Smart Home Technology Research Articles

Determinants of internet of things (IoT) smart home technology (SHT) adoption behavior of consumers: Empirical evidence from a least developed country (LDC)

Though millions of users in developed countries utilize IoT smart home technology, its applicability in underdeveloped countries is limited. This study addresses this gap by unveiling the determinants of IoT smart home technology adoption behavior among consumers in the least developed country. The study has extended the unified theory of acceptance and use of technology (UTAUT) by including perceived hedonism, lifestyle, and well-being constructs in the research framework. Data from 355 IoT smart home users were analyzed using structural equation modeling (SEM). The findings reveal that performance expectancy, facilitating conditions, perceived hedonism, and lifestyle positively affect behavioral intention. Moreover, behavioral intention positively affects use behavior. In the post-adoption stage, use behavior positively affects multifaceted user well-being. However, effort expectancy and social influence did not affect behavioral intention. The study has a novel theoretical contribution to the extant literature and significantly contributes to technology designers, marketers, elderly users, and government agencies.

A virtual sensing approach to enhancing personalized strategies for indoor environmental quality and residential energy management

This study presents a consumer-centric approach on Demand-Side Management (DSM) for residential energy systems, aiming to align cost-efficient energy strategies with the individual's preferences on operational time-flexibility for residential electric appliances, thermal comfort, and indoor air quality. The optimization framework innovatively incorporates forecasts of the Indoor Air Quality (IAQ) index and thermal comfort levels using virtual sensing technology to predict their day-ahead values, which are furtherly integrated as constraints in the optimization problem. A dual-objective approach is employed, balancing the minimization of electricity costs all whilst increasing consumer's satisfaction. The approach underlines the critical role of consumer participation in DSM and illustrates how the integration of smart home technologies can lead to reductions in energy consumption and cost savings. This paradigm not only promotes consumer engagement in energy management but also showcases the potential of intelligent home systems to optimize energy usage while maintaining personalized comfort standards.

Smart and Secure Home with Chatbot

This study delves into the fusion of smart home technology with robust security measures alongside the integration of a chatbot interface. This convergence aims to streamline home automation while fortifying security protocols. Smart home devices, sophisticated algorithms, and encryption techniques form the bedrock of this secure environment. Additionally, the incorporation of a chatbot enhances user interaction, offering an intuitive interface for monitoring and controlling smart home functionalities. Emphasizing the pivotal role of proactive security measures, this paper underscores the symbiotic relationship between advanced home technology, stringent security, and a user-friendly chatbot interface in creating a harmonious and secure living space.

Smart homes: pioneering age-friendly environments in China for enhanced health and quality of life.

Traditionally, China has been more reliant on a model of care that ensures older adults are cared for by family members. Whilst promoting the idea of older adults ageing in their own homes is essential, the provision of in-home care must shift from primarily relying on family caregivers to a model that places greater emphasis on gerontechnologies and enhanced healthcare service delivery. In this perspective article we argue for the adoption of a 'smart home' model in aged care in China. The smart home model argues for innovative technologies to older adult care, such as virtual support groups, video-conferencing, and electronic health records; assistive technologies that can safely maintain independence and assist with daily living such as sensors, wearables, telehealth, smart home technologies as well as interactive robotic technologies for mobility and cognitive support such as humanoid robots, rehabilitation robots, service/companion robots. The adoption and implementation of gerontechnologies have been slow, with only a handful of solutions demonstrating proven effectiveness in supporting home care. The utilisation of such digital technologies to support and enable older adults in China to age-in-place can bring a significant contribution to healthy ageing. Nonetheless, it's crucial to focus on co-creating with end-users, incorporating their values and preferences, and enhancing training to boost the adoption of these gerontechnologies. Through a smart home model of care, China can age-in-place more effectively, leading to significant contributions to healthy ageing.

English A cross cultural investigation on Smart Home Technology:

Some individuals may feel threatened by the digital age and suffer from technophobia, whereas technophilic individuals embrace new technologies. Relying on a study of 243 participants aged 18-65, this study explored the roles of technophobia, technophilia, trust in technology, and digital competence, and how they interact within the SHT paradigm. Technophobia positively correlated with age and negatively with ownership and most digital skills. Males outscored females in overall competence, especially in creative digital skills and technophilia. Spanish participants scored higher than English participants on operational digital skills, creative digital skills, and technophilia-dependency. A hierarchical regression model with technophobia as the dependent variable showed that operational, navigational, and mobile digital skills contributed negatively to technophobia. Technophilia-enthusiasm negatively predicted technophobia, whereas technophilia-reputation did so positively. Culture and gender were not significant predictors of technophobia in this context. There is a need to provide digital skills for all sections of society to increase self-efficacy and counterbalance any feelings of anxiety and technophobia that may manifest in individuals who feel left behind in the modern digital world. The onus is for SHT manufacturers to design products that require basic digital skills and are custom-made for different populations cohorts within society.

