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The article offers a method for user identification in smart homes through touch-screen operation data, addressing privacy concerns associated with camera-based identification. Topics include the challenges of user-aware device usage detection in smart homes, the development of a non-intrusive user identification technique based on touch-screen button operations, and the application of supervised learning to accurately identify users.

In indoor environments, air quality significantly impacts human health and well-being, with carbon monoxide (CO) posing a particular hazard due to its colorless and odorless nature and potential to cause severe health issues. Integrating the Internet of Things and remote sensing technologies has revolutionized data monitoring, collection, and evaluation, especially within the context of 'smart' homes. This study leverages these technologies to enhance indoor air quality monitoring. By collecting data on key indoor atmospheric quality indicators—carbon dioxide (CO2), methane (CH4), alcohol, liquefied petroleum gas (LPG), particulate matter (PM1 and PM2.5), humidity, and temperature—the study aims to predict indoor carbon monoxide levels. A custom dataset was compiled from August to October, consisting of 61,710 observations recorded at one-minute intervals. The methodology employs a stacking ensemble approach, integrating multiple machine learning models to boost prediction accuracy and reliability. In the stacking ensemble, six distinct models are employed: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGBoost, and Support Vector Regression. Each model is individually trained and fine-tuned using the Grid Search method to optimize parameter combinations. These optimized models are then combined in the stacking ensemble, which achieves a Mean Squared Error (MSE) of 0.0140, a Root Mean Squared Error (RMSE) of 0.1185, and a Mean Absolute Error (MAE) of 0.0291. The results demonstrate that the proposed system significantly enhances the precision of CO prediction, underscoring its critical role in air quality surveillance within smart environments. [ABSTRACT FROM AUTHOR]

With the popularization of Internet of Things applications, trillions of new devices with different application requirements, such as smart wearables and smart home appliances, will be connected to the network. Hence, it is crucial to ensure the data transmission security of these low-power and multifunctional sensor nodes. In this paper, a novel lightweight block cipher, GFSPX, is proposed for resource-constrained microdevices. The proposed algorithm combines a generalized Feistel structure with the substitution permutation networks structure to design the round function, which effectively addresses the inherent problem of slow diffusion in the traditional Feistel structure. Furthermore, the introduction of Addition or AND, Rotation, XOR operations in the round function to process part of the plaintext reduces the demand for hardware resource of the algorithm. The avalanche test results indicate that the GFSPX algorithm has strong diffusion and can satisfy the avalanche effect in just six rounds. The security analysis results verify the security of the GFSPX algorithm against differential and linear cryptanalysis attacks, algebraic attacks, structural attacks and key scheduling attacks. Finally, the performance analysis results indicate that the hardware implementation cost of GFSPX algorithm is relatively low, requiring only 1715 GE based on 0.13 micron logic process. In addition, the software implementation of this algorithm works well at an encryption rate of 12.31 Mb/s. [ABSTRACT FROM AUTHOR]

Capacitive sensors have widespread applications in human-machine interaction, Internet of Things, and smart home systems due to their low cost, high sensitivity, and ease of integration. However, improving the sensitivity and sensing distance of capacitive sensors remains a challenging issue. This study proposes a novel capacitive sensor design method based on Kirigami structures, which enhances sensor performance by introducing specific cutting patterns into the conductive layer to leverage edge effects. Through experimental testing and statistical analysis, we systematically investigated the influence of Kirigami geometric parameters on sensor sensitivity and sensing distance. We designed and fabricated 12 different Kirigami structures, including circular flower patterns, array patterns, layered pointed flower patterns, and circular strip structures, and compared them with traditional non-cut structures. The results show that Kirigami structures significantly improved sensor performance. Compared to traditional sensors without Kirigami structures, optimally designed Kirigami capacitive sensors demonstrated approximately a 3-fold increase in sensitivity and up to 170 percent extension in sensing distance. Multivariate regression analysis and nonlinear models revealed complex relationships between Kirigami structural parameters and sensor performance. Notably, the circular strip (three-layer) structure exhibited the best performance, possibly due to its maximization of edge effects and optimization of electric field distribution. This study provides new design insights for developing high-performance capacitive sensors, with potential applications in improving smart home systems and indoor activity monitoring for solitary elderly individuals. [ABSTRACT FROM AUTHOR]

