Crowdsourcing emergency data in non-operational cellular networks

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• 作者：Georgios Chatzimilioudis^a ; ^{gchatzim@cs.ucy.ac.cy} ; Constantinos Costa^a ; ^{costa.c@cs.ucy.ac.cy} ; Demetrios Zeinalipour-Yazti^a ; ^{dzeina@cs.ucy.ac.cy} ; Wang-Chien Lee^b ; ^{wlee@cse.psu.edu}
• 关键词：Smartphones ; Emergency ; Crowdsourcing ; Query processing ; All k nearest neighbor queries
• 刊名：Information Systems
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：64
• 期：Complete
• 页码：292-302
• 全文大小：1385 K
• 卷排序：64

文摘

In 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.