Future Smart Home Research Articles

Design and Implementation of an IOT-based Smart Home Automation System in Real World Scenario

INTRODUCTION: Automation developments have significantly increased general convenience in modern technology. Our study focuses on developing and deploying an Internet of Things (IoT)–enabled smart home automation system that maximizes energy efficiency and improves convenience in residential settings. Our technology provides homes with an intelligent environment by smoothly combining several sensors, actuators, and communication protocols. We explore the challenges of creating a reliable and useful smart home systemthat works well in everyday situations. We investigate the difficulties, architectural issues, communication protocols, and security features specific to these systems.OBJECTIVES: This project aims to use a database server and Wi-Fi module to develop an effective and reasonably priced smart home automation system. It improves accessibility and convenience by enabling smartphone voice control of household appliances.METHODS: This project was developed using various implementation techniques, including wireless home automation via GSM technology, speech recognition-based systems, Blynk, and the Internet of Things. These techniques added up to a reliable and effective smart home system.RESULTS: Although it improved user routines, the IoT-based smart home system had latency problems. Interoperability issues are still present. Improvements were guided by user input. Using Blynk, our software manages loads remotely. CONCLUSION: A home automation system that is inexpensive and locally supplied can effectively operate household appliances. It is adaptable, scalable, and a component of future smart homes. IoT applications in real life revolutionize convenience and efficiency in living environments.

High-performance Smart Home System based on Optimization Algorithm

Background: With the recent COVID-19 pandemic, people have become increasingly concerned about their physical health. Therefore, the ability to monitor changes in the surrounding environment in real-time and automatically improve the environment has become a current hot topic to improve the overall health level. Objective: This article describes the design of a high-performance intelligent home system that can simultaneously perform monitoring and automatic adjustment functions. Methods: The ESP8266 was used as the core controller, and the DHT11 and G12-04 sensors were used to collect data, such as temperature, humidity, and ambient light intensity. The sampling frequency was increased and the sampled data were processed to improve data accuracy. The sampled data were wirelessly transmitted to a PC or mobile terminal for real-time display. When the sampled data underwent sudden changes, an alert message was sent via the mobile terminal. Based on the real-time changes in ambient light, an improved lighting brightness adjustment algorithm combining bang-bang and single neuron adaptive PID control was used to adjust the lighting brightness. Results: After testing the system designed in this paper and analyzing the errors compared to standard values, the temperature measurement error ranged from 0% to 0.01107%, and the humidity measurement error ranged from 0% to 0.03797%. The improved algorithm was simulated and tested using MATLAB software and compared with traditional PID algorithms and single-neuron adaptive PID algorithms. The improved algorithm did not overshoot during adjustment, and the system reached a steady state much faster than traditional algorithms. Conclusion: The system showed good performance in real-time, stability, and accuracy, fully demonstrating the effectiveness of the devices and algorithms used in the system. This provides ideas for the design and improvement of future smart homes.

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Advances in Internet of Things (IoT) technologies have had a profound impact on the experiential tasks of the smart home, particularly automated tasks supported by sensors, embedded systems, and communication devices that are capable of autonomously performing a range of tasks previously performed by humans. However, the improvement in the level of smart home automation has not necessarily promoted more meaningful, relaxing, and positive experiences for users. This is largely due to the fact that it often overlooks the meaningful involvement of humans in the process. This paper proposes a conceptual design model for a smart home through two studies that aim to maintain the benefits of automation while ensuring positive experiences with user engagement tasks. Study 1 involved qualitative data using semi-structured interviews to understand what tasks lead to positive user experiences (P-UX) during the use of the smart home and what kind of factors influence these positive experiences. Study 2, using an online questionnaire, quantitatively investigated the varying impact of these factors on positive experiences in both automation and human–machine interactions. The results of both studies show that there are nine factors that influence positive user experience in the smart home, with instrumentality, convenience, and flexibility playing an important role in the positive experience of automation, aesthetics, immersion, association, and memory, having a greater effect on the positive experience of user engagement in interactions, and customization and emotion contributing to both paradigms. In future smart home design, this model will help designers rationally allocate tasks between automation and human–machine interaction as a way to enhance the overall positive user experience.