The pyroelectric effect has wide applications in various fields, such as infrared imaging, detection, alarms, and the expanding field of smart homes. The rapid advancement of smart systems, focusing on miniaturization and integration, requires pyroelectric infrared detectors with high pyroelectric coefficients and Curie temperatures to meet the requirements of integrated processes. However, the Curie temperature of commercial lead zirconate titanate is limited to <230°C, urgently looking for a breakthrough. Here, we explore pyroelectricity in Na0.5Bi4.5Ti4O15 (NBT) ceramics, characterized by a high Curie temperature (∼660°C). We systematically examined the crystal structure modifications and defect dipole effects of the Nb/Mn‐co‐doped NBT. The lattice expansion, distortion of the TiO6 octahedra, and structural transformation tendency from the orthorhombic to tetragonal phase facilitate dipole movements with increasing temperature. Furthermore, the Mn and Nb elements result in the formation of MnTi"–VO·· and NbTi·–MnTi" –NbTi· defect dipoles, inducing additional polarization changes in response to temperature variations. Finally, a significantly improved pyroelectric coefficient of 110 µC m2 K–1 and remarkable temperature stability from 25°C to 300°C is achieved in NBTM‐5Nb ceramics. This co‐doping strategy for enhancing pyroelectric performance can be expanded to other systems and substantially contribute to advancing high‐performance materials for infrared detection. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology is used in smart homes to enhance comfort, security, and energy efficiency. This study outlines a theoretical framework for the deployment and dissemination of IoT technologies in intelligent residential environments. The framework of this study highlights the significance of many components that cater to people's individual needs. The study offers a systematic explanation of IoT principles, tangible devices, services, data creation, software, applications, and logistical needs. Furthermore, it contains an extensive table that assists users in choosing the most appropriate resources and components. The study points to smart home systems based on IoT, which utilize sensors, controllers, and cloud solutions to manage data and provide user control while ensuring privacy and confidentiality. The study addresses various needs of users by utilizing a semantic vision framework for smart house design that guarantees economic advantages, enhanced security, and user contentment, ultimately leading to enhanced community interactions and the overall welfare of residents. [ABSTRACT FROM AUTHOR]

Home appliance manufacturers are increasingly focusing on building smarter home systems by incorporating user feedback to enhance products and services. To support this, we designed a federated learning (FL) system that includes a reputation mechanism to help manufacturers leverage customer data to train machine learning models. First, it downloads the initial model provided by the manufacturer and trains it with local data. Then, it asks customers to sign their models and upload them to the blockchain. To protect customer privacy, we implemented differential privacy and introduced a new normalization technique. In addition, we also attract more customers to participate in crowdsourced FL tasks by rewarding their contributions, thereby ensuring that the datasets for model training are robust and diverse. This system not only promotes collaboration between customers and manufacturers, but also facilitates the development of more responsive and smarter home appliance systems through advanced FL and blockchain technologies. [ABSTRACT FROM AUTHOR]

This paper presents an Optimal Power Management System (OPMS) for smart homes in 6G environments, which are designed to enhance the sustainability of Green Internet of Everything (GIoT) applications. The system employs a brute-force search using an exact solution to identify the optimal decision for adapting power consumption to renewable power availability. Key techniques, including priority-based allocation, time-shifting, quality degradation, battery utilization and service rejection, will be adopted. Given the NP-hard nature of this problem, the brute-force approach is feasible for smaller scenarios but sets the stage for future heuristic methods in large-scale applications like smart cities. The OPMS, deployed on Multi-Access Edge Computing (MEC) nodes, integrates a novel demand response (DR) strategy to manage real-time power use effectively. Synthetic data tests achieved a 100% acceptance rate with zero reliance on non-renewable power, while real-world tests reduced non-renewable power consumption by over 90%, demonstrating the system's flexibility. These results provide a foundation for further AI-based heuristics optimization techniques to improve scalability and power efficiency in broader smart city deployments. [ABSTRACT FROM AUTHOR]

