@inproceedings{2c14303802a245df924a6cad621a5a52,
title = "Edge-based Computing Challenges and Opportunities for Sensor Fusion: Panel Review",
abstract = "Sensor, data, and information fusion techniques are typically implemented in a centralized approach that requires cloud servers to process the large amounts of data. Recently, collaborative computing approaches can support effective and efficient distributed and decentralized information fusion communication among many sensors at the edge. The panel highlighted opportunities and challenges of edge computing sensor fusion designs. Examples of opportunities included decentralized timely response, extended coverage, and resilient redundancy; all of which will be developed in the near future to enhance healthcare, smart cities, and surveillance. The challenges to make these opportunities a reality would include low size, weight, and power (SWaP) constraints, privacy and security concerns, as well as standardized data flow and architecture protocols. Common to the discussion was that with more heterogeneous edge sensing, data and information flow techniques are needed to harness the prospects of a distributed enterprise data fusion network.",
keywords = "Active Learning, Deep Learning, Edge Computing, Information Fusion, Transformers",
author = "Erik Blasch and Genshe Chen and Yu Chen and Andreas Savakis and Fred Daum and Lynne Grewe",
note = "Publisher Copyright: {\textcopyright} 2025 SPIE.; Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV 2025 ; Conference date: 14-04-2025 Through 16-04-2025",
year = "2025",
doi = "10.1117/12.3052850",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Ivan Kadar and Blasch, \{Erik P.\} and Grewe, \{Lynne L.\}",
booktitle = "Signal Processing, Sensor/Information Fusion, and Target Recognition XXXIV",
}