Self‐powered HP‐LED intelligent energy management system with adaptive brightness adjustment

AbstractThe energy harvesting system achieved from one environmental energy channel is facing more and more challenges in meeting the power supply of electronic energy‐consuming devices. In this study, a combined photovoltaic (PV) and Thermoelectric Generator (TEG) energy management system is proposed with intelligent self‐powered technique for high‐power light‐emitting diodes (HP‐LED) lighting. Both the solar energy and waste heat induced by PV and HP‐LED are synchronously converted into electrical energy. Self‐powered by this gathered power, an adaptive light intensity control of HP‐LED is also realized using the established feedback path between ambient light and HP‐LED light. Experimental results show that the supply voltage of the TEG required for startup of the whole system can be decreased as 20.8 mV. Only 10 s is needed to complete the startup process under sufficient light, while 25 s in case of insufficient conditions. This energy management can provide a new technology for future smart home and greenhouse planting.

Microwave Speech Recognizer Empowered by a Programmable Metasurface.

Speech recognition becomes increasingly important in the modern society, especially for human-machine interactions, but its deployment is still severely thwarted by the struggle of machines to recognize voiced commands in challenging real-life settings: oftentimes, ambient noise drowns the acoustic sound signals, and walls, face masks or other obstacles hide the mouth motion from optical sensors. To address these formidable challenges, an experimental prototype of a microwave speech recognizer empowered by programmable metasurface is presented here that can remotely recognize human voice commands and speaker identities even in noisy environments and if the speaker's mouth is hidden behind a wall or face mask. The programmable metasurface is the pivotal hardware ingredient of the system because its large aperture and huge number of degrees of freedom allows the system to perform a complex sequence of sensing tasks, orchestrated by artificial-intelligence tools. Relying solely on microwave data, the system avoids visual privacy infringements. The developed microwave speech recognizer can enable privacy-respecting voice-commanded human-machine interactions is experimentally demonstrated in many important but to-date inaccessible application scenarios. The presented strategy will unlock new possibilities and have expectations for future smart homes, ambient-assisted health monitoring, as well as intelligent surveillance and security.

Application of Deep Learning and Intelligent Sensing Analysis in Smart Home

Deep learning technology can improve sensing efficiency and has the ability to discover potential patterns in data; the efficiency of user behavior recognition in the field of smart homes has been further improved, making the recognition process more intelligent and humanized. This paper analyzes the optical sensors commonly used in smart homes and their working principles through case studies and explores the technical framework of user behavior recognition based on optical sensors. At the same time, CiteSpace (Basic version 6.2.R6) software is used to visualize and analyze the related literature, elaborate the main research hotspots and evolutionary changes of optical sensor-based smart home user behavior recognition, and summarize the future research trends. Finally, fully utilizing the advantages of cloud computing technology, such as scalability and on-demand services, combining typical life situations and the requirements of smart home users, a smart home data collection and processing technology framework based on elderly fall monitoring scenarios is designed. Based on the comprehensive research results, the application and positive impact of optical sensors in smart home user behavior recognition were analyzed, and inspiration was provided for future smart home user experience research.

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion.

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.

A Virtual Reality Memory Test for Assessing Visuospatial Memory

AbstractBackgroundVisuospatial memory impairment is one of the common symptoms of Alzheimer’s disease. However, impairment in visuospatial and visuospatial memory function has not been focused on as much as that of episodic memory function. We developed a visuospatial memory test, the Hidden Objects Test (HOT), that was designed for people to go around the living room of a house created by virtual reality (VR) and find hidden objects.MethodThis study group comprised 17 Alzheimer’s disease dementia (ADD), 14 amnestic mild cognitive impairment (aMCI), and 15 normal controls (NC). All participants completed a sandardized neuropsychological battery and magnetic resonance imaging (MRI). Out of 46 participants, 28 received amyloid positron emission tomography (PET). Participants performed a novel visuospatial memory test in a virtual environment using VR headset HTC Vive. Walking trajectory in the virtual environment was tracked with 13Hz sampling rate. Participants’ movement path was basically assessed by the total distance, duration, and mean speed.ResultTotal HOT score and five subtests scores were differed among ADD, aMCI, and NC (p <0.001). We performed walking trajectory pattern mining as well as basic trajectory feature analysis such as total distance, duration, and speed. Total duration was significantly greater in ADD than in NC (p = 0.008). The number of outliers, over 95% of estimated trajectory, was significantly higher in ADD than in NC (p = 0.002). The number of stay points, an index that participants stayed in the same position, were significantly higher in ADD and aMCI patients compared to NC (ADD vs. NC: p = 0.003, aMCI vs. NC: p = 0.019). Walking trajectory analysis suggested that ADD and aMCI patients wandered rather than going straight to the hidden objects.ConclusionOur study showed that HOT total scores differed among ADD, aMCI, and NC groups. In addition, our study provided application of trajectory data mining methods to explore the patterns of ADD and aMCI patients' walking trajectory, which could be applied to detect behavior of the patients in environments equipped with the internet of things such as future smart home or facilities.

