Smart Home System Research Articles

Evolution of AI in Smart Home Systems: A Comprehensive Review

The evolution of Artificial Intelligence (AI) has significantly transformed the landscape of smart home systems into a new era of convenience, efficiency, and personalized experiences. This paper provides an insightful exploration into the historical progression and contemporary developments of AI within smart home environments. Beginning with an overview of early automation technologies, we trace the evolutionary trajectory of AI's integration into smart homes, highlighting key milestones, breakthroughs, and paradigm shifts. Moreover, this paper also highlights the applications of AI in smart home ecosystems, including home automation, energy management, security surveillance, healthcare monitoring, and personalized assistance. Through advanced sensor technologies, ubiquitous connectivity, and cloud-based computing, AI-enabled smart home platforms offer seamless integration, interoperability, and scalability, paving the way for a more interconnected and intelligent living environment. Furthermore, we discuss the emerging trends and future directions shaping the evolution of AI in smart home systems. We also address the associated challenges and considerations, including privacy concerns, data security risks, ethical implications, and regulatory frameworks, which warrant careful attention and mitigation strategies.

Investigating the Behavioral Intention of Smart Home Systems among Older People in Linyi City

Background: With an aging population and the continuous advancement of smart technology, the Chinese government is exploring smart elderly care models to address the challenges posed by aging. Although smart home systems are viewed as a promising solution, their adoption rate among older people remains low. Objectives: This study aimed to investigate the factors influencing the behavioral intention to use smart home systems among older people in Linyi City, Shandong Province, China. Methods: A literature review revealed a lack of quantitative research on older people’s behavioral intention toward smart home systems based on the Innovation Diffusion Theory. This study developed an extended model based on the Innovation Diffusion Theory, Technology Acceptance Model, and external variables, incorporating eight variables: intergenerational technical support, perceived cost, self-reported health conditions, compatibility, observability, trialability, perceived usefulness, perceived ease of use, and behavioral intention. Results: Analysis of 387 valid questionnaires showed that compatibility and trialability significantly and positively affect perceived ease of use, while self-reported health conditions, perceived ease of use, and perceived usefulness have significant effects on behavioral intention. In addition, perceived cost had a negative influence on behavioral intention. Contributions/Significance: These findings highlight the importance of considering these factors in the design of smart home systems to improve user experience and provide valuable practical guidance to smart home system developers, R&D institutions, and policymakers.

Research on Security System of Smart Home Based on ZigBee

In view of the drawbacks of traditional smart home using wired layout, and the problem that it can not accurately transmit and integrate home information, this paper proposes a new type of smart home using ZigBee protocol in wireless sensor network compared with the advantages of traditional smart home. This paper introduces a smart home system based on CC2530 wireless MCU ZigBee network nodes. The remote monitoring system is applied to the smart home environment under the ZigBee protocol, which is used to better deal with the security problems of smart home.

A sound event detection support system for smart home based on “two-to-one” teacher–student learning

Sound event detection (SED) is a core technology in smart home projects that rely on detected sound events to trigger specific actions. SED systems face two major challenges: high labeling costs and complex acoustic environments. To reduce labeling costs, some semi-supervised systems extract both global and local features for classification. However, these methods treat global and local features equally, not accounting for their varying importance when recognizing different types of sound events. Furthermore, to address complex acoustic environments, some studies use multitask learning frameworks to introduce SED-related tasks as auxiliaries to improve detection performance. However, these methods fail to align tasks within the framework, leading to conflicting outputs that may limit system performance. To address these issues, in this paper we propose a “two-to-one” teacher-student learning based semi-supervised SED system. This system employs a gating mechanism to selectively enhance global and local features, improving adaptability to different types of sound events, and incorporates a cross-task alignment module to interact SED with related tasks, reducing the risk of performance degradation caused by conflicting outputs. Experimental results on two datasets demonstrate that our system achieves the best performance in all metrics, with EB-F1 scores of 48.1 % and 64.7 %, representing improvements of 15.3 % and 10.6 % over the baseline ConformerSED system, respectively. Our work offers an effective SED solution for smart home projects by providing a semi-supervised SED system that performs well while reducing labeling costs.

