Efficient Smart Home Research Articles

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.

Design of smart home environment based on wireless sensor system and artificial speech recognition

In the context of the rapid development of intelligent technology, smart home has become the focus of attention. However, the traditional smart home system has some problems such as complicated operation and inconvenient information interaction. In order to solve these problems and improve the user experience of smart home environment, this study establishes a fast and efficient smart home environment design scheme by introducing wireless sensor system and artificial speech recognition technology. By arranging multiple wireless sensor nodes, the monitoring and data acquisition of environmental parameters are realized, and then the voice control and command interaction of smart home devices are realized by using artificial voice recognition technology. Through the effective combination of wireless sensor system and artificial voice recognition technology, users can realize the control of smart home environment through voice commands, and improve the convenience and intelligence of the system. The experimental results show that the design scheme can accurately and stably realize the recognition of user voice commands and the control of smart home devices, and users' evaluation of the degree of intelligence and user experience has also been significantly improved.

Development of AR Android Application for Disability and Elderly-Friendly Smart Home Control

In the rapidly evolving era of technology, smart homes have become a significant trend. This research aims to develop a smart home control model through an Android augmented reality (AR) application using a marker-based method. The marker-based approach utilizes physical markers recognized by the application to project virtual objects into the real world. The developed application enables users, including individuals with disabilities and the elderly, to easily control various features in a smart home using their Android devices. Physical markers used as references in the AR application are identified. Once a marker is detected, virtual objects appear above it, allowing users to control lights, fans, and various smart home devices. Testing has shown that the AR application with the marker-based method can accurately recognize markers and provide quick responses to user commands. Users have also reported a positive interactive experience. This research represents an innovative contribution to the development of disability-friendly and elderly friendly smart home technology. It is expected that this technology will advance the creation of more inclusive, convenient, and efficient smart homes in the future.

Energy efficient smart home heating system using renewable energy source with fuzzy control design

This research article presents an energy- efficient smart home heating system that uses a renew-able energy source and incorporates fuzzy control design. The objective of the study is to design a heating system that optimizes energy consumption while providing a comfortable indoor temperature. The main methods used in the study include the integration of a renewable energy source, such as solar energy or geothermal energy, with a fuzzy control system that adjusts the heating power based on indoor and outdoor temperature, humidity, and occupancy. The main results of the study show that the proposed heating system can reduce energy consumption by up to 40% compared to conventional heating systems, while maintaining a comfortable indoor temperature. The fuzzy control system provides precise control of the heating power and ensures efficient use of the renewable energy source. The main conclusion of the article is that the proposed smart home heating system is a viable solution for reducing energy consumption and promoting a sustainable lifestyle, especially in areas with abundant renewable energy sources.

IoT-Based Smart Air Conditioner as a Preventive in the Post-COVID-19 Era: A Review

The Internet of Things (IoT) refers to physical objects with sensors, computing power, software, and other technologies that communicate and exchange data with other devices, platforms, and systems over the Internet or other communication networks. Remarkable developments in IoT have paved the way for new possibilities, enabling the creation and automation of innovative services and advanced applications and constituting a collection of crucial enabling technologies for smart homes. In this New-Normal Era, the concept of an IoT-based Smart Air Conditioner (AC) as a Preventive Effort against COVID-19 is a proposed innovation and application. The Smart AC is designed based on the analysis of existing problems and is equipped with literature obtained in the study. The purpose of this study is to review the research literature on IoT-enabled Smart AC to emphasize the main trends and open problems of integrating IoT technology to create sustainable and efficient Smart homes. The IoT-based Smart AC was designed and equipped with air quality filter features, human sensors, temperature control, voice control, Cloud Storage, and Solar Panel services that can be controlled via smartphone devices. From the framework and study results, the IoT offers many benefits. The IoT-based Smart AC concept is one step ahead of existing AC products.

스마트 홈의 지속가능성 확보를 위한 적응가능한 설계 요소 제안

Smart home is an intelligent living environment suited to residents because various intelligent devices are connected in a network. For building a sustainable and efficient smart home, adaptive design from an integrating viewpoint, which considers the life cycle of smart home components is required. With this background, this study was conducted to embody the direction and elements of adaptive design in smart home. In order to achieve the research purpose, a phased research method was utilized which consists of consideration of theory review-based design direction and deduction of relevant design standard review-based design elements. Design elements extracted through the study were reviewed through the evaluation of eight experts consisting of workers in related industries. As a result, the adaptive design for smart home was divided into design adaptability and use adaptability and the design directions composing of variability, reliability, connectivity, standard, integrity, contextuality, modifiability, polysensitivity of information transmission, service scalability, technology scalability, ease of use, diversity of operation/management work, real time, automaticity, and controllability and subsequently 41 sub design elements were drawn. This study proposed the sustainability values of smart home design as it focused on the ability of smart homes to address the user’s burden of physical and spatial changes and to adapt to the diverse needs of users. Further studies will need to do an in-depth investigation, for example user research for expanding and materializing design elements.

