Smart Home Management Research Articles

Artificial Intelligence Applied to Civil Engineering: A Multidisciplinary Approach

Objective: Present the main applications of Artificial Intelligence (AI) in Civil Engineering, covering all phases of building a Smart Home, from conception to disposal. Theoretical Framework: Based on the multidisciplinary nature of artificial intelligence in Civil Engineering and the various methodologies and techniques to apply it, based on data collection and processing of the information obtained, a Smart Home is proposed that is planned from its conception to its disposal or demolition, covering the entire life cycle of the project. To manage the subsystems, a more extensive System called “Digital Home Management (DHM)” was developed, in which the data collected by the sensors is processed and transformed into information that will be stored and transmitted to the resident when necessary. Method: The methodology adopted for this study is qualitative and exploratory and is based on documentary bibliographic research in national and international databases. Results and Discussions: The results show the advantages of considering different fields of human knowledge when adopting Artificial Intelligence techniques, leading to better results in the products and services delivered by the Smart Home and Digital Home Management. Research Implications: AI applied to Civil Engineering is just beginning. This practice will increase considerably in the coming years. The integration between human beings (peopleware), computer programs (software), and machines (hardware) means that AI presents a wide range of options for services and products to be provided to improve the quality of life of society. Originality/Value: The relevance and value of this research are evidenced by proposing the implementation of a Digital Home Management System to mitigate the damage caused to the environment by human activities, whether routine or not, contributing to a healthier society and a more sustainable environment. This research on AI application to Civil Engineering is just beginning. This practice will increase considerably in the coming years. The integration between human beings (peopleware), computer programs (software), and machines (hardware) makes AI present a wide range of options for services and products to be provided to improve the quality of life of society. shows how much the field of Artificial Intelligence applied to Civil Engineering is quite promising and will increasingly require professionals who can interrelate with people from other areas.

Retraction Note: Deployment of smart home management system at the edge: mechanisms and protocols

Retraction Note: Deployment of smart home management system at the edge: mechanisms and protocols

Design and implementation of embedded hardware in furniture control based on DSP

The continuous progress of embedded technology and Internet of Things technology has accelerated the arrival of the era of home intelligence. By designing a smart home control system based on embedded DSP technology, it can detect temperature, humidity, flammable gas leakage, smoke, fire, and other indicators in the home environment, and carry out intelligent control of the detected security risks. The core of the system is the smart home management terminal, which is responsible for the network interconnection inside and outside the home. Terminal hardware includes a high-speed DSP chip, various interface modules, and so on. The software adopts the C/OS-II operating system to realize multi-task processing. This paper briefly summarizes DSP technology and embedded technology, and proposes the composition of intelligent home control system based on embedded DSP technology, as well as the hardware and software design and implementation of the system. Through the smart home system, users can control home conditions and obtain public service information anytime and anywhere, which is highly practical.

Comparative analysis of smart home management systems

The article presents a comparative analysis of smart home management systems. The paper includes an analysis of the market, from which two systems were selected for detailed analysis. The choice was between the open source locally running Home Assistant system and the cloud-based Google Home solution. The detailed analysis includes a comparison of documentation, functionality, configurability, security, architecture and user feedback. In addition, two studies were conducted. The first addressed the response time of end devices to perform an action in the smart home management system. The second, on the other hand, addressed the time and number of steps required to configure devices with the systems.

Thermal Energy consumption monitoring System in the Hot water Supply circuit as part of the Smart Home management system

Thermal Energy consumption monitoring System in the Hot water Supply circuit as part of the Smart Home management system

The Importance of AI-Enabled Internet of everything Services for Smart Home Management

Abstract Smart home applications are ubiquitous and have become popular because of the overwhelming use of the Internet of Things (IoT) and artificial intelligence (AI). Living smart with automation and integrated AI-IoT has become more affordable as home automation technologies have matured. In addition, the Internet of Everything (IoE), which involves the interconnection of humans, businesses, and intelligent objects, has the potential to reshape various industries. However, the rising energy cost and demand have led numerous organizations to determine smart ways to monitor, control, and save energy. Hence, this study suggests AI-Enabled Internet of Everything Services (AI-IoES) for efficient smart home energy management. The data have been taken from the Open Smart Home IoT//Energy Dataset for analyzing the energy consumption of home appliances. This paper presents an IoT sensor for energy management to track and control specific loads in smart homes. The deep neural network (DNN) is built for secure demand-side management (DSM) in an IoT-assisted smart grid and trained on the extracted feature from electricity consumption information gathered using an IoT sensor. The system is established with real-time monitoring and a user interface for remote control and access. The experimental outcome demonstrates that the suggested AI-IoES system increases the user experience by 98.9%, energy efficiency ratio (EER) by 97.8%, and accuracy ratio by 97.2%, and reduces energy consumption by 19.2% compared with other existing methods.