In-situ manipulation of wireless link with reinforcement-learning-driven programmable metasurface in indoor environment

It is of great importance to control flexibly wireless links in the modern society, especially with the advent of the Internet of Things (IoT), fifth-generation communication (5G), and beyond. Recently, we have witnessed that programmable metasurface (PM) or reconfigurable intelligent surface (RIS) has become a key enabling technology for manipulating flexibly the wireless link; however, one fundamental but challenging issue is to online design the PM's control sequence in a complicated wireless environment, such as the real-world indoor environment. Here, we propose a reinforcement learning (RL) approach to online control of the PM and thus in-situ improve the quality of the underline wireless link. We designed an inexpensive one-bit PM working at around 2.442 ​GHz and developed associated RL algorithms, and demonstrated experimentally that it is capable of enhancing the quality of commodity wireless link by a factor of about 10 ​dB and beyond in multiple scenarios, even if the wireless transmitter is in the glancing angle of the PM in the real-world indoor environment. Moreover, we also prove that our RL algorithm can be extended to improve the wireless signals of receivers in dual-receiver scenario. We faithfully expect that the presented technique could hold important potentials in future wireless communication, smart homes, and many other fields.

A Review of Advanced Transceiver Technologies in Visible Light Communications

Visible Light Communication (VLC) is an emerging technology that utilizes light-emitting diodes (LEDs) for both indoor illumination and wireless communications. It has the potential to enhance the existing WiFi network and connect a large number of high-speed internet users in future smart home environments. Over the past two decades, VLC techniques have made significant strides, resulting in transmission data rates increasing from just a few Mbps to several tens of Gbps. These achievements can be attributed to the development of various transceiver technologies. At the transmitter, LEDs should provide high-quality light for illumination and support wide modulation bandwidths. Meanwhile, at the receiver, optics systems should have functions such as optical filtering, light concentration, and, ideally, a wide field of view (FOV). The photodetector must efficiently convert the optical signal into an electrical signal. Different VLC systems typically consider various transceiver designs. In this paper, we provide a survey of some important emerging technologies used to create advanced optical transceivers in VLC.

Who are Smart Home Users and What do they Want? – Insights from an International Survey

Abstract Any set of devices for controlling home appliances that link to a common network and may be controlled independently or remotely are typically referred to as smart home technology. Smart homes and home automation are not completely unknown to people anymore; smart devices and sensors are part of daily life in the 21st century. Among other benefits of home automation devices, it is possible to manage home appliances and monitor resource usage, and security. It is essential to find practical information about smart home users, and possible use cases. The current survey covers smart home usage benefits and challenges for the users. The study presents the result of the collected information from different countries, and the participants are people from a variety of age groups and occupations. The questionnaire that contains both qualitative and quantitative questions was distributed through internet channels such as blog posts and social network groups. Furthermore, to generate the survey questions we conducted a literature review to gain a better understating of the subject and the related work. The research provides a better foundation for future smart home development. As a result of this survey-based study and in addition to finding the desirable home automation features, we discovered the amount of money users are ready to spend to automate their homes. Connecting the favourite smart home features to its users and the amount of money they are ready to spend on them can provide a bigger picture for the smart home industry as a whole and particularly be beneficial for developers and start-ups.

“It's never telling me that I'm good!” Household experiences of testing a smart home energy management system with a personal threshold on energy use in Sweden

Although smart technologies are widely promoted as enabling flexibility in households' energy demand, they often fail to achieve substantial impact. How smart technologies are actually used and to what extent they enable changes of energy-reliant practices in everyday life therefore needs to be better understood. This paper evaluates a smart home energy management system, Ero 2.0, which was tested by households in a multi-residential building in Sweden. To our knowledge, apart from its forerunner, Ero 2.0 is the first of its kind to include a personal threshold on energy use varying with the availability of preferred energy sources. Semi-structured interviews were conducted with 15 participants, complemented by pre and post surveys, answered by 39 and 32 participants respectively. Overall, Ero 2.0 contributed to raised awareness regarding electricity and water use. To some extent, it also contributed to changes in practices, mainly through changed or reduced use of certain functions. However, it was less effective in enabling shifting practices in time. Difficulties to change practices were found to depend on: (1) lack of flexibility in everyday life, (2) limitations in size and layout of the dwelling, (3) lack of incentives and perceived impact, (4) lack of guidance and (5) lack of possibilities to control devices through the interface. The second point is an aspect that to date has received little attention in smart energy research. Design opportunities for future smart home energy management systems are discussed while acknowledging that such technologies cannot alone achieve the transition to more sustainable energy systems.

