Smart Home Control and Management Based on Big Data Analysis

Chi, Hao; Chi, Yuyan

doi:10.1155/2022/3784756

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…The library's operating and programming framework affects search speed (Chi and Chi (2022)). Speed is not emphasized in all projects, however, speed is a significant factor.…”

Section: Challenges Open Issues and Research Directionsmentioning

confidence: 99%

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Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

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SummaryThe rapid growth of the Internet of Things (IoT) has led to its widespread adoption in various industries, enabling enhanced productivity and efficient services. Integrating IoT systems with existing enterprise application systems has become common practice. However, this integration necessitates reevaluating and reworking current Enterprise Architecture (EA) models and Expert Systems (ES) to accommodate IoT and cloud technologies. Enterprises must adopt a multifaceted view and automate various aspects, including operations, data management, and technology infrastructure. Machine Learning (ML) is a powerful IoT and smart automation tool within EA. Despite its potential, a need for dedicated work focuses on ML applications for IoT services and systems. With IoT being a significant field, analyzing IoT‐generated data and IoT‐based networks is crucial. Many studies have explored how ML can solve specific IoT‐related challenges. These mutually reinforcing technologies allow IoT applications to leverage sensor data for ML model improvement, leading to enhanced IoT operations and practices. Furthermore, ML techniques empower IoT systems with knowledge and enable suspicious activity detection in smart systems and objects. This survey paper conducts a comprehensive study on the role of ML in IoT applications, particularly in the domains of automation and security. It provides an in‐depth analysis of the state‐of‐the‐art ML approaches within the context of IoT, highlighting their contributions, challenges, and potential applications.

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“…The library's operating and programming framework affects search speed (Chi and Chi (2022)). Speed is not emphasized in all projects, however, speed is a significant factor.…”

Section: Challenges Open Issues and Research Directionsmentioning

confidence: 99%

“…In a given case, one should consider the size and complexity of the data to be analyzed when implementing a specific toolkit. The library's operating and programming framework affects search speed (Chi and Chi (2022)). Speed is not emphasized in all projects, however, speed is a significant factor.…”

Section: Machine Learning and Internet Of Thingsmentioning

confidence: 99%

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Rehman,

Tariq,

Moqurrab

et al. 2023

Expert Systems

View full text Add to dashboard Cite

show abstract

“…is conflict checking mode can provide a good reference for system developers and users of smart homes. Chi et al [22] used big data technology to design a smart home management control scheme. At the same time, he uses the hybrid particle swarm optimization method to optimize and control the smart home system.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Network Home Management System Based on Big Data

Shan

2022

Mathematical Problems in Engineering

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Network home has become a research hotspot in today’s society, and it can improve the comfort, safety, and convenience of people’s lives. The traditional network home model only makes certain actions to the home system according to people’s instructions, and it is difficult to realize the intelligence of network home. This also limits the security and convenience of an online home. This study makes full use of the advantages of big data technology in processing nonlinear data, and applies the convolutional neural network (CNN) method and long and short-term memory (LSTM) neural network method to the network home system. CNN can be used to extract people’s behavior information, and LSTM can be used to extract people’s speech features. CNN method can establish the relationship between people’s behavior information, speech information, and network home management system. At the same time, this research mainly analyzes the lighting system, home appliance system, security system, and floor heating system in the network home system. The results show that the CNN-LSTM method has high accuracy in predicting the four systems of network home. The largest prediction error is only 2.78%, and this part of the error comes from the prediction of the home appliance system. The smallest prediction error is only 0.98%.

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Digital Twins to Monitor IoT Devices for Green Transformation of University Campus

Muka,

Marinova

2024

2024 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)

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Smart Home Control and Management Based on Big Data Analysis

Cited by 6 publications

References 29 publications

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Machine learning and internet of things applications in enterprise architectures: Solutions, challenges, and open issues

Optimization of Network Home Management System Based on Big Data

Digital Twins to Monitor IoT Devices for Green Transformation of University Campus

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