arXiv AI By Mattia Consani, Denisa-Andreea Constantinescu, {\AA}se H{\aa}tveit, Titus Venverloo, Fabio Duarte, Carlo Ratti, David Atienza

Dot-Flik: A Scalable Edge AI Architecture for Distributed Insect Monitoring

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arXiv:2606. 26121v1 Announce Type: cross Abstract: Global insect population declines necessitate scalable, continuous monitoring systems, yet existing vision-based solutions remain constrained by high hardware costs, energy demands, and reliance on centralized processing or cloud connectivity.

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arXiv Machine Learning
Jul 28

Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs

arXiv:2506. 03168v2 Announce Type: replace-cross Abstract: Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital transformation to facilitate efficient big data processing.

By Dawen Jiang, Zhishu Shen, Qiushi Zheng, Tiehua Zhang, Wei Xiang, Jiong Jin
arXiv AI
Jun 2

Project SPARROW and the Future of Conservation Technology

arXiv:2606. 00108v1 Announce Type: cross Abstract: Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessibility.

By Juan M. Lavista Ferres, Carl Chalmers, Bruno Demuro Segundo, Zhongqi Miao, Andres Hernandez Celis, Federico Alves Torres, Isai Daniel Chacon Silva, Anthony Cintron Roman, Allen Kim, Meygha Machado, Luana Marotti, Amy Michaels, Daniela Ruiz Lopez, Catherine Romero, Rahul Dodhia, Inbal Becker-Reshef, Pablo Arbelaez
arXiv AI
Jul 7

Agentic IoT: Architectures, Applications, and Challenges Toward the Internet of Agents

arXiv:2607. 04219v1 Announce Type: new Abstract: The integration of AI into Internet of Things (AIoT) systems has gradually transformed them from passive data collection infrastructures into intelligent systems capable of anomaly detection, predictive maintenance, classification, forecasting, and optimization.

By R\"umeysa Hilal Sevin\c{c}, Bahaeddin T\"urko\u{g}lu, \.Ibrahim K\"ok
arXiv Machine Learning
Jul 14

DynaFilter: Cloud-driven Dynamic Filtering for Satellite Edge Intelligence

arXiv:2607. 10098v1 Announce Type: cross Abstract: Modern satellite edge systems, including those performing remote sensing tasks such object detection and tracking, are characterized by severely limited bandwidth and intermittent connections, making continuous data transmission to the cloud impractical.

By Ziyang Zhang, Jie Liu, Luca Mottola