HOMES OF OUR TIME: THE POWER AND POTENTIAL OF SMART HOME TECHNOLOGY

In an era dominated by technological development, our living spaces are experiencing radical changes. Thus, smart home technology is a concept that has turned from a futuristic idea into a material reality. In the article that we present today, we can give the idea of "how we can make our homes profitable" by talking about the advantages, power and potential of Smart homes. As the beginning of the smart home concept, the concept of home automation, which aims to simplify and improve the tasks done in our daily lives, is making a comeback. On the other hand, in the development of Internet of Things and Artificial Intelligence, it is the Smart Home concept that has helped to further improve. Central to smart home technology is the integration of IoT devices. From smart thermostats to voice assistants, all devices communicate with each other, and as a result of these connections, the smart home concept becomes even more functional. It can also be said that smart home technology is dominated by communication and control. Thus, all intelligent things created are connected to each other through the internet network. As a result, it becomes easier to activate voice commands through Siri, Alexa and other smart assistants built into smartphones.

Integration of Smart Home and Building Automation Systems in Virtual Reality and Robotics-Based Technological Environment for Neurorehabilitation: A Pilot Study Protocol.

Technological innovation has revolutionized healthcare, particularly in neurological rehabilitation, where it has been used to address chronic conditions. Smart home and building automation (SH&BA) technologies offer promising solutions for managing chronic disabilities associated with such conditions. This single group, pre-post longitudinal pilot study, part of the H2020 HosmartAI project, aims to explore the integration of smart home technologies into neurorehabilitation. Eighty subjects will be enrolled from IRCCS San Camillo Hospital (Venice, Italy) and will receive rehabilitation treatment through virtual reality (VR) and robotics devices for 15 h per day, 5 days a week for 3 weeks in the HosmartAI Room (HR), equipped with SH&BA devices measuring the environment. The study seeks to optimize patient outcomes and refine rehabilitation practices. Findings will be disseminated through peer-reviewed publications and scientific meetings, contributing to advancements in neurological rehabilitation and guiding future research.

Development design of an IoT-based smart home monitoring system with security features

<div style="text-align: start;" align="center"><p>A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.</p></div>

IOT-Based Smart Home Control Design Using Blink Application and Esp8266 Wi-Fi Module

The swift progression of technology has significantly impacted contemporary human existence, leading to an escalating demand for tools that simplify daily tasks. Automation is viewed as a means to reduce time, enhance accessibility, and improve efficiency. Particularly, the adoption of Smart Home technology is becoming increasingly crucial in present times. This research endeavors to develop an innovative prototype for a home equipped with IoT capabilities. The prototype incorporates a range of sensors and components to fulfill various functions: a DHT11 sensor for monitoring room temperature, an MQ-2 sensor for detecting gas leaks, an ultrasonic sensor for object detection, an MC38 magnet sensor for door security, a relay to control lamp switches, and a buzzer for alarms, all managed by a microcontroller. Additionally, the system utilizes a NodeMCU with a Wi-Fi module ESP8266, facilitating communication and control through the Blynk App. The Research and Development (R&D) methodology was employed to create this IoT-enabled Smart Home prototype, aiming to enhance user convenience in daily living.

Heating load prediction in buildings using decision tree machine learning method

In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R2 = 0.975 and RMSE = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies.

Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM.

Unfulfilled Promises of Child Safety and Privacy: Portrayals and Use of Children in Smart Home Marketing

Smart home technologies are making their way into families. Parents' and children's shared use of smart home technologies has received growing attention in CSCW and related research communities. Families and children are also frequently featured as target audiences in smart home product marketing. However, there is limited knowledge of how exactly children and family interactions are portrayed in smart home product marketing, and to what extent those portrayals align with the actual consideration of children and families in product features and resources for child safety and privacy. We conducted a content analysis of product websites and online resources of 102 smart home products, as these materials constitute a main marketing channel and information source about products for consumers. We found that despite featuring children in smart home marketing, most analyzed product websites did not mention child safety features and lacked sufficient information on how children's data is collected and used. Specifically, our findings highlight misalignments in three aspects: (1) children are depicted as users of smart home products but there are insufficient child-friendly product features; (2) harmonious child-product co-presence is portrayed but potential child safety issues are neglected; and (3) children are shown as the subject of monitoring and datafication but there is limited information on child data collection and use. We discuss how parent-child relationships and parenting may be negatively impacted by such marketing depictions, and we provide design and policy recommendations for better incorporating child safety and privacy considerations into smart home products.

