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Spatial Algorithms Syst."],"published-print":{"date-parts":[[2017,6,30]]},"abstract":"<jats:p>\n            Location-based services have become an important part of our daily lives. However, such services require continuous user tracking while preserving the scarce cell-phone battery resource. In this article, we present\n            <jats:italic>Dejavu<\/jats:italic>\n            , a system that uses standard cell-phone sensors to provide accurate and energy-efficient outdoor localization.\n            <jats:italic>Dejavu<\/jats:italic>\n            is capable of localizing and navigating both pedestrian and in-vehicle users in real time. Our analysis shows that, whether walking or in-vehicle, when the user encounters a road landmark such as going inside a tunnel, ascending a staircase, or even moving over a bump, all these different landmarks affect the inertial sensors on the phone in a unique pattern.\n            <jats:italic>Dejavu<\/jats:italic>\n            employs a dead-reckoning localization approach and leverages these road landmarks, among other automatically discovered virtual landmarks, to reset the dead-reckoning accumulated error and achieve accurate localization. To maintain a low energy profile,\n            <jats:italic>Dejavu<\/jats:italic>\n            uses only energy-efficient sensors or sensors that are already running for other purposes. Moreover,\n            <jats:italic>Dejavu<\/jats:italic>\n            provides a localization confidence measure along with its predicted location. This improves the usability of the predicted location from end users\u2019 perspective.\n          <\/jats:p>\n          <jats:p>\n            We present the design of\n            <jats:italic>Dejavu<\/jats:italic>\n            and how it leverages crowd-sourcing to automatically learn virtual landmarks and their locations. Our evaluation results from implementation on different Android devices using different testbeds showing that\n            <jats:italic>Dejavu<\/jats:italic>\n            can localize cell-phones in vehicles with a median error of 8.4\n            <jats:italic>m<\/jats:italic>\n            in city roads and 16.6\n            <jats:italic>m<\/jats:italic>\n            on highways and can localize cell-phones carried by pedestrians with a median error of 3.0m. Moreover, compared to the global position system (GPS) and other state-of-the-art systems,\n            <jats:italic>Dejavu<\/jats:italic>\n            can extend the battery lifetime by up to 347%, while achieving even better localization results than GPS in the more challenging in-city areas. In addition,\n            <jats:italic>Dejavu<\/jats:italic>\n            estimates the localization confidence measure accurately with a median error of 2.3m and 31cm for in-vehicle and pedestrian users, respectively.\n          <\/jats:p>","DOI":"10.1145\/3085575","type":"journal-article","created":{"date-parts":[[2017,7,31]],"date-time":"2017-07-31T12:12:00Z","timestamp":1501503120000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Accurate and Energy-Efficient GPS-Less Outdoor Localization"],"prefix":"10.1145","volume":"3","author":[{"given":"Heba","family":"Aly","sequence":"first","affiliation":[{"name":"University of Maryland, MD"}]},{"given":"Anas","family":"Basalamah","sequence":"additional","affiliation":[{"name":"Umm Al-Qura University, Makkah, KSA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2063-4364","authenticated-orcid":false,"given":"Moustafa","family":"Youssef","sequence":"additional","affiliation":[{"name":"Egypt-Japan University of Science and Technology (E-JUST), Alexandria, Egypt"}]}],"member":"320","published-online":{"date-parts":[[2017,7,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/VTCFall.2012.6399360"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/SAHCN.2014.6990394"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOM.2015.7146523"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmcj.2015.10.019"},{"key":"e_1_2_1_5_1","volume-title":"Automatic rich map semantics identification through smartphone-based crowd-sensing","author":"Aly Heba","year":"2016","unstructured":"Heba Aly , Anas Basalamah , and Moustafa Youssef . 2016. 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