Optimal Planning of Electrical Appliance of Residential Units in a Smart Home Network Using Cloud Services

One of the important aspects of realizing smart cities is developing smart homes/buildings and, from the energy perspective, designing and implementing an efficient smart home area energy management system (HAEMS) is vital. To be effective, the HAEMS should include various electrical appliances as well as local distributed/renewable energy resources and energy storage systems, with the whole system as a microgrid. However, the collecting and processing of the data associated with these appliances/resources are challenging in terms of the required sensors/communication infrastructure and computational burden. Thanks to the internet-of-things and cloud computing technologies, the physical requirements for handling the data have been provided; however, they demand suitable optimization/management schemes. In this article, a HAEMS is developed using cloud services to increase the accuracy and speed of the data processing. A management protocol is proposed that provides an optimal schedule for a day-ahead operation of the electrical equipment of smart residential homes under welfare indicators. The proposed system comprises three layers: (1) sensors associated with the home appliances and generation/storage units, (2) local fog nodes, and (3) a cloud where the information is processed bilaterally with HAEMS and the hourly optimal operation of appliances/generation/storage units is planned. The neural network and genetic algorithm (GA) are used as part of the HAEMS program. The neural network is used to predict the amount of workload corresponding to users’ requests. Improving the load factor and the economic efficiency are considered as the objective function that is optimized using GA. Numerical studies are performed in the MATLAB platform and the results are compared with a conventional method.

Recognizing Activities of Daily Living using 1D Convolutional Neural Networks for Efficient Smart Homes

Human activity recognition is considered a challenging task in sensor-based monitoring systems. In ambient intelligent environments, such as smart homes, collecting data from ambient sensors is useful for recognizing activities of daily living, which can then be used to provide assistance to inhabitants. Activities of daily living are composed of complex multivariable time series data that has high dimensionality, is huge in size, and is updated constantly. Thus, developing methods for analyzing time series data to extract meaningful features and specific characteristics would help solve the problem of human activity recognition. Based on the noticeable success of deep learning in the field of time series classification, we developed a model called a deep one-dimensional convolutional neural network (Deep 1d-CNN) for recognizing activities of daily living in smart homes. Our model contains several one-dimensional convolution layers coupled with max-pooling technique to learn the internal representation of time series data and automatically generate very deep features for recognizing different activity types. For the performance evaluation, we tested our deep model on the new real-life dataset, ContextAct@A4H, and the results showed that our model achieved a high F1 score (0.90). We also extended our study to show the potential energy saving in smart homes through recognizing activities of daily living. We built a recommendation system based on the activities recognized by our deep model to detect the devices that are wasting energy, and recommend the user to execute energy optimization actions. The experiment indicated that recognizing activities of daily living can result in energy savings of around 50%.

Fuzzy Efficient Energy Smart Home Management System for Renewable Energy Resources

This article provides a fuzzy expert system for efficient energy smart home management systems (FES-EESHM), demand management, renewable energy management, energy storage, and microgrids. The suggested fuzzy expert framework is utilized to simplify designing smart microgrids with storage systems, renewable sources, and controllable loads on resources. Further, the fuzzy expert framework enhances energy and storage to utilize renewable energy and maximize the microgrid’s financial gain. Moreover, the fuzzy expert system utilizes insolation, electricity price, wind speed, and load energy controllably and unregulated as input variables to enable energy management. It uses input variables including insolation, electrical quality, wind, and the power of uncontrollable and controllable loads to allow energy management. Furthermore, these input data can be calculated, imported, or predicted directly via grid measurement using any prediction process. In this paper, the input variables are fuzzified, a series of rules are specified by the expert system, and the output is de-fuzzified. The findings of the expert program are discussed to explain how to handle microgrid power consumption and production. However, the decisions on energy generated, controllable loads, and own consumption are based on three outputs. The first production is for processing, selling, or consuming the energy produced. The second output is used for controlling the load. The third result shows how to produce for prosumer’s use. The expert method can be checked via the hourly input of variable values. Finally, to confirm the findings, the method suggested is compared to other available approaches.

SH-BlockCC: A secure and efficient Internet of things smart home architecture based on cloud computing and blockchain technology

The growing demand for human-independent comfortable lifestyle has emboldened the development of smart home. A typical keenly intellective home includes many Internet of things contrivances that engender processes and immensely colossal data to efficiently handle its users’ demands. This incrementing demand raises a plethora of concern cognate to a smart home system in terms of scalability, efficiency, and security. All these issues are tedious to manage, and the existing studies lack the granularity for surmounting them. Considering such a requisite of security and efficiency as a quandary at hand, this article presents a secure and efficient smart home architecture, which incorporates the blockchain and the cloud computing technologies for a cumulated solution. Because of the decentralized nature of blockchain technology, it can serve the processing services and make the transaction copy of the collected sensible user data from smart home. To ensure the security of smart home network, our proposed model utilizes the multivariate correlation analysis technique to analyze the network traffic and identify the correlation between traffic features. We have evaluated the performance of our proposed architecture using different parameters like throughput and discovered that blockchain is an efficient security solution for the future Internet of things network.

