Recent developments in Internet of Things for data collection and monitoring have increased its potential applications in monitoring environmental phenomenon for data dependent climate action. Internet of things devices are convenient for real time monitoring and generate huge amount of data. The collected data can be uncertain and erroneous, making traditional data analysis approaches redundant. Moreover, specific approaches can be best suited to a dataset depending upon its nature. The present work aims to tackle these challenges in the experimental dataset consisting of vital parameters for water quality assessment of Ganga River. The dataset was collected over a period of one year. The experiment based comparative analysis were performed and the results show that the Dempster-Shafer evidence theory based data fusion approach outperforms other similar methods and improves the classification accuracy
Raut, Ashwin; Shivhare, Anubhav; Purohit, Neetesh; Chaurasiya, Vijay Kumar; and Kumar, Manish, "Improving Classification Accuracy Using Data Fusion Technique in IoT" (2021). PACIS 2021 Proceedings. 142.
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