IoT Security Using Machine Learning Methods with Features Correlation

The Internet of Things (IoT) is an innovative technology that makes our environment smarter, with IoT devices as an integral part of home automation. Smart home systems are becoming increasingly popular as an IoT service in the home that connects via a network. Due to the security weakness of many devices, the malware is targeting IoT devices. After being infected with malicious attacks on smart devices, they act like bots that the intruders can control. Machine learning methods can assist in improving the attack detection process for these devices. However, the irrelevant features raise the computation time as well as affect the detection accuracy in the processing with many features. We proposed a machine learning-based IoT security framework using feature correlation. The feature extraction scheme, one-hot feature encoding, correlation feature selection, and attack detection implement an active detection mechanism. The results show that the implemented framework is not only for effective detection but also for lightweight performance. The proposed system outperforms the results with the selected features, which have almost 100% detection accuracy. It is also approved that the proposed system using CART is more suitable in terms of processing time and detection accuracy.

Design and Development of IoT-Enabled Power Management System for Smart Home with Enhanced Security Feature

Design and Development of IoT-Enabled Power Management System for Smart Home with Enhanced Security Feature

PENGENDALI PINTAR DALAM PENGONTROLAN LAMPU DAN KIPAS MENGGUNAKAN BLUETOOTH

The evolution of mobile technology has revolutionized it from a mere communication tool to a versatile platform capable of handling images, videos, documents, and controlling other devices such as TVs. The Android OS gives developers the flexibility to create applications that meet the diverse needs of users. This shift has made smartphones indispensable, supporting tasks from browsing to entertainment and productivity. The popularity of Android ensures seamless integration with existing hardware for efficient application performance. This research aims to design a smart home device control system, focusing on lights and fans via Android on an Arduino Nano microcontroller. Android's affordability and development through third-party applications make it ideal for smart home systems. Integrating the Arduino Nano and the EMS Blue Shield Bluetooth module improves convenience and energy efficiency. Its limitations include control of basic functions (lights, fan speed) and Android smartphones with Bluetooth. This study aims to design a practical control system and understand the device operation and Android communication. The test results of data transmission response time (latency) show that the average response time of the system for five days is 122.3 ms, with small variations and standard deviations between 2.81 ms and 3.92 ms. The range of response time ranged from 118 ms to 130 ms.

Exploration on the Application of Smart Home Technologies to Enhance the Quality Design of Elderly-Friendly Living space in Existing Residences

Drawing upon the progressively evolving theoretical frameworks and methodologies for housing adaptation tailored to the needs of the elderly, this study explores the enhancement of age-friendly living spaces through the integration of smart home technologies. A novel, multidimensional framework is introduced, integrating the elements of "elderly individuals, living areas, functional spaces, and technologies," fostering a comprehensive application of technology in elderly care. This approach aims to scrutinize the seamless integration of technological advancements into the daily lives of the elderly from a multidisciplinary perspective. The study delves into the effectiveness and adaptation strategies of smart home systems across three pivotal scenarios: daily living, health monitoring, and safety measures. It underscores the pivotal role of smart home technologies in elevating the well-being of the elderly. By implementing sophisticated smart solutions, such as voice commands and streamlined operations, these technologies empower seniors to effortlessly manage lighting, climate control, and security systems within their living spaces, fostering a customized, intelligent living ambiance. This initiative not only propels the application of smart home systems in the lives of the elderly but also ensures a safer, more comfortable, and autonomous lifestyle.

Exploring charge regeneration effect in interdigitated array electrodes-based TENGs for a more than 100-fold enhanced power density

The vast potential of harnessing high entropy and abundantly available mechanical energy through triboelectric nanogenerators (TENGs) has attracted significant attention in recent years. However, the cost of harvesting this energy has often outweighed the energy collected. Recent advancements in TENGs for blue energy harvesting from water flow have shown great promise. In this study, we present a novel approach to optimize the performance of interdigitated electrode array-based TENGs operating in free-standing mode (IDA-FTENG) by introducing a gap-to-width ratio (GWR) relationship for the electrodes and its impact on the charge regeneration effects. We investigate the dependence of the charge regeneration effect on GWRs and the number of electrode pairs to enhance the performance of IDA-FTENGs, employing a rapid and industrially scalable laser scribing process for fabricating the devices. An optimized device, featuring a maximum of 34 interdigitated electrode grids, demonstrates a 140-fold increase in power density compared to conventional single electrode pair TENGs (SEP-TENGs). Furthermore, power density projections indicate that the optimized IDA-FTENGs can compete with current solar cells, if designed suitably. We showcase the applicability of the proposed IDA-FTENG devices in self-powered sensors, autonomous wireless operations, security monitoring, and smart home systems.