A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources. This approach improves the performance of an SHEMS by incorporating stochastic energy consumption and PV generation models over a horizon of several days, using only the computational power of existing smart meters. In this paper, we consider a PV-storage (thermal and battery) system, however, our method can extend to multiple controllable devices without the exponential growth in computation that other methods such as dynamic programming (DP) and stochastic mixed-integer linear programming (MILP) suffer from. Specifically, probability distributions associated with the PV output and demand are kernel estimated from empirical data collected during the Smart Grid Smart City project in NSW, Australia. Our results show that ADP computes a solution much faster than both DP and stochastic MILP, and provides only a slight reduction in quality compared to the optimal DP solution. In addition, incorporating a thermal energy storage unit using the proposed ADP-based SHEMS reduces the daily electricity cost by up to 26.3% without a noticeable increase in the computational burden. Moreover, ADP with a two-day decision horizon reduces the average yearly electricity cost by a 4.6% over a daily DP method, yet requires less than half of the computational effort.

Technology analysis for internet of things using big data learning

We implemented a n efficient smart home automation system through the Internet of Things (IoT) including different type of sensors, this whole module will helps to the human beings to understand and provide the information about their home security system we are also going to apply Big Data Analysis to analyze the data that we are getting from different type of sensors in this module. We are using some sensors in our module to sense some type of things or object that makes our home standard and also introducing the face recognition system with an efficient algorithm into the module to make it more impressive and provide standardization in advance era.

Smart home simulation model for synthetic sensor datasets generation

World population is ageing due to longer life expectancy worldwide. There is a trend in elderly people to live alone in their habitual residences in spite of health and safety risks. Smart Homes, intelligent environment systems deployed at elderly homes can act as early warning systems trying to forecast the worsening or exacerbation of the resident chronic conditions. Access to sensor datasets is essential for the development of an efficient real smart home. Procurement of such datasets is subject to several restrictions and difficulties. This paper describes the generation of synthetic datasets by means of a simulation model as a suitable alternative previous to the deployment of a real monitoring system. The collection of synthetic datasets will be used during the next project step to train and evaluate activity recognition methods and algorithms.

Toward software defined smart home

In recent years, the smart home field has caught wide attention and witnessed rapid development. Smart devices, continuously increasing in number, make user management and implementation more difficult while promoting the development of the smart home. How to design an efficient smart home management platform is one of the great challenges the current smart home field faces. This article refers to the core idea of SDN, and proposed the software defined smart home platform, SDSH for short. The design features of virtualization, openness, and centralization can effectively integrate the heterogeneous network devices in the smart home platform, and flexibly adapt to the great difference between family scenes and user demands. At the same time, this article brings up the core technology of SDSH, and discusses the application value of the four core technologies and the new challenges the current technology is facing in a smart home scenario. In the end, regarding the SDSH application scenarios, this article analyzes the household experience innovation brought by this kind of smart home management platform, and the opportunities and challenges the SDSH platform faces.

Malaysia's Existing Green Homes Compliance with LEED for Homes

In this paper, the Leadership in Energy and Environmental Design for Homes (LEED-H) rating method was tested on three existing Malaysian green homes (GH). This was to ascertain the amount of changes and modifications needed to configure LEED-H to suit the local context. The three GHs are Demonstration, Cool and Energy Efficient House (DCEEH), Smart and Cool Home (SCH) and CoolTek House (CTH). These are benchmark Malaysian GHs either due to their design, construction methods, building materials or operational procedures. It was found that all case studies did not comply with at least 12 mandatory prerequisites out of 23 as outlined in LEED-H.

RETRACTED: Challenges of information and communication technology in energy efficient smart homes

Energy saving is an important issue in the times of increasing energy prices. Products and services provided by the information and communication technology (ICT) can support energy efficiency and emissions reductions. The synergy of ICT and a residential area gives rise to the smart home environment. Ecological and smart home network applications should not only improve the peoples life satisfaction in the domain of health care, security protection, and efficient management of energy and resource consumption, but they should also enable personal happy life through easy communication with community people, high level entertainment, continuous education and so on. A smart home in the smart grid is the latest addition to a family of ideas emerging in relation to the ICT usage in the home. This paper presents an overview of the smart home concept and some challenges that ICT faces in that environment.

Improvements in home automation strategies for designing apparatus for efficient smart home

Proposals are made to design and build intelligent home meeting the modern requirements satisfying most living standards of consumers. Home security activities supported by real time audio visual system which permits only the entry of authorised visitors into the flat are incorporated in the system. Easy handling of selected home appliances in the flat by switching ON or OFF from any room is provided in the system. Moreover, remote control of these selected appliances are performed through telephone dialling or with Internet are accommodated in the system. A Home Assistant system software installed in the PC brings the daily activities to the monitor screen as obtained from the activities programmed in tables of data bases. The electrical energy requirements of the flats are partially supported by a unit extracting solar energy and wind energy. Saving of electrical energy by this unit is maximised by automatically changing the angles of the solar panel and wind mill fixed on the terrace. Furthermore, intruder detection unit included in the system dials automatically a pre-programmed sequence of digits to give message to a remote location while triggering a local alarm.