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dc.contributor.authorChatzimilioudis, Georgiosen
dc.contributor.authorCosta, Constantinosen
dc.contributor.authorZeinalipour-Yazdi, Constantinos D.en
dc.contributor.authorLee, W. -Cen
dc.creatorChatzimilioudis, Georgiosen
dc.creatorCosta, Constantinosen
dc.creatorZeinalipour-Yazdi, Constantinos D.en
dc.creatorLee, W. -Cen
dc.description.abstractIn overloaded or partially broken (i.e., non-operational) cellular networks, it is imperative to enable communication within the crowd to allow the management of emergency and crisis situations. To this end, a variety of emerging short-range communication technologies available on smartphones, such as, Wi-Fi Direct, 3G/LTE direct or Bluetooth/BLE, are able to enable users nowadays to shape point-to-point communication among them. These technologies, however, do not support the formation of overlay networks that can be used to gather and transmit emergency response state (e.g., transfer the location of trapped people to nearby people or the emergency response guard). In this paper, we develop techniques that generate the k-Nearest-Neighbor (kNN) overlay graph of an arbitrary crowd that interconnects over some short-range communication technology. Enabling a kNN overlay graph allows the crowd to connect to its geographically closest peers, those that can physically interact with the user and respond to an emergency crowdsourcing task, such as seeing/sensing similar things as the user (e.g., collect videos and photos). It further allows for intelligent synthesis and mining of heterogeneous data based on the computed kNN graph of the crowd to extract valuable real-time information. We particularly present two efficient algorithms, namely Akin+ and Prox+, which are optimized to work on a resource-limited mobile device. We use Rayzit, a real-world crowd messaging framework we develop, as an example that operates on a kNN graph to motivate and evaluate our work. We use mobility traces collected from three sources for evaluation. The results show that Akin+ and Prox+ significantly outperform existing algorithms in efficiency, even under a skewed distribution of users. © 2015 Elsevier Ltden
dc.sourceInformation Systemsen
dc.subjectWireless networksen
dc.subjectMobile telecommunication systemsen
dc.subjectPoint-to-point communicationen
dc.subjectData miningen
dc.subjectMobile devicesen
dc.subjectQuery processingen
dc.subjectOverlay networksen
dc.subjectQuery languagesen
dc.subjectAll k nearest neighbor queriesen
dc.subjectK nearest neighbor (KNN)en
dc.subjectK nearest neighbor queriesen
dc.subjectMotion compensationen
dc.subjectNearest neighbor searchen
dc.subjectReal-time informationen
dc.subjectShort-range communicationen
dc.subjectSkewed distributionen
dc.titleCrowdsourcing emergency data in non-operational cellular networksen
dc.description.endingpage302 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.source.abbreviationInf Systen
dc.contributor.orcidZeinalipour-Yazdi, Constantinos D. [0000-0002-8388-1549]

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