The Internet of Things and fast data streams: prospects for geospatial data science in emerging information ecosystems

Marc P. Armstrong, Shaowen Wang, Zhe Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

This paper surveys the rapid development of the Internet of Things, the massive data streams that are only now beginning to be generated from it, and the resulting opportunities and challenges that these data streams bring to geographic information analysis. These challenges arise because streaming data volumes cannot bt subjected to analysis using the standard repertoire of methods that have been designed to analyze static geospatial datasets. New approaches are needed, not to supplant, but to supplement, these existing tools. A focus is placed on the concept of data velocity (fast data) and its effects on sampling and inference. Innovative data ingestion strategies based on principles related to reservoir sampling and sketching are described. Dynamic temporal data flows present significant challenges to load balancing in distributed (e.g. cloud) parallel environments, even at exascale levels of performance. Further advances in the exploitation of data locality based on geographical concepts, as well as advanced processing methods based on edge and approximate computing, require further elucidation. Concepts are illustrated using a database compiled from a distributed sensor network of mobile radioactivity detectors.

Original languageEnglish (US)
Pages (from-to)39-56
Number of pages18
JournalCartography and Geographic Information Science
Volume46
Issue number1
DOIs
StatePublished - Jan 2 2019

Keywords

  • Internet of Things
  • data streams
  • fast data
  • parallel computing
  • sensors

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Management of Technology and Innovation

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