Impact of swiping direction on the interaction performance of elderly-oriented smart home interface: EEG and eye-tracking evidence.

Smart home technology is increasingly popular, yet not all seniors are receptive and comfortable with it. This situation recognizes that the usability of smart home interfaces is particularly important. Most studies on interface swiping direction demonstrate the advantages of horizontal over vertical swiping, but the findings lack age-based as well as gender-specific judgments. In this paper, we use cognitive neural techniques of EEG and eye-tracking, combined with a subjective preference questionnaire, to analyze the preference of older persons for the swiping direction of smart home interfaces in a multimodal manner. The EEG data showed that swiping direction had a significant effect on potential values (p = 0.001). Also, the mean power in the δ and the θ band was enhanced during vertical swiping. Gender had no significant effect on potential values (p = 0.085), but the cognitive task was more EEG stimulating for females. The eye-tracking metrics data showed a significant effect of swiping direction on fixation duration (p = 0.047) and a non-significant effect on pupil diameter (p = 0.576). These results were consistent with the results of the subjective preference questionnaire, both demonstrating a preference for vertical swiping among participants. This paper uses three research tools simultaneously, combining objective perceptions as well as subjective preferences, to make the findings more comprehensive and reliable. Gender differences were also taken into account and differentiated in the data processing. The findings of this paper are different from most previous studies and better reflect the preference of elderly people for swiping directions, which can provide a reference for the future elderly-friendly smart home interface design.

SaferHome: Interactive Physical and Digital Smart Home Dashboards for Communicating Privacy Assessments to Owners and Bystanders

Private homes are increasingly becoming smart spaces. While smart homes promise comfort, they expose most intimate spaces to security and privacy risks. Unfortunately, most users today are not equipped with the right tools to assess the vulnerabilities or privacy practices of smart devices. Further, users might lose track of the devices installed in their homes or are unaware of devices placed by a partner or host. We developed SaferHome, an interactive digital-physical privacy framework, to provide smart home users with security and privacy assessments and a sense of device location. SaferHome includes a digital list view and physical and digital dashboards that map real floor plans. We evaluated SaferHome with eight households in the wild. We find that users adopted various strategies to integrate the dashboards into their understanding and interpretation of smart home privacy. We present implications for the design of future smart home privacy frameworks that are impacted by technical affinity, device types, device ownership, and tangibility of assessments.

Thermal insulating walls based on Ti3C2TX as energy storage panels for future smart house

Sustainable decentralized energy generation and storage in the cities are critical for a sustainable future. Here we design a smart energy storage device based on thermal insulation and MXene (Ti3C2Tx) for powered future smart homes. The modified surface of a common thermal insulation wall (TIW) using Ti3C2Tx and polyaniline (PANI) by in situ chemical oxidative polymerization of aniline monomer serves as an energy storage device in the wall and, at the same time, maintains the temperature inside the house. The as-fabricated PANI@Ti3C2Tx-TIW-based supercapacitor exhibited high specific capacitance with outstanding rate capability, cyclic stability, mechanical stability, and power density, and functions in extreme temperatures (−15 °C to 45 °C). Further, the device was integrated into real rock mineral wool insulation to develop a future house energy storage system that can store electricity in the house wall and supply power to operate emergency evacuation and alert devices in the event of a disaster.

Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model.

Building energy consumption prediction has become an important research problem within the context of sustainable homes and smart cities. Data-driven approaches have been regarded as the most suitable for integration into smart houses. With the wide deployment of IoT sensors, the data generated from these sensors can be used for modeling and forecasting energy consumption patterns. Existing studies lag in prediction accuracy and various attributes of buildings are not very well studied. This study follows a data-driven approach in this regard. The novelty of the paper lies in the fact that an ensemble model is proposed, which provides higher performance regarding cooling and heating load prediction. Moreover, the influence of different features on heating and cooling load is investigated. Experiments are performed by considering different features such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. Results indicate that relative compactness, surface area, and wall area play a significant role in selecting the appropriate cooling and heating load for a building. The proposed model achieves 0.999 for heating load prediction and 0.997 for cooling load prediction, which is superior to existing state-of-the-art models. The precise prediction of heating and cooling load, can help engineers design energy-efficient buildings, especially in the context of future smart homes.

