, Joyson Joy
Advanced Fiber Optic Sensors
article

Smart Gravel or Smart Wireless Sensors in Rail: A Comprehensive Review

J. M. Cordes, Joyson Joy
article en

Abstract

In this paper, we explore the use of wireless smart sensors in railway systems, focusing on the concept of "smart gravel" -a system that embeds wireless sensors within the ballast to monitor track conditions.We first provide an overview over the landscape by highlighting reviews from other authors on the topic.We then explore the topics embedded AI, and energy harvesting in the rail domain by reviewing existing literature.Following we are discussing works from other authors which came the closest to realising the idea of smart gravel.Through a comprehensive review of the existing literature, we identify the key challenges in the field, including the need for open datasets in research, more and longer field tests and the adoption of MEMS sensors in the railway industry.Possible research gaps according to our findings are the development of dependable smart gravel systems as well as bringing federated learning into the picture for railway monitoring.

Openalex Percentile: Top 72%
Advanced Fiber Optic Sensors
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