Purpose: Drawing on the personalization–privacy paradox and guided by means–end analysis, this study explores how consumers balance their concerns for privacy and the benefits of smart home device personalization and the role that trust plays in the process. More specifically, this study aims to investigate how perceptions of smart device personalization and privacy concerns are shaped by consumers' experiences and the role of trust in the deliberation process. Design/methodology/approach: In-depth interviews were conducted across diverse demographic groups of smart device users to shed light on the balancing act between personalization and privacy. Findings: The study found that product experience, ownership type, perceived value of convenience and control and quality of life via "smart things" are key motivators for product usage. The benefits of tailored recommendations and high relevance are balanced against the risks of echo chamber effects and loss of control. The results also show the role of active involvement in the privacy calculus and trust level. The study points to the significance of an ecosystem-based service/business model in gaining consumer confidence when they balance between personalization and privacy. Originality/value: Although many studies have explored trust, privacy concerns and personalization in an artificial intelligence (AI)-related context, few have addressed trust in the context of both smart devices and the personalization–privacy paradox. As such, this study adds to the existing literature by incorporating the concept of trust and addressing both privacy concerns and personalization in the AI context. [ABSTRACT FROM AUTHOR]

Image captioning, which combines natural language processing and computer vision, has developed rapidly in recent years. It tends to be applied in data retrieval, blind navigation, intelligent transportation, smart home, medical assistance, news media and other domains. In order to elevate the consistency and abundance of image captioning languages and express people's subjective emotions effectively, a Generative Adversarial Network (GAN) is applied in this paper to obtain multi-stylized image emotion captions and generate two captions containing positive and negative emotions, respectively. Among them, Residual Network (ResNet) and Gate Recurrent Unit (GRU) are integrated into the generator, while the capsule neural network is applied to the discriminator. We conduct experiments on the popular MSCOCO and Senticap datasets to validate the model and demonstrate its satisfied performance in comparison to current advanced image captioning approaches. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Design Data Sources Methods Results Conclusion Impact Patient or Public Contribution To systematically review the potential of smart home technology to enhance the independence of older adults and reduce their dependence on care. Additionally, it sought to examine the positive impacts of such technology on their golden years.A systematic review based on Preferred Reporting Items for Systematic Review and Meta‐Analyses (PRISMA).The search was conducted on 8 April 2024. Peer‐reviewed studies in PubMed, Embase, Web of Science, IEEE Xplore, Scopus, The Cochrane Library, CINAHL, CNKI, WANFANG DATA and VIP from 1 January 2000 to 8 April 2024 were searched.The methodological quality assessment used the Mixed Methods Appraisal Tool (MMAT). Positive findings relevant to this study were extracted from the literature and analysed using thematic synthesis.After meticulously examining 3404 studies, we identified 21 relevant sources for in‐depth analysis, including qualitative studies (n = 10), experimental studies (n = 9) and mixed method studies (n = 2). These sources were grouped into five core themes based on the pivotal role of smart home technologies in enabling ageing in place: daily monitoring, assisted living activities, life reminders, functional improvement and emotional companionship. The study found that smart home technology offers numerous benefits to the lives of older adults, including increased independence, psychological support, improved cognitive functioning, enhanced self‐management, increased mobility, support for caregivers, promoted social engagement and enhanced quality of life.Smart home technology can enhance the independence of older adults' lives, reduce their dependence on care, alleviate the burden on caregivers and promote home‐based elderly care.This systematic review contributes to understanding the capability of smart home technology to promote elderly care at home and help better utilise smart home technology to benefit older adults. Older adults and their caregivers should be encouraged to adopt this technology to improve older adults' quality of life.No patient or public contribution. [ABSTRACT FROM AUTHOR]

In an aging society, the need for efficient emergency detection systems in smart homes is becoming increasingly important. For elderly people living alone, technical solutions for detecting emergencies are essential to receiving help quickly when needed. Numerous solutions already exist based on wearable or ambient sensors. However, existing methods for emergency detection typically assume that sensor data are error-free and contain no false positives, which cannot always be guaranteed in practice. Therefore, we present a novel method for detecting emergencies in private households that detects unusually long inactivity periods and can process erroneous or uncertain activity information. We introduce the Inactivity Score, which provides a probabilistic weighting of inactivity periods based on the reliability of sensor measurements. By analyzing historical Inactivity Scores, anomalies that potentially represent an emergency can be identified. The proposed method is compared with four related approaches on seven different datasets. Our method surpasses existing approaches when considering the number of false positives and the mean time to detect emergencies. It achieves an average detection time of approximately 05:23:28 h with only 0.09 false alarms per day under noise-free conditions. Moreover, unlike related approaches, the proposed method remains effective with noisy data. [ABSTRACT FROM AUTHOR]

