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
N1 - Funding Information:
Manuscript received April 27, 2017; revised August 1, 2017; accepted September 2, 2017. Date of publication September 11, 2017; date of current version August 9, 2018. This work was supported in part by the NSFC under Grant 61571336 and in part by the Research and Development Project of Henan Province under Grant 151100211400. (Corresponding author: Wenfeng Li.) L. Yang and W. Li are with the School of Logistics Engineering, Wuhan University of Technology, Wuhan 430068, China (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2017 IEEE.
PY - 2018/8
Y1 - 2018/8
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.
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U2 - 10.1109/JIOT.2017.2751307
DO - 10.1109/JIOT.2017.2751307
M3 - Article
AN - SCOPUS:85053701897
SN - 2327-4662
VL - 5
SP - 2353
EP - 2366
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 4
ER -