People-centric cognitive internet of things for the quantitative analysis of environmental exposure

Lin Yang, Wenfeng Li, Masoud Ghandehari, Giancarlo Fortino

Research output: Contribution to journalArticle

Abstract

Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5 exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.

Original languageEnglish (US)
Pages (from-to)2353-2366
Number of pages14
JournalIEEE Internet of Things Journal
Volume5
Issue number4
DOIs
StatePublished - Aug 1 2018

Fingerprint

Air pollution
Servers
Chemical analysis
Health
Pollution
Systems analysis
Air
Internet of things

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Cite this

People-centric cognitive internet of things for the quantitative analysis of environmental exposure. / Yang, Lin; Li, Wenfeng; Ghandehari, Masoud; Fortino, Giancarlo.

In: IEEE Internet of Things Journal, Vol. 5, No. 4, 01.08.2018, p. 2353-2366.

Research output: Contribution to journalArticle

Yang, Lin ; Li, Wenfeng ; Ghandehari, Masoud ; Fortino, Giancarlo. / People-centric cognitive internet of things for the quantitative analysis of environmental exposure. In: IEEE Internet of Things Journal. 2018 ; Vol. 5, No. 4. pp. 2353-2366.
@article{8d18326d5b924ba0b16d2d595da3ba05,
title = "People-centric cognitive internet of things for the quantitative analysis of environmental exposure",
abstract = "Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5 exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.",
author = "Lin Yang and Wenfeng Li and Masoud Ghandehari and Giancarlo Fortino",
year = "2018",
month = "8",
day = "1",
doi = "10.1109/JIOT.2017.2751307",
language = "English (US)",
volume = "5",
pages = "2353--2366",
journal = "IEEE Internet of Things Journal",
issn = "2327-4662",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - People-centric cognitive internet of things for the quantitative analysis of environmental exposure

AU - Yang, Lin

AU - Li, Wenfeng

AU - Ghandehari, Masoud

AU - Fortino, Giancarlo

PY - 2018/8/1

Y1 - 2018/8/1

N2 - Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5 exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.

AB - Exposure to air pollution poses a significant risk to human health, particularly to urban dwellers. When correlated with individual health outcomes, high resolution information on human mobility, and the spatial and temporal distribution of the pollutants can lead to a better understanding of the effects of pollution exposure. People-centric sensing is normally carried out by data sharing through a central cloud server. This system architecture is not designed to serve the ever-growing number of high fidelity connected devices, particularly when crowdsourcing urban data on location and environmental conditions. Here, we outline an architecture for a people-centric and cognitive Internet of Things (PIoT) environmental sensing platform, which involves closed loops of interactions among people nodes and physical devices as well as servers and recommendations on device connections by cognitive computing. Taking advantage of smart objects and virtual node technology in PIoT, an algorithm to aggregate on-demand user data from smart devices is proposed. A PIoT prototype sensing system is designed and deployed to measure the space-time distribution of particulate matter in air (PM2.5), and mobility counts, for quantifying personal exposure to air pollution. A case study of particulate matter PM2.5 exposure in New York City is presented to illustrate the potential application of people-centric measurement system and data analysis.

UR - http://www.scopus.com/inward/record.url?scp=85053701897&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053701897&partnerID=8YFLogxK

U2 - 10.1109/JIOT.2017.2751307

DO - 10.1109/JIOT.2017.2751307

M3 - Article

VL - 5

SP - 2353

EP - 2366

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 4

ER -