Innovating Household Food Waste Management: A User-Centric Approach with AHP-TRIZ Integration.

Food waste management remains a paramount issue in the field of social innovation. While government-led public recycling measures are important, the untapped role of residents in food waste management at the household level also demands attention. This study aims to propose the design of a smart system that leverages sensors, mobile terminals, and cloud data services to facilitate food waste reduction. Unlike conventional solutions that rely on mechanical and biological technologies, the proposed system adopts a user-centric approach. By integrating the analytical hierarchy process and the theory of inventive problem solving, this study delves into users' actual needs and explores intelligent solutions that are alternatives to traditional approaches to address conflicts in the problem solving phase. The study identifies five main criteria for user demands and highlights user-preferred subcriteria. It determines two physical conflicts and two technical conflicts and explores corresponding information and communications technology (ICT)-related solutions. The tangible outcomes encompass a semi-automated recycling product, a mobile application, and a data centre, which are all designed to help residents navigate the challenges regarding food waste resource utilisation. This study provides an approach that considers users' genuine demands, empowering them to actively engage in and become practitioners of household food waste reduction. The findings serve as valuable references for similar smart home management systems, providing insights to guide future developments.

An Enhanced Secure User Authentication and Authorized Scheme for Smart Home Management

An Enhanced Secure User Authentication and Authorized Scheme for Smart Home Management

An IoT-Based Thermal Modelling of Dwelling Rooms to Enable Flexible Energy Management

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This darkgrey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills.

Rancang Bangun Prototype Smart Home System Kontrol Lokal Area Menggunakan IoT MQTT Panel

With the progress of civilization, science, and technology in Indonesia, the study entitled "Design and Development of Prototype Smart Home System using IoT MQTT Panel" aims to facilitate efficient control and monitoring of home activities. This smart home optimizes lighting, security, air, and garden systems, while saving electricity costs. The study adopts an experimental method to establish cause-effect relationships between factors by controlling other potential influences. Functional testing shows that the software system functions well and efficiently controls various aspects of the smart home. Failure value testing evaluates sensor performance, with the garden control showing the highest responsiveness at 93%. This indicates that the system provides a comprehensive solution for smart home management. Conclusion: Successful development of a smart home system using IoT MQTT Panel, effectively controlling lighting, air, security, and garden. Failure value testing shows an average sensor responsiveness of 90% and delay of 10.6%. Recommendations: Improve internet connectivity, add a camera, and conduct testing with different methods.

Energy management of smart homes over fog-based IoT architecture

Existing research studies on home automation systems mostly conserve energy by modeling the occupancy of users within home. Some others apply statistical approaches on the survey data about usage of appliances. Consequently, these research works either reduce wastage of electricity through automation or achieve energy efficiency based on appliances’ usage estimations. However, they do not provide energy consumption modeling which is human comfort centric and also validated through practical implementation in real-world smart homes. We present a Markov-chain-based probabilistic model to obtain users stochastic activity patterns which are used to forecast the energy consumption in a smart home environment. These predictions are then leveraged by our novel comfort aware energy saving mechanism named as prediction- and feedback-based proactive energy conservation (PF-PEC) algorithm. The PF-PEC algorithm reduces the total energy consumption while ensuring standard human comfort. Furthermore, a fog-based Internet of Things (IoT) architecture is implemented and deployed in a smart home to efficiently incorporate the proposed algorithm in real-world scenarios. Experimental results show up to 36% energy conservation, marking substantial reduction in daily electricity usage.

