Big data analytics for emergency communication networks: A survey

Junbo Wang, Yilang Wu, Neil Yen, Song Guo, Zixue Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

50 Citations (Scopus)

Abstract

Disaster management is a crucial and urgent research issue. Emergency communication networks (ECNs) provide fundamental functions for disaster management, because communication service is generally unavailable due to large-scale damage and restrictions in communication services. Considering the features of a disaster (e.g., limited resources and dynamic changing of environment), it is always a key problem to use limited resources effectively to provide the best communication services. Big data analytics in the disaster area provides possible solutions to understand the situations happening in disaster areas, so that limited resources can be optimally deployed based on the analysis results. In this paper, we survey existing ECNs and big data analytics from both the content and the spatial points of view. From the content point of view, we survey existing data mining and analysis techniques, and further survey and analyze applications and the possibilities to enhance ECNs. From the spatial point of view, we survey and discuss the most popular methods and further discuss the possibility to enhance ECNs. Finally, we highlight the remaining challenging problems after a systematic survey and studies of the possibilities.
Original languageEnglish
Article number7429689
Pages (from-to)1758-1778
Number of pages21
JournalIEEE Communications Surveys and Tutorials
Volume18
Issue number3
DOIs
Publication statusPublished - 1 Jul 2016
Externally publishedYes

Keywords

  • Big data analytics
  • Content analytics
  • Emergency communication networks
  • Machine learning
  • Spatial analytics

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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