{"ID":5439483,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-02T19:06:01.127452785Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30843","arxiv_id":"2606.30843","title":"TinyML for On-Device and Edge Analytics in Wireless Networks: A Survey of Deployments, Opportunities, and Concept-Drift Mitigation","abstract":"Ubiquitous intelligence is essential for enabling real-time, adaptive, autonomous, and scalable operations in the next generation of wireless networks. However, this poses significant challenges in data management and energy consumption on the end-device/edge side, specially under dynamic environmental conditions. This has driven the adoption of tiny machine learning (tinyML), which offers data-driven optimization at the end-device/edge side. In this work, we survey and thoroughly discuss various tapped/untapped deployment possibilities of tinyML in wireless networks. We identify existing frameworks, accustomed to design tinyML algorithms, that could be utilized to solve a range of wireless network problems. We present a federated learning-based tinyML model update procedure, for both battery-powered and batteryless end-devices, to resolve the concept drift problem faced by tinyML models. Furthermore, we discuss the update-aware checkpointing, fault-tolerant bootloader, and intermittent-aware modify operation, which could support federated learning-based tinyML model update in the case of batteryless end-devices. Overall, this paper spells out several areas where end-device/edge intelligence can be utilized in the next generation of wireless systems, as well as ways to mitigate the concept drift problem faced in the case of end-device intelligence.","short_abstract":"Ubiquitous intelligence is essential for enabling real-time, adaptive, autonomous, and scalable operations in the next generation of wireless networks. However, this poses significant challenges in data management and energy consumption on the end-device/edge side, specially under dynamic environmental conditions. This...","url_abs":"https://arxiv.org/abs/2606.30843","url_pdf":"https://arxiv.org/pdf/2606.30843v1","authors":"[\"Prasoon Raghuwanshi\",\"Vimal Bhatia\",\"Sridhar Iyer\",\"Matti Latva-aho\",\"Onel Luis Alcaraz López\"]","published":"2026-06-29T19:20:45Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"eess.SP\"]","methods":"[]","has_code":false}