Investigation of IoT-Integrated Smart Homes

The growth of internet-connected services, known as the Internet of Things (IoT), has led to a proliferation of new applications. One such application is the smart home, where household appliances and devices can be remotely monitored and controlled. To achieve this, appropriate network architecture and standard protocols are used to connect various devices to the internet, resulting in an "IoT-based smart home." However, managing and regulating the entire system, as well as ensuring the security of servers and smart homes, present challenges. This paper presents an IoT architecture and discusses the issues and difficulties faced by IoT-enabled smart home systems while also proposing potential solutions. Smart homes simplify home automation tasks and offer greater convenience to users. The Industrial Wireless Sensor Network (WSN) has already demonstrated the potential of IoT, and the integration of IoT into smart homes is a logical next step. The article explores various aspects of IoT-based smart homes and highlights the need for proper management and security protocols. In conclusion, the study investigates the integration of IoT into smart homes, highlighting the challenges and solutions associated with the development of an IoT-based smart home system. The objective is to provide a framework for the development and management of IoT-based smart homes that will enhance the quality of life for users.

Wi-NN: Human Gesture Recognition System Based on Weighted KNN

Gesture recognition, the basis of human–computer interaction (HCI), is a significant component for the development of smart home, VR, and senior care management. Most gesture recognition methods still depend on sensors worn by the user or video-based gestures for recognition, can be used for fine-grained gesture recognition. our paper implements a gesture recognition method that is independent of environment and gesture drawing direction, and it achieves gesture recognition classification by using small sample data. Wi-NN, proposed in this study, does not require the user to wear additional device. In this case, channel state information (CSI) extracted from Wi-Fi signal is used to capture the action information of the human body via CSI. After pre-processing to reduce the interference of environmental noise as much as possible, clear action information is extracted using the feature extraction method based on time domain to obtain the gesture action feature data. The gathered data are integrated with the weighted k-nearest neighbor (KNN) classification recognizer for classification task. The experiment outcomes revealed that the accuracy scores of the same gesture for different users and different gestures for the same user under the same environment were 93.1% and 89.6%, respectively. The experiments in different environments also achieved good recognition results, and by comparing with other experimental methods, the experiments in this paper have better recognition results. Evidently, good classification results were generated after the original data were processed and incorporated into the weighted KNN.

IoT Technology for Intelligent Management of Energy, Equipment and Security in Smart House

The Internet of Things means that many of the daily devices used by humans will share their functions and information with each other or with humans by connecting to the Internet. The most important factor of the Internet of Things is the integration of several technologies and communication solutions. Identification and tracking technologies, wired and wireless sensors and active networks, protocols for increasing communication and intelligence of objects are the most important parts of the Internet of Things. In this article, an attempt has been made to determine the parts that can be used to make a house smart among the concepts and technologies related to web-based programs based on Internet of Things technology. Since it is very time-consuming to investigate the effect of all the Internet of Things technologies in smart homes, by studying and examining various types of research, the web-based program based on the Internet of Things is selected as an independent variable, and its effect on smart home management is investigated. For this purpose, a web-based program based on the Internet of Things for intelligent building energy management, intelligent equipment management, and intelligent security has been designed and implemented. As experimental results shown the proposed method the proposed method achieves better results compared to other existing methods in energy consumption by 33.8% reducing energy usage.

IoT Sensor-based sustainable smart home management for human needs through Micro Controller

Smart home technology is emerging rapidly as an exciting new paradigm. Appliances inside the home can be watched over, managed, and controlled by the consumer. Additionally, the gadgets are linked together via the internet, enabling the user to remotely control features like home lighting, air conditioner, and security systems providing sustainable approach. To aid the elderly and others with disabilities, this paper’s major goal is to teach consumers how to properly handle their household appliances. Thus, a smart home is being developed where the environmental conditions like air conditioner, television and lights adjust themselves with the person entering the room or even according to the weather conditions. Due to the complexity, in this paper an Microcontroller, LCD and other necessary materials in place of TV, AC and light are used to show that if a house consists of two or more people how the LCD will adjust itself and displays the standard required channel, likewise same LCD adjusts itself to the standard temperature required by the person and similarly adjusts itself according to the person entering into the room. Additionally, if it is raining outside the LCD will display a monsoon mood song, this is done using rain sensor. If these sensors are connected to the cloud, then the information related to the person entering the room is stored. This information can be accessed using your mobile.

