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Internet Things"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>Drone delivery is envisioned to be the delivery mode of the future due to its capability to provide autonomous, end-to-end delivery. Such rapid growth of the drone market necessitates careful checks on drone flight delivery, as a failure in any of a drone\u2019s parts can result in an overestimation of the drone\u2019s battery life, an unexpected increase in delivery time, or even a drone crash. Prior works utilize onboard sensors to detect potential drone failures during flight, which is a reactive approach where the problem may have already occurred.<\/jats:p>\n          <jats:p>\n            In this work, we propose \u00a0\n            <jats:italic>PADrone<\/jats:italic>\n            , a\n            <jats:italic>pre-flight<\/jats:italic>\n            and an\n            <jats:italic>automated<\/jats:italic>\n            drone abnormality detection system that leverages contactless radio frequency\u2013 (RF) based vibration sensing.\u00a0\n            <jats:italic>PADrone<\/jats:italic>\n            utilizes an end-to-end deep learning pipeline to differentiate various abnormalities in motors, propellers, and other drone\u2019s parts, by leveraging their unique\n            <jats:italic>vibration fingerprints<\/jats:italic>\n            .\n            <jats:italic>PADrone<\/jats:italic>\n            uses a frequency-modulated continuous wave radar-based RF system to capture these unique drone vibrations using an RF bandwidth of 150 MHz in the industrial, scientific, and medical band (5.8 GHz). Our real-world evaluations show that\n            <jats:italic>PADrone<\/jats:italic>\n            can classify various drone abnormalities with an average accuracy of 97.5%.\n          <\/jats:p>","DOI":"10.1145\/3706121","type":"journal-article","created":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T09:30:54Z","timestamp":1732872654000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["PADrone: Pre-flight Abnormalities Detection on Drone via Deep RF Sensing"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1822-7148","authenticated-orcid":false,"given":"Ghozali Suhariyanto","family":"Hadi","sequence":"first","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8048-6044","authenticated-orcid":false,"given":"Soundarya","family":"Ramesh","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6563-275X","authenticated-orcid":false,"given":"Mun Choon","family":"Chan","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2025,1,15]]},"reference":[{"key":"e_1_3_4_2_2","first-page":"1","article-title":"IEEE standard letter designations for radar-frequency bands","year":"2003","unstructured":"2003. 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