2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 1 , PP: 28-39, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Sushant Kumar 1 * , Saurabh Mukherjee 2 , Richa Gupta 3
Doi: https://doi.org/10.54216/FPA.140103
Due to advancement in technology, various fields have boosted the development of systems that improve people’s life quality, contributing to the welfare of the community by providing relevant and pertinent information for decision-making. On the Internet of Things (IoT), the systems demand measuring and monitoring several environmental variables. The heterogeneity of the captured data and the measuring instruments used to hinder the interoperability among the different components of the IoT. The problems are raised an interest in the development of methods and tools that support the heterogeneity of the data from the sensors, the measurements, and the measuring devices. Some existing tools have resolved some of these interoperability problems. However, it forces to IoT developers to use sensors from specific brands, limiting their generalized use in the community. Furthermore, it is required to solve the challenge of integrating different protocols in a same IoT project. Besides, by generating alerts, it may help making decisions daily, considering the data provided by the sensors. it is required to solve the challenge of integrating different protocols in a same IoT project. To overcome the limitations of the existing glitches, there is need to develop a framework based on network of sensors via software that allows communication-using protocols in a specific environment to monitor the quality of air and to alarm users about this. In this paper, a prototype of proposal is mentioned about the architecture, list of hardware, software and different APIs are utilized to gather data in a systematic way so as users can visualize data in a semantic view. The visualization is shown later by using Matplotlib, Seaborn tools of Machine Learning (ML) and Deep Learning (DL) to plot the temperature along with humidity in a historical span. The result shows that accuracy obtained via Machine Learning Classifier is 87% in the context of Weather Prediction.
Matplotlib , Internet of things (IoT) , Machine Learning (Ml) , Deep Learning (DL)
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