Automatic optimal multi-energy management of smart homes

Residential and commercial buildings are responsible for approximately 35% of carbon emissions in industrialized countries. Making buildings more efficient and sustainable is, therefore, a fundamental step toward a low-carbon energy society. A key to achieving sustainability is by leveraging on energy storage systems and smart technologies to switch between energy carriers in order to optimize environmental impact. However, the research on energy management in buildings has mostly focused on its economic aspect, overlooking the environmental dimension. Additionally, the concept of energy system flexibility has been mostly proposed as the ability to shift demand over time or, at most, to curtail it, aiming at reducing the system’s operating costs. We propose a multi-energy multi-objective scheduling model to optimally manage the supply, demand, and interchange of multiple energy carriers, based on dynamic price and carbon emission signals. Our holistic and integrated approach is applied to a group of 200 smart homes with varying thermal and electric loads, and equipped with different types of smart technologies. The effectiveness of the approach in reducing the home carbon footprint, while remunerating the users, is evaluated using historical and statistical data of three European countries.

An Internet of Things based approach for Smart Home Management

The advent of the Internet of Things (IoT) paradigm has led to the construction of technological environments based on device communication. In this scenario, solutions have been developed for the intelligent management of various services, such as those related to Smart Cities. In such a scenario, widespread use is that of Smart Homes, environments that can provide management help and intelligent and tailored services to users. This paper aims to present an Internet of Things-based approach to Smart Home management. The proposed approach, which leverages graph approaches, enables the extraction and processing of contextual data, collected from sensors, and probabilistic approaches able to provide valuable information and autonomous actions. The system training phase is performed using graph approaches, which could represent an increase in Context and Situation Awareness. In the experimental phase, a prototype is used to test the proposed method’s energysaving and ventilation management capabilities, which shows encouraging findings.

Modular Furniture Design by Using Intelligent Platform and Wireless Sensors.

The continuous development of the society has led to the improvement of people's quality of life and consumption level. At the same time, peoples demand for all aspects of production and life is also increasing, thus promoting the emergence and innovation of intelligent household appliances. To manage these devices conveniently and quickly and enrich family life, "smart home" bureau plays a very important role. Smart home, which enters people's family life, uses communication technology, Internet connection technology, automatic fire control technology, network wiring technology, and visual and audio transmission technology to communicate with home. Mobile terminals have been developed, and more and more PC functions have been realized. Based on the hardware platform of the smart home management system, two solutions were put forward. The first solution is combined with the current 5G network, and through it, the user can control the mobile phone and other mobile terminals on the corresponding application operation instructions to create. The second solution is the design of the web server intelligent management system, for relevant information. It is collected into the database of the server, allows remote access to the node and subscriber information related to storage in the database through the Internet, and searches the information to control the home lighting and temperature. This system is designed to imitate the modular scheme, which includes the central control module, sensor data acquisition module, and software module. Finally, on the browser side and the electronic devices of Android operating system, it realizes the wireless control of lighting, air conditioning, washing machine, and other devices, as well as the detection of the home environment.

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

The concept of energy management in smart homes has received increasing attention in recent years, particularly on issues such as creating a balance between user privacy and reducing energy costs. Accordingly, this article proposes a user-oriented multi-objective approach, which minimizes energy costs and maximizes consumer privacy. In addition, a home energy management system is suggested for smart homes to optimize the energy consumption pattern of appliances. On the other hand, considering challenges in energy management of smart homes, the concept of demand-side management (DSM) is introduced. The objective of the proposed method is to reduce energy consumption to lower consumers’ electricity bills. Also, it improves user comfort (UC) in average waiting time conditions. In this research, a smart home equipped with an energy management system and smart home appliances that can inject electric power into the upstream network is considered the main system. This framework leads to a multi-objective optimization problem in which the two objectives mentioned above are considered two separate dimensions. To solve the problem, an ITS-BF Algorithm is used, which employs a random search to schedule home appliances and batteries based on the application of flexible devices in smart homes. The case studies show that the proposed method can considerably respect and satisfy users’ privacy and reduce the energy cost to an acceptable level. Finally, the numerical results obtained from the simulation have been analyzed to evaluate the proposed method’s efficiency. The simulation results show that an ITS-BF algorithm performs better than the existing methods in reducing costs and waiting time.

Iot Smart Home Management System Based on Embedded Devices

This project is the use of Internet of Things technology how to better serve people's home life, which is a wide range of application prospects, social and economic benefits of the research direction. The system is mainly designed based on the planning of multiple detection points of the home environment, so as to provide personal intelligent housekeeping services from temperature, humidity, purification, cleaning, audio and visual for home life. Provide a set of solutions for smart homes, continuously optimize the comfort and services of the home environment, so as to improve people's home life.