Understanding of IoT smart home environment based on the transformation of domestic interior spatial morphology: Case study of Beijing families

There are two difficulties in designing a future home for urban dwellers. First is the uneven understanding between the physical built environment and everyday activities led by technological development. Second is the vague understanding between family living culture and the spatial arrangement of a dwelling. The invention of new digital technologies, such as big data, artificial intelligence (AI), and internet of things (IoT), has entirely transformed the lifestyle of urban dwellers. The gap between technology promotion in urban living environments and knowledge of living environment structures has raised the question of how smart homes could be designed in future cities. Therefore, in this paper, I examine the transformation of dwelling interior morphology for Beijing families. Five typical families are selected from 30 family samples according to the time of residence, original housing typology, family structure and occupations. The distinctiveness of family culture and spatio-temporal distribution of domestic activities could be found in the 1980s between five families, which tend to be influenced by the macro changes socially, economically, politically and technologically. The nature of IoT smart home environments and the communication between devices builds a context-awareness system based on technology. I argue that the smartness of homes could be achieved without a cultural perspective, which raises the question of design strategy for future IoT smart homes.

User requirements for the design of smart homes: dimensions and goals

The ‘Smart Home’ is a strongly technology-driven field. While user-centered requirements have been reported for specific features, a considerable gap persists for design based on an everyday home context and the social and emotional nature of the home. To address this, we present a user-centered design process to question and expand narrow framings of energy-efficiency and smart control and consider the richness and variety of the domestic context as design space for smart homes. Our three-step investigation employs cultural probing, participatory design fiction, and focus groups to progress from the home context “as-is” towards a blending of values with technological responses. Our findings highlight the home as a complex construct imbued with organically grown practices and individual and collective needs, values, and emotions. Based on empirical, real-user data we present features and system expectations that address this multifaceted overall picture. This paper advises the design process of future smart home solutions in three facets: first, we discuss the value of the design process applied in this study and future possibilities to expand. Second, we show design dimensions, namely time, space, relations, individual factors, and values that allow design for a heterogeneity of users and situations. Third, we derive specific design goals to highlight directions of smart home system design: design for control, low effort, integration, evolvability, identity, sociability, and benefits.

Voice Liveness Detection for Voice Assistants Through Ear Canal Pressure Monitoring

The voice assistants are important input devices in future smart homes. Thanks to the great performance that is provided by current voice recognition systems, current voice assistants can understand various commands in different language from users and connect with other devices in the same local network to perform corresponding actions. However, the voices are not secure due to its nature. Even if we secure voice assistant using the voiceprint, attackers can still steal the victim's voices and replay them to voice assistants for attacking purpose. In this paper, we propose a new voice liveness detection system that is specifically designed for voice assistants. The key insight behind our system is that users will open their mouth when they say some phonemes. Such opening mouth activities will impact the air pressure in the ear canal if the ear canal is an enclosed space. Therefore, we can detect the liveness of the voices on the side of voice assistants by cauterizing the correlations between each sentence and the air pressure. Experiments with ten volunteers show that our system can accurately accept voice commands from legitimate users with accuracy of 94.8% and 97%. Moreover, our system can effectively defend current voice assistant devices from replay attacks with accuracy of 99.25% and 99.5%.

Social Contexts, Agency, and Conflicts: Exploring Critical Aspects of Design for Future Smart Home Technologies

Smart devices are increasingly being designed for, and adopted in, the home environment. Prior scholarship has investigated the challenges that users face as they take up these devices in their homes. However, little is known about when and how users or potential users would prefer future domestic Internet of Things (IoT) to support their activities in home settings. To fill this gap, we conducted two co-design workshops, an in-home activity between the two sessions, and pre- and post-study interviews with 18 adult participants, who had diverse levels of prior experience of IoT use. Our findings contribute new insights into how smart home devices could adapt their behavior based on social contexts; how to re-imagine agency and support useful intelligibility; and how to resolve user-driven conflict by providing appropriate information about those with whom devices are shared. Finally, based on these findings, we discuss the implications of our work and provide a set of design considerations from which designers of future smart home technologies can benefit.