DiffCRNN: A Novel Approach for Detecting Sound Events in Smart Home Systems Using Diffusion-based Convolutional Recurrent Neural Network

DiffCRNN: A Novel Approach for Detecting Sound Events in Smart Home Systems Using Diffusion-based Convolutional Recurrent Neural Network

Prototype, method, and experiment for evaluating usability of smart home user interfaces

Despite the widespread adoption of smart devices and home automation systems, users still face usability challenges. Additionally, evaluating the usability of smart home systems is not straightforward. Our paper focuses on building a Smart Home Prototype, selecting, and evaluating software User Interfaces (UI). We have chosen the smart home heating system as the basis, but the principles are applicable to other applications. To discuss smart home UIs, it is necessary to understand the components, implementation scenarios, configuration, and operation of smart home systems. Therefore, we devote considerable attention to these topics in the paper. Our smart home system utilizes a Raspberry Pi computer, temperature sensors, servo drives, an Android-based smartphone, and other necessary hardware. We installed and configured open-source software and third-party services on the experimental setup. We provided a brief description of each selected software. We used the Home Assistant operating system, its mobile application, voice assistant Alice, and Telegram chatbot. In our study, we described a methodology for evaluating user interfaces in smart homes and conducted an experiment to assess their usability. The experiment results indicated which interface was more user-friendly and identified the drawbacks of each. In conclusion, we discuss UI features that can improve usability and available options for building a smart home based on capabilities.

Reliability model and emergency maintenance strategies for smart home systems

After the smart home system is impacted by malicious attacks or other factors, the system reliability drops drastically, so the user's living environment is greatly disturbed. To determine the emergency maintenance strategy, firstly, we study the failure mechanism of the smart home system by the fault tree theory, and distinguish the components into two types: critical components and non-critical components. Secondly, a recovery prioritization is proposed to measure the extent to which changes in the reliability of each component affect the reliability of the system. Then, three scenarios of smart home system failure are proposed. Under the constraints of time and cost, the maintenance strategies for the three scenarios are determined to achieve emergency recovery. Finally, through the multi-objective particle swarm optimization algorithm, the system reliability is maximized, the maintenance cost is minimized, and the optimal reliability recovery level of each component is obtained, so that the reliability of the smart home system is further improved. The effectiveness and practicality of the emergency maintenance strategies proposed in this paper are demonstrated by a smart home system example.

Rekayasa SmartHome System Berbasis Internet of Things

In the current digital era, the Internet of Things (IoT) has emerged as a concept that presents new opportunities to expand sustainable internet connectivity. In the context of Smarthome, IoT enables the control of electronic devices through smartphone applications using internet connectivity, bringing various benefits, including increased comfort, enhanced security, better electricity savings, and introducing innovative solutions that not only allow real-time monitoring of home environments but also provide accessibility by considering the needs and preferences of various users, including families, busy individuals, and people with disabilities. The implementation of an IoT-based Smarthome system utilizes NodeMCU 8266 as a microcontroller, MIT App Inventor as a control tool, Thingspeak as a data processing platform, ESP32-CAM as a camera, and Telegram bot as a notification sender. This system includes the control of lights, fan, environmental monitoring, motion detection, gas leak detection, and fire detection. Various sensors are used, including Flame sensors for fire detection, MQ5 sensors for gas leak detection, and light sensors for automatically turning on lights. Relays are also applied to connect electronic devices to the system. Testing and system analysis show that equipment control operates according to the given commands, provided that the internet connection remains stable and continuous. This underscores the importance of a reliable network infrastructure in supporting the smooth operation of IoT-based Smarthome systems.

