Sustainable and smart rail transit based on advanced self-powered sensing technology
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View abstract on PubMed
Summary
This summary is machine-generated.Rail transit is advancing with self-powered, intelligent systems using artificial intelligence and the internet of things (AIoT). This review explores AIoT frameworks for sustainable energy and infrastructure management in railways.
Area Of Science
- Engineering
- Computer Science
- Transportation Technology
Background
- Expanding railway networks necessitate sustainable energy and intelligent infrastructure management.
- Rapid advancements in rail self-powered technology integrate artificial intelligence (AI) and the internet of things (IoT).
Purpose Of The Study
- To review self-powered and self-sensing systems in rail transit, analyzing their characteristics and potential.
- To explore an IoT framework for sustainable self-powered sensing in railways.
- To investigate emerging technologies and future trends in rail transit.
Main Methods
- Literature review of self-powered and self-sensing systems in rail transit.
- Analysis of AIoT frameworks, including device nodes, network communication, and platform deployment.
- Exploration of cloud and edge computing, machine learning (ML), and deep learning (DL) applications in railway IoT.
Main Results
- Identified current characteristics and innovative potentials of self-powered and self-sensing systems.
- Proposed an IoT framework leveraging sustainable self-powered sensing systems.
- Highlighted the role of cloud/edge computing and ML/DL for comprehensive railway monitoring and management.
Conclusions
- Sustainable self-powered sensing systems are crucial for intelligent railway infrastructure management.
- AIoT, supported by cloud/edge computing and ML/DL, offers significant potential for enhancing railway operations.
- Cross-disciplinary research is vital for future advancements in rail transit technology.
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