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Found 6 papers, 2 papers with code
FLARE: Detection and Mitigation of Concept Drift for Federated Learning based IoT Deployments
In order to simultaneously reduce communication traffic and maintain the integrity of inference models, we introduce FLARE, a novel lightweight dual-scheduler FL framework that conditionally transfers training data, and deploys models between edge and sensor endpoints based on observing the model's training behaviour and inference statistics, respectively.
Deep Transfer Learning for WiFi Localization
Finally, an ablation study of the training dataset shows that, in both office and sport hall scenarios, after reusing the feature extraction layers of the base model, only 55% of the training data is required to obtain the models' accuracy similar to the base models.
Wireless Localisation in WiFi using Novel Deep Architectures
Meanwhile, using a well-organised architecture, the neural network models can be trained directly with raw data from the CSI and localisation features can be automatically extracted to achieve accurate position estimates.
Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies
High accuracy localisation technologies exist but are prohibitively expensive to deploy for large indoor spaces such as warehouses, factories, and supermarkets to track assets and people.
How Agile is the Adaptive Data Rate Mechanism of LoRaWAN?
The LoRaWAN based Low Power Wide Area networks aim to provide long-range connectivity to a large number of devices by exploiting limited radio resources.
Networking and Internet Architecture
Low Power Wide Area Network Analysis: Can LoRa Scale?
Low Power Wide Area (LPWA) networks are making spectacular progress from design, standardisation, to commercialisation.
Networking and Internet Architecture
