{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:18:35Z","timestamp":1750220315793,"version":"3.41.0"},"reference-count":36,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T00:00:00Z","timestamp":1644883200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Things"],"published-print":{"date-parts":[[2022,5,31]]},"abstract":"<jats:p>\n            Having an efficient onboarding process is a pivotal step to utilize and provision the IoT devices for accessing the network infrastructure. However, the current process to onboard IoT devices is time-consuming and labor-intensive, which makes the process vulnerable to human errors and security risks. In order to have a streamlined onboarding process, we need a mechanism to reliably associate each digital identity with each physical device. We design an onboarding mechanism called\n            <jats:monospace>MAIDE<\/jats:monospace>\n            to fill this technical gap.\n            <jats:monospace>MAIDE<\/jats:monospace>\n            is an Augmented Reality (AR)-facilitated app that systematically selects multiple measurement locations, calculates measurement time for each location and guides the user through the measurement process. The app also uses an optimized voting-based algorithm to derive the device-to-ID mapping based on measurement data. This method does not require any modification to existing IoT devices or the infrastructure and can be applied to all major wireless protocols such as BLE, and WiFi. Our extensive experiments show that\n            <jats:monospace>MAIDE<\/jats:monospace>\n            achieves high device-to-ID mapping accuracy. For example, to distinguish two devices on a ceiling in a typical enterprise environment,\n            <jats:monospace>MAIDE<\/jats:monospace>\n            achieves ~95% accuracy by measuring 5 seconds of Received Signal Strength (RSS) data for each measurement location when the devices are 4 feet apart.\n          <\/jats:p>","DOI":"10.1145\/3506667","type":"journal-article","created":{"date-parts":[[2022,2,15]],"date-time":"2022-02-15T11:43:11Z","timestamp":1644925391000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MAIDE: Augmented Reality (AR)-facilitated Mobile System for Onboarding of Internet of Things (IoT) Devices at Ease"],"prefix":"10.1145","volume":"3","author":[{"given":"Huanle","family":"Zhang","sequence":"first","affiliation":[{"name":"University of California, Davis, California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostafa","family":"Uddin","sequence":"additional","affiliation":[{"name":"Peraton Labs, Basking Ridge, New Jersey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Hao","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Holmdel, New Jersey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarit","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Holmdel, New Jersey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasant","family":"Mohapatra","sequence":"additional","affiliation":[{"name":"University of California, Davis, California"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3386901.3389033"},{"key":"e_1_3_1_3_2","article-title":"ARKit Augmented Reality","author":"Developer Apple","unstructured":"Apple Developer. 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