bornabr/RSCNet
The Pytorch implementation of "RSCNet: Dynamic CSI Compression for Cloud-based WiFi Sensing"
This helps operations engineers and IoT developers reduce the data transmission costs and bandwidth needs for real-time sensing using WiFi signals. It takes raw WiFi Channel State Information (CSI) from IoT devices and outputs highly compressed CSI that still maintains high accuracy for various sensing applications when analyzed in the cloud. This is ideal for those managing large-scale IoT deployments or developing new WiFi sensing solutions.
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Use this if you need to perform accurate human activity recognition or other environmental sensing using WiFi signals but are constrained by network bandwidth or the processing power of your IoT devices.
Not ideal if your sensing application does not rely on WiFi Channel State Information or if you do not need to transmit data to a cloud server for analysis.
Stars
13
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 25, 2024
Commits (30d)
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