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FogETex: Fog Computing Framework for Electronic Textile Applications
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AI
DOI：10.1109/JIOT.2024.3490981.png)
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摘要
En 中文
Textile products are present in almost every aspect of human life. With the introduction of electronic textiles (e-textiles), textile products have become capable of converting various physiological and environmental stimuli into electrical signals, many of which are of vital importance to humans. Therefore, these products require real-time (low-latency) and robust computing systems. However, due to comfort considerations, they cannot accommodate powerful computing resources. In this study, a novel fog computing-based framework (FogETex) is proposed to meet the needs of e-textile applications. FogETex is a Platform-as-a-Service model that is cross-platform supported, scalable, and operates in real time. This framework encompasses end-to-end integration of the system, including Textile-based Internet of Things (T-IoT) device, fog devices, and the cloud. Fog devices consist of a broker that manages the fog node and a worker that handles incoming computation requests. Sensor data is transmitted to the fog node through a mobile application, and system architecture can be monitored through developed user interfaces. Resource usage from broker devices is monitored in real time to prevent worker devices from experiencing overload. For the system case study, a deep-learning-based gait phase analysis application using textile-based capacitive sensors is employed. FogETex was evaluated in terms of time characteristics, resource usage, and network bandwidth usage using a mock client to determine the ideal system performance and an actual client to conduct real-world tests. The fog devices outperformed the cloud system in these metrics. Besides being developed primarily for e-textile applications, the FogETex framework can accommodate other IoT devices as well.
Keyword:
Smart textiles
Sensors
Internet of Things
Edge computing
Cloud computing
Real-time systems
Wearable devices
Textiles
Monitoring
Medical services
Deep learning
electronic textile (e-textile)
fog computing
gait phase analysis
Raspberry Pi
socket programming