Intelligent Energy Management: Leveraging an Effective Machine Learning for Predictive Appliance Energy Optimization in Smart Homes

In the context of escalating energy demands and the proliferation of smart home technologies, this study introduces a novel approach to energy management using the Random Forest machine learning model. Our research focuses on optimizing household appliance energy use, harmonizing efficiency with user comfort. By analyzing data on appliance usage patterns, environmental conditions, and user preferences, the Random Forest model predicts future energy needs, enabling the intelligent scheduling of appliances to reduce unnecessary consumption. The model’s strength lies in its capacity to unravel complex, non-linear relationships in high-dimensional data typical of household energy usage scenarios. Initial results demonstrate a notable decrease in energy consumption, affirming the model’s efficacy in enhancing energy efficiency without diminishing user convenience. This research not only highlights the potential of machine learning in energy management but also sets a foundation for future exploration into adaptive, real-time energy optimization strategies in smart homes. In this research, we compared our model with other machine learning models, and our model got a good accuracy of 95.71%, while for time series data, we got 99.71%.

IOT BASED VEHICLE THEFT DETECTION AND ENGINE LOOKING SYSTEM

The advancement of smart home technology has revolutionized traditional security measures, offering more efficient and convenient solutions for safeguarding homes and properties. In this project, we propose a smart door lock system that integrates Passive Infrared (PIR) motion detection, a solenoid-based locking mechanism, and an ESP32 Cam module for intruder capturing and remote monitoring. The core components of the system include a PIR sensor, which detects motion in the vicinity of the door, and a solenoid actuator, which controls the locking and unlocking of the door. The ESP32 Cam module serves as the central processing unit, responsible for coordinating the operation of the components and capturing images or videos of intruders. Upon detecting motion, the PIR sensor triggers the ESP32 Cam module to activate the solenoid, thereby locking the door to prevent unauthorized access. Simultaneously, the ESP32 Cam captures images or videos of the intruder using its built-in camera and stores the evidence locally or transmits it to a designated mobile device. Keywords: Biometric access control, Motion detection, Intruder detection, PIR sensor, ESP32 Cam, Smartphone app control, Solenoid actuator

Access Control Using Facial Recognition

The field of smart home technology has grown by leaps and bounds over the last few decades. This development has made several new products available that can add convenience and security to facility. People became aware of smart home devices with the introduction of the smart thermostat, but these thermostats were only the beginning to smart homes. Smart devices helps people manage their sched-ules, grocery lists, home lighting and even the home security. These devices work together to make life a bit easier as well as safer. One group of such smart home security devices that are gaining popularity are smart door locks. Smart door locks are also seeing new innovative features being added recently where some companies are adding the capability of facial recognition to the locks. With nothing but the face, it’s now possible for a door lock can be operated. As far back as 2016, manufacturers started talk-ing about smart locks with face recognition. The innovation has continued to grow as several main-stream tech devices with facial recognition like the phones and handheld devices with facial recogni-tion unlock features. In smart door locks, the device uses facial recognition, leveraging advanced processing and analytics to identify a person’s facial attributes at the door. KEY WORDS Access control, facial recognition, door locks, security.

GENERAL QUESTIONS OF SELECTION AND APPLICATION OF COMMUNICATION PROTOCOLS OF «SMART HOUSE» SYSTEM

The article considers SMART technology, in particular smart house and basic communication protocols used for data exchange. Smart house technology additionally bears the name «home automation». Home automation in modern conditions allows the user or consumer to flexibly manage and independently configure the system, depending on the requirements of the user. One of the stages of customization of smart home technology is the selection of communication protocol for data exchange within the smart home system.To implement data exchange in smart home technology, it is necessary to properly approach the choice of communication protocols. Smart home technology uses several types of devices: controllers, sensors, acoustics. Since not all devices support existing protocols, there are also unique devices that support several of the existing protocols. There are several protocols used in smart home technology: ZigBee, Z-Wave, Wi-Fi.In this article the analysis of two main wireless protocols operating at high frequencies, namely ZigBee and Z-Wave. Correctly selected protocols implement fast data transmission without loss. In addition, it will be possible to realize the needs of the user or user, which are set by the system.

