wireless sensor networks

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papersTODAY 04:00 UTC

Hierarchical Q-Learning Routing Proposed for Multi-Charger Scheduling in Wireless Sensor Networks

A new arXiv preprint introduces HQARRF, a scheduling approach that combines hierarchical Q-learning with force-aware routing to coordinate multiple chargers in wireless rechargeable sensor networks. The method aims to balance sensor death risk, charger energy limits, travel costs, return-to-base feasibility and inter-charger coordination, which urgency-driven schedulers handle poorly. The work is categorized under machine learning research.

reinforcement-learning/scheduling/wireless-networks/research

papersSEP 12 04:00 UTC

Machine Learning and Weather Data Used to Predict Train Delays in Finland

Researchers developed a machine learning approach that forecasts railway delays in Finland by combining weather observations with other operational data. The work situates such environmental sensing within future 6G-enabled wireless sensor networks and edge computing, which the authors argue will improve real-time reliability. The study is published as an arXiv preprint.

railways/weather/machine-learning/forecasting