In recent years, Internet of Things (IoT) applications have become across-the-board and are used by most smart device users. Wired Communication, Bluetooth, radio frequency (RF), RS485/Modbus, and zonal intercommunication global standard (ZigBee) can be used as IoT communication methods. The low delay times and ability to control homes from outside the building via the Internet are the main reasons wireless fidelity (Wi-Fi) communication is preferred. Commercially produced devices generally use their unique interfaces. The devices do not allow integration to form an intelligent home automation and demand-side energy management system. In addition, the high cost of most commercial products creates barriers for users. In this study, a local home automation server (LHAS) was created subject to low cost. Smart devices connected to the server through a Wi-Fi network were designed and implemented. The primary purpose of the design is to create an IoT network to form an LHAS. The IoT network will learn the energy consumption behavior of users for future Smart Grids. The designed intelligent devices can provide all the necessary measurements and control of houses. The open-source software Home Assistant (Hassio) was used to create the LHAS. Espressif systems (ESP) series microcontrollers (µCs) were chosen to design intelligent devices. ESP-01, NodeMCU, and ESP-32, the most widely used ESP models, were preferred. A convolutional neural network (CNN)/long short-term memory (LSTM) neural network was designed, and analysis was performed to learn the consumption behavior of residential users. [ABSTRACT FROM AUTHOR]

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a network of devices that can communicate and cooperate over the Internet. As the IoT expands, guaranteeing the dependability and accuracy of communication systems becomes increasingly important. One of the key challenges faced in the process of system development is the need to detection the errors in the early phases of system development. Formal techniques are the gold standard for ensuring a system's correctness. In the context of the IoT, this paper presents an Event-B formal model for the verification of the correctness of Content-Based Publish/Subscribe Systems (CBPS). We developed our model using Event-B, which is an incrementally formal technique with a plugin-supported platform. Furthermore, it supports both theorem proving and model checking. The incremental method uses a series of refining processes to help manage complexity. The paper offers a thorough exposition of the CBPS architecture, with an emphasis on decentralised design, reliable message delivery, and message ordering. This formalised method ensures that the CBPS system satisfies its criteria and free of errors. As a case study for our concept, we employ a smart home system. Finally, we validate and verify the formal model using proof obligations and the Rodin platform. [ABSTRACT FROM AUTHOR]

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes. [ABSTRACT FROM AUTHOR]

Current solutions that rely on a single-server architecture have privacy, anonymity, integrity, and confidentiality limitations. Blockchain-based solutions can address some of these issues but face challenges regulating behaviour and protecting access policy privacy. This study proposes a new approach to securing a smart home environment to overcome these limitations.The proposed architecture is based on a group signature scheme, which allows multiple IoT devices to securely exchange keys and authenticate each other without needing a central authority. Our experimental results indicate the effectiveness and efficiency of the proposed architecture and provide insights into its security and privacy features compared to existing IoT authentication methods. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a vast network of devices with sensors or actuators connected through wired or wireless networks. It has a transformative effect on integrating technology into people's daily lives. IoT covers essential areas such as smart cities, smart homes, and health-based industries. However, security and privacy challenges arise with the rapid growth of IoT devices and applications. Vulnerabilities such as node spoofing, unauthorized access to data, and cyberattacks such as denial of service (DoS), eavesdropping, and intrusion detection have emerged as significant concerns. Recently, machine learning (ML) and deep learning (DL) methods have significantly progressed and are robust solutions to address these security issues in IoT devices. This paper comprehensively reviews IoT security research focusing on ML/DL approaches. It also categorizes recent studies on security issues based on ML/DL solutions and highlights their opportunities, advantages, and limitations. These insights provide potential directions for future research challenges. [ABSTRACT FROM AUTHOR]

