Human Arthritis Analysis in Fog Computing Environment Using Bayesian Network Classifier and Thread Protocol
- Authors
- Tanwar, Sudeep; Vora, Jayneel; Kaneriya, Shriya; Tyagi, Sudhanshu; Kumar, Neeraj; Sharma, Vishal; You, Ilsun
- Issue Date
- 1-Jan-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Arthritis; Protocols; Monitoring; Logic gates; Smart devices; Sensors
- Citation
- IEEE Consumer Electronics Magazine, v.9, no.1, pp 88 - 94
- Pages
- 7
- Journal Title
- IEEE Consumer Electronics Magazine
- Volume
- 9
- Number
- 1
- Start Page
- 88
- End Page
- 94
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19597
- DOI
- 10.1109/MCE.2019.2941456
- ISSN
- 2162-2248
- Abstract
- Nowadays, many people are facing the problem of arthritis. Regular monitoring and consultation of joint health from a specialist can help patients with this chronicle disease. The ratio of orthopedic doctors to patients with arthritis is low, worldwide. Use of smart devices can support the healthcare industry a lot. Motivated by these facts, here we propose an architecture to track the hand movements of the patient. For regular monitoring of patients with arthritis, fog and cloud gateways for real-time response generation are used. Thread protocol and Bayesian network classifier have been included in the proposed architecture to achieve reliable communication and anomaly detection, respectively. A dataset of 431 patients with arthritis is taken in real time and simulated on OMNet++ simulator. Observations show that the packet delivery ratio is improved by 15-20%, the response time is reduced by 20-30%, and the packet delivery rate is improved by 25-35%, in comparison to not using the fog and thread protocol.
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Collections - College of Engineering > Department of Information Security Engineering > 1. Journal Articles
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