{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T13:39:05Z","timestamp":1777297145587,"version":"3.51.4"},"reference-count":92,"publisher":"Association for Computing Machinery (ACM)","issue":"2","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2026,6,30]]},"abstract":"<jats:p>Floor identification has gained much attention due to the increasing demand for indoor location-based services, especially prompt emergency response services. Leveraging cellular signals for floor identification has recently been of interest due to the pervasiveness of cellular technology. However, all current systems require information from multiple cell towers concurrently, which is inaccessible in most phones and thus severely limits their deployability.<\/jats:p>\n                  <jats:p>\n                    We propose\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    , a ubiquitous and deployable floor identification system.\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    is the first system to meet the regulatory agencies\u2019 accuracy requirements using received signal strength information from only the serving cell tower.\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    relies on a sequence of signal measurements to overcome the limited information available when using only the serving tower. Our system handles multiple challenges affecting accuracy and deployability, including noisy cellular data, overfitting, data collection overhead, and suitability for mobile device deployment. Moreover,\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    employs recent advances in deep generative models to improve the system\u2019s robustness to unseen noisy data and reduce data collection overhead. Our extensive experiments verify\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    \u2019s effectiveness in multiple real testbeds, where it correctly estimates the user\u2019s exact floor up to 98.7% of the time using only the serving tower, an improvement up to 310% compared to the state-of-the-art cellular-based system. Furthermore,\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    can remain regulatory-compliant with up to 70% reduction in available training data.\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    achieves these improvements with over an order of magnitude reduction in the model size compared to the state-of-the-art. This shows\n                    <jats:italic toggle=\"yes\">UniCellular<\/jats:italic>\n                    \u2019s superior performance while providing a ubiquitous, efficient, and practical solution that meets regulatory requirements.\n                  <\/jats:p>","DOI":"10.1145\/3708986","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T05:07:18Z","timestamp":1734671238000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Ubiquitous and Low-Overhead Floor Identification with Limited Cellular Information"],"prefix":"10.1145","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1405-3966","authenticated-orcid":false,"given":"Sherif","family":"Mostafa","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2063-4364","authenticated-orcid":false,"given":"Moustafa","family":"Youssef","sequence":"additional","affiliation":[{"name":"The American University in Cairo","place":["New Cairo, Egypt"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1327-9077","authenticated-orcid":false,"given":"Khaled A.","family":"Harras","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University - Qatar Campus","place":["Doha, Qatar"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"issue":"7","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"9250","DOI":"10.1109\/JSEN.2020.3041424","article-title":"Ubiquitous transportation mode detection using single cell tower information","volume":"21","author":"AbdelAziz Ali M.","year":"2021","unstructured":"Ali M. 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