Human activity recognition(HAR) is an important research focus in ubiquitous computing. It has been widely applied in various domains, such as smart homes, healthcare assistance, and sports training. Accurate human activity recognition directly impacts the performance of downstream tasks. Existing methods for human activity recognition primarily rely on extracting temporal or spatial features from the data. These features are used for the task of human activity recognition. The existing methods mainly use CNN or RNN models to model the Euclidean correlations among spatially adjacent sensors or channels. However, non-Euclidean pairwise correlations among all sensors or channels are even critical for accurate classification, which has been ignored by the existing methods. In this paper, we incorporate fine-grained spatial structural data into the model to overcome these limitations. A novel deep learning model for human activity recognition is proposed, which is called the fine-grained data-oriented Spatial-Temporal Graph Transformer network (STGT). The introduction of the STGT model can eliminate the limitations of existing spatial feature extraction methods by leveraging a novel data organization approach proposed in this study. This model enhances the spatial features within the time feature extraction module for effective spatio-temporal feature extraction. We conducted experiments on four large-scale real-world HAR datasets to evaluate its performance. The experimental results demonstrate the superiority of our method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) is a technology that infers current user activities by using the available sensory data network. Research on activity recognition is considered extremely important, particularly when it comes to delivering sensitive services such as healthcare services and live tracking assistance and autonomy. For this purpose, many researchers have proposed a knowledge-driven approach or data-driven reasoning for identification techniques. However, there are multiple limitations associated with these approaches and the resulting models are typically not complete enough to capture all types of human activities. Thus, recent works have suggested combining these techniques through a hybrid model. This paper's goal is to give a brief overview of activity recognition implementation approaches by looking at various sensing technologies used to gather data from internet of things (IoT) gadgets, looking at preprocessing and feature extraction approaches, and then comparing methods used to identify human activities in smart homes, and highlighting their strengths and weaknesses across various fields. Numerous pertinent works were located, and their accomplishments were assessed. [ABSTRACT FROM AUTHOR]

Neural interfaces, emerging at the intersection of neurotechnology and urban planning, promise to transform how we interact with our surroundings and communicate. By recording and decoding neural signals, these interfaces facilitate direct connections between the brain and external devices, enabling seamless information exchange and shared experiences. Nevertheless, their development is challenged by complexities in materials science, electrochemistry, and algorithmic design. Electrophysiological crosstalk and the mismatch between electrode rigidity and tissue flexibility further complicate signal fidelity and biocompatibility. Recent closed‐loop brain‐computer interfaces, while promising for mood regulation and cognitive enhancement, are limited by decoding accuracy and the adaptability of user interfaces. This perspective outlines these challenges and discusses the progress in neural interfaces, contrasting non‐invasive and invasive approaches, and explores the dynamics between stimulation and direct interfacing. Emphasis is placed on applications beyond healthcare, highlighting the need for implantable interfaces with high‐resolution recording and stimulation capabilities. [ABSTRACT FROM AUTHOR]

The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments. [ABSTRACT FROM AUTHOR]

Light fidelity (Li-Fi) is a pioneering optical wireless communication (OWC) technology that utilizes visible light for wireless data transmission. Since its inception in a TED global talk by Professor Harald Haas in 2011, Li-Fi has captured significant attention in the research community. Smart home automation systems (SHAs) leverage internet of things (IoT) technology to remotely manage and automate various home devices and systems. Li-Fi technology has the potential to enable remote control of devices such as lighting, air conditioning, music systems, security cameras, and door locks within SHAs. This study presents Li-Fi-IoT, a Li-Fi-based system designed for efficient and secure IoT device management in SHAs. A series of experiments demonstrates the system's potential in IoT device control using Li-Fi technology. The research findings highlight the substantial improvement in data transfer speed, energy efficiency, and data security that Li-Fi technology can bring to SHAs. [ABSTRACT FROM AUTHOR]

The current study uses Field-Programmable Gate Array (FPGA) hardware to advance smart home technology through a self-learning system. The proposed intelligent three-hidden layer system outperformed prior systems with 99.21% accuracy using real-world data from the MavPad dataset. The research shows that FPGA solutions can do difficult computations in seconds. The study also examines the difficulties of maximizing performance with limited resources when incorporating deep learning technologies into FPGAs. Despite these challenges, the research shows that FPGA-based solutions improve home technology. It promotes the integration of sophisticated learning algorithms into ordinary electronics to boost their intelligence. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Considering human influence and its negative impact on the environment, the world will have to transform the current energy system into a cleaner and more sustainable one. In residential as well as office buildings, there is a demand to minimize electricity consumption, improve the automation of electrical appliances and optimize electricity utilization. This paper describes the implementation of a smart switch with extended facilities compared to traditional switches, such as visual indication of evacuation routes in case of fire and acoustic alerts for emergencies. The proposed embedded system implements Modbus RTU serial communication to receive information from a fire alarm-control panel. An extension to the Modbus communication protocol, called Modbus Extended (ModbusE), is also proposed for smart switches and emergency switchboards. The embedded smart switch described in this paper as a scientific and practical contribution in this field, based on a performant microcontroller system, is integrated into the Building Internet of Things (BIoT) concept and uses the innovative ModbusE protocol. The proposed smart lighting system integrates building lighting access control for smart switches and sockets and can be extended to incorporate functionality for smart thermostats, access control and smart sensor-based information acquisition. [ABSTRACT FROM AUTHOR]