“Those blimmin Ts and Cs”: a mixed methods analysis of how people manage personal information, privacy, and impressions

ABSTRACT Interconnected and smart technologies complicate personal information management (PIM) because users delegate the storing, organizing, and retrieving of personal information to smart and mobile service providers. Meta-level PIM activities are required to maintain the privacy and security of personal information. This study provides insights into how users of location tracking, mobile apps, and smart home technologies perceive PIM and privacy. We turn to the privacy as contextual integrity (CI) and impression management (IM) literatures to explore informational norms and interpersonal dynamics in PIM. This study is based on a mixed methods design to analyze focus groups and interviews with 106 British and Dutch respondents. Combining unsupervised Latent Dirichlet allocation (LDA) topic modeling and thematic analysis, we reveal discursive patterns in respondent accounts of technology use and provide an in-depth interpretation of these patterns. Our findings indicate that PIM practices are associated with the perceived appropriateness of information flows, anthropomorphic interpretations of technologies, and interpersonal surveillance. Thus, impressions are managed toward social actors as well as technology providers. We contribute to PIM research with a demonstration of how PIM in mobile, smart, and location-based technology use cannot be separated from contextual factors and strategies to manage impressions of habits and behaviors.

Security and Privacy Concerns in the Adoption of IoT Smart Homes: A User-Centric Analysis

The advancement of Internet of Things (IoT) technologies has ushered in a new era of smart homes, promising convenience and automation. However, alongside these advancements, concerns regarding the security and privacy of Internet of Things smart homes have garnered significant attention. The study embarked on a user-centric analysis, delving into the intricacies of security and privacy concerns in the adoption of Internet of Things smart homes. The primary purpose of this research was to investigate the security and privacy concerns that users harbour when adopting Internet of Things smart home technologies. We used SMART-PLS version (4.0.9.6) as the data analysis tool, to examine the concerns and to gain a comprehensive understanding of their impact on adoption. The analysis was, rooted in quantitative research design and based on data gathered through an online questionnaire distributed to the target population of 325 participants. The research response rate was 92%. The hypotheses examined unveiled statistically significant relationships, culminating in results indicating an R<sup>2</sup> of 0.762. This implies that approximately 76.2% of the rationale behind individuals' decisions to either adopt or refrain from using IoT smart home devices, with a focus on security and privacy considerations, can be elucidated by our proposed Structural Equation Model. his model served as a comprehensive lens through which we dissected the intricate interplay of variables shaping user attitudes and behaviors. The study sheds light on the critical concerns of security and privacy within the IoT smart home domain. By leveraging quantitative analysis and a well-crafted Structural Equation Model, we offer valuable insights into the factors influencing user adoption decisions. The research contributes to the broader discourse IoT technology adoption and serves as a foundation for future studies and policy considerations in the ever-evolving landscape of smart homes.

The effects of technology readiness, risks, and benefits on smart home technology adoption: extending the Theory of Planned Behavior model

Grounded in the Theory of Planned Behavior (TPB), this study investigated generational differences in the roles of technology readiness (TR), risks, and benefits on the behavioral intention of smart home technology. Data were gathered from a probability sample of 501 respondents by conducting a telephone survey in 2020. Findings showed that smart home technologies were not widely adopted in Hong Kong at the time of the survey. People who reported higher intention to adopt were all driven by components in the TPB model, TR (optimism and innovativeness), tangible benefits (such as saving energy, money, and time), and trendiness; however, this intention was inhibited by psychological risks. Furthermore, the interaction effect identified in the study provides a more nuanced view about the ways in which attitude toward IoT can interact with tangible benefits to regulate the behavioral intention of smart home products. This study suggests that designers and marketers of smart home products should recognize the role played by both intrinsic (e.g., optimism and innovativeness towards technology) and extrinsic factors (e.g., perceived risks and benefits) in significantly influencing the intention to adopt.

Smart home technology adoption in Denmark: Diffusion, social differences, and energy consumption

Smart home technology (SHT) is increasingly entering homes to provide services such as energy management, security, comfort, and convenience. However, research shows that adoption varies across social groups and that SHT might affect energy demand. Denmark is considered a frontrunner in the uptake of SHT and offers a unique opportunity to combine administrative household information with energy consumption data and survey responses on SHT uptake. This paper studies a representative sample of 1,468 Danish households to investigate SHT diffusion, social differences in SHT adoption, and correlations between SHT and energy consumption, divided into appliance use and heating. The results indicate that entertainment and home security are the most popular SHT services in Denmark and that SHT is more widespread among younger households, especially for entertainment and robotic help. In contrast, smart heating systems are more widespread among older households. Living in a detached house seems to increase the likelihood of having SHT, while the highest income group appears more likely to have security systems, and technical education seems associated with the adoption of SHT for heating management. Finally, smart security systems were associated with higher electricity consumption, and SHT for electricity management was associated with higher heating consumption and to a lesser extent higher electricity consumption, but there was no significant correlation between having smart heating control and heating consumption.