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TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
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Volume-3 | Issue-3
Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles
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Cyber-attack and Measuring its Risk
Volume-3 | Issue-4
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Analysis of Solar Power Generation Performance Improvement Techniques
Volume-4 | Issue-3
Pollination Inspired Clustering Model for Wireless Sensor Network Optimization
Volume-3 | Issue-3
Design of Low Power Cam Memory Cell for the Next Generation Network Processors
Volume-3 | Issue-4
A STUDY OF RESEARCH NOTIONS IN WIRELESS BODY SENSOR NETWORK (WBSN)
Volume-1 | Issue-2
Computation of Constant Gain and NF Circles for 60 GHz Ultra-low noise Amplifiers
Volume-3 | Issue-3
Volume - 3 | Issue - 1 | march 2021
Published
16 March, 2021
Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.
KeywordsFog computing disease prediction ensemble methods body sensor network multi-sensor data fusion
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