In smart applications, edge nodes are deployed to perform faster computations. Due to the limited computational capability of edge nodes, collaborative computing is used in which multiple edge nodes collaborate for request processing. For faster processing, these edge nodes are used in many applications namely, smart homes, smart farming, healthcare and so forth. In this paper, we have discussed the use of edge nodes in smart home applications. The smart home application contains different types of sensors and these sensors generate various types of data. Edge nodes are used in these applications for the immediate processing of data. A data classifier is used to classify the data and to reduce delay in data processing. However, the data classifier is more susceptible to DDoS attacks. Hence, an efficient attack detection mechanism is required to detect DDoS attacks. We have used a Feature Selection SVM (FSSVM) algorithm to select optimal parameters for attack recognition. In this algorithm, the information gain ratio is used for optimal parameter selection, and SVM is used for classification. The FSSVM algorithm has been compared with KPCA‐SVM, SVM, and Naive Bayes. Simulation results show that the FSSVM algorithm provides better accuracy compared to KPCA‐SVM, SVM, and Naive Bayes algorithms. [ABSTRACT FROM AUTHOR]

The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models -LSTM and CNN -are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively. [ABSTRACT FROM AUTHOR]

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as "IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed". As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems. [ABSTRACT FROM AUTHOR]

The advent of smart cities has revolutionized urban living by providing innovative solutions, such as smart homes, smart hospitals, and smart parking. These smart applications have made life easier for people by improving infrastructure and accessibility. However, the development of smart cities also poses significant challenges for cybersecurity. The smooth operation of smart applications is essential to ensure the well‐being of users, and any disruption caused by cyber‐attacks can lead to critical situations. Malware, malicious software that can cause harm to devices or systems, is the most common type of cyber‐attack. Smart applications may consist of various heterogeneous devices, each with different security requirements and specifications, making it difficult to present an efficient mechanism against malicious software for all devices within different smart applications. Hence, developing a flexible and efficient solution to overcome this challenge is vital. This research presents a framework termed as Secure Software Update for the Internet‐of‐Things (SSUIT), which is designed to protect IoT devices from malicious software updates. This framework includes three primary components: publishers hosted on the cloud platform, an intelligent broker implemented on edge devices, and IoT devices as the subscribers. The publishers send software updates to the intelligent broker, which detects whether the update is malicious or not. The intelligent broker includes a secure software engine that integrates a disassembler, a preprocessor, and predictive models to detect malicious software. The predictive models are designed by taking into account the resource‐constrained nature of IoT systems. The end‐to‐end time taken for complete execution of a software update is also reported. [ABSTRACT FROM AUTHOR]

Leveraging the ultra‐wideband advantages of the terahertz band, Integrated sensing and communication (ISAC) facilitates high‐precision sensing demands in human smart home applications. ISAC channel characteristics are the basis for ISAC system design. Currently, the ISAC channel is divided into target and background channels. Existing researches primarily focus on the attributes of human target itself, e.g. radar cross‐section and micro‐Doppler effect. However, the impact of human target on neither the pathloss characteristic of background channel nor the multipath propagation characteristic of target channel is considered. To address the gap, we conduct indoor channel measurements at 105 GHz to investigate the ISAC channel characteristics with the impact of human target. Firstly, by analysing the power angular delay profiles with and without human target, the changes in quantity and power of multipath components (MPCs) are observed. Then, a parameter called power control factor is proposed to evaluate the human target impact on pathloss, thereby modifying the existing pathloss model of background channel. Eventually, the MPCs belonging to target channel are extracted within target‐oriented power delay profile to count the power proportion of each bounce MPCs of the target‐Rx link, which supports the necessity of multi‐bounce (indirect) paths modelling in target channel. [ABSTRACT FROM AUTHOR]

