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Leveraging Fog Computing and Deep Learning for Building a Secure Individual Health-Based Decision Support System to Evade Air Pollution
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Author(s): Chitra P. (Thiagarajar College of Engineering, India)and Abirami S. (Thiagarajar College of Engineering, India)
Copyright: 2020
Pages: 27
Source title:
Security, Privacy, and Forensics Issues in Big Data
Source Author(s)/Editor(s): Ramesh C. Joshi (Graphic Era University, Dehradun, India)and Brij B. Gupta (National Institute of Technology, Kurukshetra, India)
DOI: 10.4018/978-1-5225-9742-1.ch017
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Abstract
Globalization has led to critical influence of air pollution on individual health status. Insights to the menace of air pollution on individual's health can be achieved through a decision support system, built based on air pollution status and individual's health status. The wearable internet of things (wIoT) devices along with the air pollution monitoring sensors can gather a wide range of data to understand the effect of air pollution on individual's health. The high-level feature extraction capability of deep learning can extract productive patterns from these data to predict the future air quality index (AQI) values along with their amount of risks in every individual. The chapter aims to develop a secure decision support system that analyzes the events adversity by calculating the temporal health index (THI) of the individual and the effective air quality index (AQI) of the location. The proposed architecture utilizes fog paradigm to offload security functions by adopting deep learning algorithms to detect the malicious network traffic patterns from the benign ones.
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