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Review
Computer Science, Information Systems
Tehseen Mazhar et al.
Summary: With the help of machine learning, difficult tasks can be automated. In a smart grid, computers and mobile devices make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) connects the components of smart buildings, improving the quality of life for everyone.
Review
Energy & Fuels
Amjad Almusaed et al.
Summary: The normal development of smart buildings, which involves integrating sensors, rich data, and artificial intelligence (AI) simulation models, holds the promise of bringing about a new era of architectural concepts. AI simulation models can enhance home functions, improve user comfort and greatly reduce energy consumption through better control, increased reliability, and automation. This article highlights the potential of using AI models to enhance the design and functionality of smart homes, particularly in the implementation of living spaces.
Article
Energy & Fuels
Jueru Huang et al.
Summary: This paper proposes an hour-ahead demand response algorithm for energy management at home. It utilizes an artificial neural network approach with stable cost predictions to address upcoming price uncertainties. By using multi-agent reinforcement learning, it enables optimum and decentralized decision-making for various household devices. Simulation experiments show that this algorithm is effective in managing energy for multiple devices, reducing consumer electricity expenses and discomfort prices, and significantly lowering energy expenses compared to no demand response.
Article
Energy & Fuels
Zahra Solatidehkordi et al.
Summary: The increase in household energy consumption globally has highlighted the need for effective management and monitoring of electricity usage. This study proposes a smart home appliance classification system that utilizes deep learning and a comprehensive database for training, achieving competitive results across various appliances. The model is deployed on a Raspberry Pi micro-controller and interfaces with smart meters to provide almost real-time appliance classification to end users or utility providers through a mobile application.
Review
Telecommunications
Vasilios A. Orfanos et al.
Summary: With the rapid development of Internet communication technologies, it has expanded beyond computer networks. MEMS now allows for smaller and higher-performing sensors and actuators. WSN and IoT enable devices to communicate, share data, and be remotely controlled. The cost of components needed for M2M scenarios has become more affordable. This paper provides a comparison of different connectivity technologies to assist in choosing the right technology for home automation.
JOURNAL OF SENSOR AND ACTUATOR NETWORKS
(2023)
Review
Computer Science, Information Systems
Barjinder Kaur et al.
Summary: The evolution of mobile technologies has introduced smarter and more connected objects into our day-to-day lives, known as the Internet of Things (IoT). However, the IoT also brings cybersecurity threats due to different communication standards, weak security defaults, and the difficulty of updating. To address these threats, developing a robust intrusion detection framework specifically for IoT is a promising approach.
INTERNET OF THINGS
(2023)
Proceedings Paper
Computer Science, Interdisciplinary Applications
Sanguk Park
Summary: The goal of this paper is to enable cost-effective IoT system design by removing and integrating redundant IoT sensors through correlation analysis between sensing data collected in a smart home environment. This paper presents data analysis and prediction technology that induces meaningful inference through data correlation analysis between different heterogeneous IoT sensors installed inside a smart home. Through this, we propose an intelligent service model that can be implemented based on machine learning/deep learning in a smart home environment.
2023 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, ICCE
(2023)
Article
Chemistry, Analytical
Zakaria El Mrabet et al.
Summary: This paper proposes a data-driven approach for determining fault characteristics using fault trajectory samples, and uses a random forest regressor-based model to detect real-time fault location and duration simultaneously. The experiments show that the model consistently outperforms other models in terms of accuracy, prediction error, and processing time.
Article
Chemistry, Analytical
Purna Prakash Kasaraneni et al.
Summary: This paper proposes an ML-based ensemble classifiers approach to address anomalies in smart home energy consumption data. By identifying and removing anomalies, and imputing missing information, more accurate data analysis is achieved. The study finds that the ensemble classifier RF+SVM+DT performs superior in anomaly handling.
Article
Computer Science, Information Systems
Houda Jmila et al.
Summary: Smart home IoT devices lack proper security measures, raising concerns about safety and privacy. One-size-fits-all network administration is ineffective due to the diverse QoS requirements of IoT devices. Device classification can enhance IoT management and security by identifying vulnerable and rogue items and automating network administration based on device type or function. This study analyzes machine learning-based traffic analysis approaches to uncover hidden patterns in IoT traffic and enable automatic device classification. It provides insights into the potential and limitations of these methods, presents a generic workflow for IoT device classification, and explores potential research directions.
Article
Energy & Fuels
Isaac Machorro-Cano et al.
Article
Computer Science, Information Systems
Jaewoong Kang et al.
Article
Computer Science, Artificial Intelligence
Dan Popa et al.
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Chemistry, Analytical
Sangyoon Lee et al.
Article
Engineering, Electrical & Electronic
Prabha Sundaravadivel et al.
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
(2018)
Article
Environmental Sciences
M Xu et al.
REMOTE SENSING OF ENVIRONMENT
(2005)
Article
Computer Science, Artificial Intelligence
L Breiman
