Maximum Data-Resolution Efficiency for Fog-Computing Supported Spatial Big Data Processing in Disaster Scenarios

Junbo Wang*, Michael Conrad Meyer, Yilang Wu, Yu Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


Spatial big data analysis is very important in disaster scenarios to understand distribution patterns of situations, e.g., people's movements, people's requirements, resource shortage situations, and so on. In a general case, spatial big data is generated from distributed sensing devices and analyzed in a centralized way, e.g., a cloud center with high-performance computing resources. However, data transmission from sensing devices to cloud centers always takes a long time, especially in disaster scenarios with an unstable network. Fog computing is a promising technique to solve the above problem by offloading data processing tasks from the cloud to nearby computation devices. But data resolution also decreases after local processing in the fog nodes. It is necessary to investigate the optimal task distribution solutions to efficiently use computation resources in the fog layer. In this paper, we take the above research problem, and study fog-computing supported spatial big data processing. We analyze the process for spatial clustering, which is a typical category for spatial data analysis, and propose an architecture to integrate data processing into fog computing. We formalize a problem to maximize the data-resolution efficiency by considering data resolution and delay. We further propose core algorithms to enable spatial clustering in a fog-computing environment and implement the above algorithms in a real system. We have performed both simulations and experiments on a real Twitter dataset collected when Kumamoto-city suffered an earthquake. Through the simulations and the experiments, we have determined that the proposed solution significantly outperforms the other solutions.

Original languageEnglish
Article number8630038
Pages (from-to)1826-1842
Number of pages17
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number8
Publication statusPublished - 2019 Aug 1


  • Spatial big data analytics
  • data resolution
  • disaster
  • fog computing
  • spatial clustering

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Computational Theory and Mathematics


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