Formulates energy-aware scheduling for battery-less IoT as a long-term average-reward MDP with i.i.d. energy arrivals and derives an optimal stationary threshold-based scheduler that improves full-chain completion and reduces power failures.
Foundations for energ y-aware zero-energy devices: From energy sensing to adaptive proto cols
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Survey of TinyML deployments and opportunities in wireless networks, with a proposed federated learning-based update procedure for concept drift mitigation on battery-powered and batteryless devices.
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MDP-based Energy-aware Task Scheduling for Battery-less IoT
Formulates energy-aware scheduling for battery-less IoT as a long-term average-reward MDP with i.i.d. energy arrivals and derives an optimal stationary threshold-based scheduler that improves full-chain completion and reduces power failures.
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TinyML for On-Device and Edge Analytics in Wireless Networks: A Survey of Deployments, Opportunities, and Concept-Drift Mitigation
Survey of TinyML deployments and opportunities in wireless networks, with a proposed federated learning-based update procedure for concept drift mitigation on battery-powered and batteryless devices.