Adaptive and Fault-tolerant Data Processing in Healthcare IoT Based on Fog Computing

Kun Wang, Yun Shao, Lei Xie, Jie Wu, Song Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

Abstract

In recent years, healthcare IoT have been helpful in mitigating pressures of hospital and medical resources caused by aging population to a large extent. As a safety-critical system, the rapid response from the health care system is extremely important. To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds. However, these fog devices generate huge amount of sensor data. Designing a specific framework for fog devices to ensure reliable data transmission and rapid data processing becomes a topic of utmost significance. In this paper, a Reduced Variable Neighborhood Search (RVNS)-based sEnsor Data Processing Framework (REDPF) is proposed to enhance reliability of data transmission and processing speed. Functionalities of REDPF include fault-tolerant data transmission, self-adaptive filtering and data-load-reduction processing. Specifically, a reliable transmission mechanism, managed by a self-adaptive filter, will recollect lost or inaccurate data automatically. Then, a new scheme is designed to evaluate the health status of the elderly people. Through extensive simulations, we show that our proposed scheme improves network reliability, and provides a faster processing speed.

Original languageEnglish
Article number8418810
Pages (from-to)263-273
Number of pages11
JournalIEEE Transactions on Network Science and Engineering
Volume7
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

Keywords

  • Healthcare IoT
  • data load reduction
  • fog computing
  • reduced variable neighborhood search (RVNS)
  • transmission reliability

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications

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