Towards an Optimized Semantic Interoperability Framework for IoT-Based Smart Home Applications

Balakrishna, Sivadi; Thirumaran, M.

doi:10.1007/978-3-030-04203-5_9

Cited by 8 publications

(2 citation statements)

References 19 publications

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“…The heavy traffic in urban cities is leading to congestion, accidents that have caused the loss of property, wasted time, environmental pollution, and sometimes even the death of a person. Therefore, there is a big need for traffic monitoring and reduction systems in urban areas for them to become Smart cities [2]. The best approach to solve this problem is using the Internet of Things and it provides a new trend to intelligent traffic management.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

2020

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Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective: To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data. Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.

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Section: Introductionmentioning

confidence: 99%

Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

2020

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“…The heavy traffic in urban cities is leading to congestion, accidents that have affected by the property loss, waste of time, environmental pollution, and sometimes it goes to the next level of man's death. Therefore, there is a big need for traffic monitoring and reduction system in urban areas to become Smart cities [2]. The best approach to solve this problem using the Internet of Things and it provides a new trend to intelligent traffic management.…”

Section: Introductionmentioning

confidence: 99%

A Framework for IoT Sensor Data Acquisition and Analysis

Balakrishna¹,

Thirumaran²,

Solanki³

2018

EAI Endorsed Trans IoT

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In the current scenario, around 35 billion Internet of Things (IoT) devices is connected to the internet. By 2025, it is predicted that the number will grow between 80 and 120 billion devices connected to the internet, supporting to generate 180 trillion gigabytes of new sensor data that year. The IoT sensor data is generated from various heterogeneous devices, communication protocols, and data formats that are enormous in nature. This huge amount of sensor data is unable to acquire and analyze manually. This is a significant problem for IoT application developers to make the integration of IoT sensor data automatically. However, the large amount of data has led to the inadequacy of the manual data acquisition and stressed the urgency into the research of IoT based frameworks in automatic. In this paper, we have proposed a framework for IoT sensor data acquisition and analysis (FSDAA). The FSDAA has been implemented on the ThingSpeak IoT Cloud platform for data analysis and visualizations, and compared with the state of the art schemes. Finally, the results show that the proposed FSDAA is efficient in terms of Accuracy, Precision, Recall, and F-measure.

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Semantics and Clustering Techniques for IoT Sensor Data Analysis: A Comprehensive Survey

Balakrishna

Thirumaran

2019

Intelligent Systems Reference Library

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Towards an Optimized Semantic Interoperability Framework for IoT-Based Smart Home Applications

Cited by 8 publications

References 19 publications

Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

A Framework for IoT Sensor Data Acquisition and Analysis

Semantics and Clustering Techniques for IoT Sensor Data Analysis: A Comprehensive Survey

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