Enhancing smart home interaction through multimodal command disambiguation

AbstractSmart speakers are entering our homes and enriching the connected ecosystem already present in them. Home inhabitants can use those to execute relatively simple commands, e.g., turning a lamp on. Their capabilities to interpret more complex and ambiguous commands (e.g., make this room warmer) are limited, if not absent. Large language models (LLMs) can offer creative and viable solutions to enable a practical and user-acceptable interpretation of such ambiguous commands. This paper introduces an interactive disambiguation approach that integrates visual and textual cues with natural language commands. After contextualizing the approach with a use case, we test it in an experiment where users are prompted to select the appropriate cue (an image or a textual description) to clarify ambiguous commands, thereby refining the accuracy of the system’s interpretations. Outcomes from the study indicate that the disambiguation system produces responses well-aligned with user intentions, and that participants found the textual descriptions slightly more effective. Finally, interviews reveal heightened satisfaction with the smart-home system when engaging with the proposed disambiguation approach.

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.

Robot Motion Planning Based on an Adaptive Slime Mold Algorithm and Motion Constraints

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time.

Integrasi Artificial Intelegent Berbasis Sistem Operasi Android pada Smart Home

This study explores the integration of Artificial Intelligence (AI) into smart home systems using the Android operating system to enhance security, privacy, efficiency, and user comfort. Key security measures include data encryption, robust authentication methods, sandboxing, and AI integration, specifically leveraging Google Assistant for improved privacy controls. Maintenance strategies for smart homes emphasize energy management, device condition monitoring, and enhanced safety features. AI adaptation to user habits enhances productivity and situational awareness, while Android's role in connecting various IoT devices facilitates remote control and energy-efficient recommendations. Methods such as Eco Android, Greensource, byte-code transformations, and automated energy diagnosis tools aid in optimizing energy use. The comparison between smart and non-smart homes highlights the efficiency and convenience of smart homes despite higher installation costs and potential network issues. The development and deployment of an Android-based application, SafeHause, exemplifies practical implementation, emphasizing end-to-end testing, security updates, and user education. The findings affirm that AI integration with Android significantly improves the smart home experience by enhancing energy optimization, data security, and personalized user interaction. Furthermore, the study discusses future trends in smart home technology, such as the potential for more advanced AI algorithms and machine learning techniques to provide even greater personalization and automation. The importance of regular software updates and the role of user feedback in refining smart home systems are also highlighted, ensuring that these technologies continue to evolve and meet user needs effectively.

On the Development of a New Flexible Pressure Sensor.

The rapid advancement of the Internet of Things (IoT) serves as a significant driving force behind the development of innovative sensors and actuators. This technological progression has created a substantial demand for new flexible pressure sensors, essential for a variety of applications ranging from wearable devices to smart home systems. In response to this growing need, our laboratory has developed a novel flexible pressure sensor, designed to offer an improved performance and adaptability. This study aims to present our newly developed sensor, detailing the comprehensive investigations we conducted to understand how different parameters affect its behaviour. Specifically, we examined the influence of the resistive layer thickness and the elastomeric substrate on the sensor's performance. The resistive layer, a critical component of the sensor, directly impacts its sensitivity and accuracy. By experimenting with varying thicknesses, we aimed to identify the optimal configuration that maximizes sensor efficiency. Similarly, the elastomeric substrate, which provides the sensor's flexibility, was scrutinized to determine how its properties affect the sensor's overall functionality. Our findings highlight the delicate balance required between the resistive layer and the elastomeric substrate to achieve a sensor that is both highly sensitive and durable. This research contributes valuable insights into the design and optimization of flexible pressure sensors, paving the way for more advanced IoT applications.

A Model Design Using Blockchain and Smart Contracts Against Cyberattacks in Smart Home Systems

A Model Design Using Blockchain and Smart Contracts Against Cyberattacks in Smart Home Systems

Smart Home and Thief Detection using AI

In our world increasingly interconnected, demand for smart home solutions that prioritizing security a convenient ever-growing. This abstract presents an inventive approach to home security an automation through integration of ESP32-CAM, ultrasonic sensor, a Firebase technology. Our system offering real-time theft detecting a remote controlling of home appliances via mobile app. By leveraging AI-drive theft detection a Firebase's cloud-based platform, users can monitor a managing their homes from anywhere, enhancing both security a convenient. This abstract outline component, workflow, features, a potential future enhancement of our Smart Home and Theft Detect system, offering a glimpse into the future of home automation.