A Systematic Review on Machine Learning Fundamentals for Smart Home: Towards Smart City Solution (original) (raw)

IJERT-Role of Machine learning and Internet of Things devices : An explorative study with respect to Smart City

International Journal of Engineering Research and Technology (IJERT), 2020

https://www.ijert.org/role-of-machine-learning-and-internet-of-things-devices-an-explorative-study-with-respect-to-smart-city https://www.ijert.org/research/role-of-machine-learning-and-internet-of-things-devices-an-explorative-study-with-respect-to-smart-city-IJERTCONV8IS05052.pdf This paper is an explorative study to analyze the role of various Machine Learning (ML). Algorithms which can be used for Internet of Things (IoT) devices. Since the growth in use of Internet of Things devices, the ease and quality of living of Human species has touched a different level. Everyday creating a huge amount of data. While. Machine learning on the other hand is ensuring that this Big Data is analyzed optimally and innovatively. Through this paper we are also trying to explore the possibilities of combining these two approaches ML and IoT; to understand its application in the field of Smart cities.

Machine learning applications to smart city

ACCENTS Transactions on Image Processing and Computer Vision, 2019

The basic need of human is increasing as they interact with different devices and also, they provide many feedbacks. Many smart devices generate high data and that can be retrieved and reviewed by humans. Applications are not fixed as it increases day to day life. Based on these data generated by different smart devices and smart city applications machine learning approach is the best adaptive solution. Rapid development in software, hardware with high speed internet connection provides large data to this physical world. The key contribution of this paper is a machine learning application survey towards smart city.

Smart Cities Using Machine Learning and Intelligent Applications (ITALIC)

International Transactions on Artificial Intelligence (ITALIC)

The goal of smart cities is to properly manage to expand urbanization, Reduce energy usage, Enhance the economic and quality of life of the locals while also preserving the environment conditions, and improve people's ability to use and adapt modern technology used in information and communication efficiently (ICT). Information and communication technology (ICT) is central to the concept of smart cities because it facilitates the development of policies, decision-making, implementation, and, eventually, the provision of useful services. This review's main objective is to examine how machine learning, deep reinforcement learning (DRL), and artificial intelligence are advancing smart cities. The previous strategies are effectively utilized to provide the best policies possible for a number of challenging issues relating to smart cities. The uses of the earlier methods are thoroughly discussed in this survey. in intelligent transportation systems (ITSs), Cybersecurity, as well ...

The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency

Applied Sciences

Machine learning can be used to automate a wide range of tasks. Smart buildings, which use the Internet of Things (IoT) to connect building operations, enable activities, such as monitoring temperature, safety, and maintenance, for easier controlling via mobile devices and computers. Smart buildings are becoming core aspects in larger system integrations as the IoT is becoming increasingly widespread. The IoT plays an important role in smart buildings and provides facilities that improve human security by using effective technology-based life-saving strategies. This review highlights the role of IoT devices in smart buildings. The IoT devices platform and its components are highlighted in this review. Furthermore, this review provides security challenges regarding IoT and smart buildings. The main factors pertaining to smart buildings are described and the different methods of machine learning in combination with IoT technologies are also described to improve the effectiveness of sm...

An overview of machine learning applications for smart buildings

Sustainable Cities and Society

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change and its consequences. On the other hand, the rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. A lot of research has been dedicated to specific machine learning applications for specific phases of a building's life-cycle. The reviews commonly take a specific, technological perspective without a vision for the integration of smart technologies at the level of the whole system. Especially, there is a lack of discussion on the roles of autonomous AI agents and training environments for boosting the learning process in complex and abruptly changing operational environments. This review article discusses the learning ability of buildings with a system-level perspective and presents an overview of autonomous machine learning applications that make independent decisions for building energy management. We conclude that the buildings' adaptability to unpredicted changes can be enhanced at the system level through AI-initiated learning processes and by using digital twins as training environments. The greatest potential for energy efficiency improvement is achieved by integrating adaptability solutions at the timescales of HVAC control and electricity market participation. 'intelligent building' (IB) and 'smart building' (SB) (Al Dakheel et al. (2020); Wang et al. (2020))). A shift towards the implementation of artificial intelligence (AI) trained by machine learning algorithms is recognized as one of the major trends of development (Karpook, 2017). Given the complexities related to the operational environment, the machine learning techniques 'reinforcement learning (RL)' and its derivative 'deep reinforcement learning (DRL)' have been experienced useful for the autonomous control networks of buildings (Han et al., 2019). Quite a few review articles have been published with various perspectives on smart buildings. A quick look at the most relevant review articles in the field reveals that most of them focus on issues such as hardware technologies, monitoring, forecasting, modelling, building energy management, and applications of machine learning (Alawadi et al.

Machine Learning, Big Data, And Smart Buildings: A Comprehensive Survey

2019

Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

Securing the Dynamic Realm: A Comprehensive Review of ML Algorithms in IoT-Based Home Automation Systems and Beyond

International Journal of Emerging Trends in Engineering Research , 2024

This paper comprehensively reviews the imperative for secure IoT systems, emphasizing the challenges posed by their dynamic nature. Exploring various ML algorithms for IoT security, it highlights their advantages while addressing common limitations, including computational overhead and privacy risks. The focus narrows to federated learning (FL) and deep learning (DL) algorithms, showcasing their potential to overcome conventional ML drawbacks by preserving data privacy. The study provides an in-depth analysis of FL and DL-based techniques, emphasizing their efficiency in enhancing security in IoT-based home automation systems. The paper further examines ML's pivotal role in smart homes, presenting a case study that utilizes the support vector machine algorithm to distinguish between regular occupants and intruders. Extending the discussion to face recognition for home automation, the review underscores the utilization of IoT and smart techniques. Beyond home automation, the paper delves into the broader landscape of ML applications in the Fourth Industrial Revolution, offering insights into cybersecurity, smart cities, healthcare, and more. The review briefly introduces the utilization of Convolutional Neural Networks (CNNs) within the broader context of deep learning algorithms. While the main emphasis remains on FL and DL, the paper acknowledges CNNs as a powerful tool for image-based tasks, especially relevant in the context of visual data analysis for security in IoT-based home automation systems. In summary, this concise review encapsulates the transformative impact of ML on IoT-based home automation security, providing valuable perspectives on current trends, challenges, and future research directions. The inclusion of CNNs within the abstract recognizes their relevance, especially in image-based security applications.

Machine Learning for Smart Building Applications: Review and Taxonomy

2018

The use of machine learning (ML) in smart building applications is reviewed in this paper. We split existing solutions into two main classes, occupant-centric vs. energy/devices centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories, (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed and compared, as well as open perspectives and research trends. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building ma...

When Smart Cities Get Smarter via Machine Learning: An In-Depth Literature Review

IEEE ACCESS, 2022

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of the smart cities. Application domains include the essential aspects of the smart cities including the energy, healthcare, transportation, security, and pollution. The research methodology presents a state-of-the-art taxonomy, evaluation and model performance where the ML algorithms are classified into one of the following four categories: decision trees, support vector machines, artificial neural networks, and advanced machine learning methods, i.e., hybrid methods, ensembles, and Deep Learning. The study found that the hybrid models and ensembles have better performance since they exhibit both a high accuracy and low overall cost. On the other hand, the deep learning (DL) techniques had a higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference was negligible, it can be concluded that using either of them is appropriate.

Investigating the Impact of AI/ML for Monitoring and Optimizing Energy Usage in Smart Homes

Universal Wiser Publisher, 2025

Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.