Realistic appliance power consumption data are essential for developing smart home energy management systems and the foundational algorithms that analyze such data. However, publicly available datasets are scarce and time-consuming to collect. To address this, we propose HYDROSAFE, a hybrid deterministic-probabilistic model designed to generate synthetic appliance power consumption profiles. HYDROSAFE employs the Median Difference Test (MDT) for profile characterization and the Density and Dynamic Time Warping based Spatial Clustering for appliance operation modes (DDTWSC) algorithm to cluster appliance usage according to the corresponding Appliance Operation Modes (AOMs). By integrating stochastic methods, such as white noise, switch-on surge, ripples, and edge position components, the model adds variability and realism to the generated profiles. Evaluation using a normalized DTW-distance matrix shows that HYDROSAFE achieves high fidelity, with an average DTW distance of ten samples at a 1Hz sampling frequency, demonstrating its effectiveness in producing synthetic datasets that closely mimic real-world data. [ABSTRACT FROM AUTHOR]

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device's companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users' privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices. [ABSTRACT FROM AUTHOR]

Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) is making its entrance in people's homes, differences in the skills to operate smart home devices need to be considered. This study examined (1) the levels of digital skills to use IoT home automation among Dutch adult citizens and (2) differences of these skills over gender, age, and education. Therefore, a performance test with actual real-life tasks was conducted among a representative sample (N = 99) of the Dutch adult population to measure digital skill levels. The participants performed tasks while using interconnected smart home devices in a virtual test environment. The results revealed that the Dutch adult population possesses insufficient data and strategic skills to use smart home devices to its full potential. Even less likely to benefit are the elderly and less educated; they showed the lowest levels of data and strategic skills. In addition, the elderly lack operational skills to use IoT home automation beneficially. [ABSTRACT FROM AUTHOR]

Deep learning and the Internet of Things (IoT) are rapidly advancing technologies with wide-ranging applications, notably in areas such as face detection. There is a growing need for sophisticated touchless authentication systems within this domain. While numerous security methods are available, many of them suffer from various shortcomings, including issues like forgotten passwords and the potential transmission of diseases through touch-based authentication techniques. This paper proposes IoT-based intelligent control face detection systems that leverage deep learning (DL) models. These systems are designed to significantly enhance security without relying on manually crafted rules. Face detection plays a pivotal role in safeguarding society, identifying wrongdoers, and bolstering community safety. However, challenges persist, particularly in achieving high accuracy with face recognition control systems in uncontrolled environments and real-time scenarios, such as intersections. In response to these challenges, our paper introduces a novel model that harnesses a combination of deep convolutional neural networks (CNNs) with self-attention mechanisms. Our experimental results demonstrate that this model can rapidly detect whole-scale images in a single forward pass. Notably, our proposed method achieved an outstanding accuracy rate of 99.7%. In comparison to existing state-of-the-art methods, our approach exhibits superior efficiency. This work showcases how the integration of IoT and DL, particularly with the use of a CNN with self-attention, outperforms other CNN-based approaches in terms of both speed and accuracy. [ABSTRACT FROM AUTHOR]

Human exploration of Mars and beyond will demand unprecedented levels of self-sufficiency due to the exceedingly far distances from Earth and lengthy mission durations. As such, astronaut crews must be able to function in a progressively autonomous manner, as they will be unable to rely on timely logistical resupply and/or operational guidance from ground-based mission support teams. One means of increasing autonomy is by incorporating intelligent (i.e., smart) technologies into the mission architecture and habitat design. As plans for sending humans to deep space continue to develop and mature, identifying current and emerging smart technologies and which have the potential to enable these missions or maintain habitability for the crew can be used to explore design options prioritized for autonomous operations. This paper surveys select commercial-off-the-shelf (COTS) technologies currently available and marketed terrestrially for use in integrated smart homes and aligns them for consideration by their functional relevance toward potential Smart Habitat (SmartHab) applications in deep-space. • Self-sufficiency becomes increasingly necessary as distance from Earth prohibits timely communication and resupply capability. • Habitability affects a crew's physical, physiological, psychological, and social health, as well as human performance. • Self-sufficiency and habitability for deep space missions may benefit from utilizing 'smart systems'to enable autonomy. • Over 90 smart technologies were reviewed resulting in 97 space-relevant applications for deep space missions. [ABSTRACT FROM AUTHOR]

The development of smart homes, equipped with devices connected to the Internet of Things (IoT), has opened up new possibilities to monitor and control energy consumption. In this context, non-intrusive load monitoring (NILM) techniques have emerged as a promising solution for the disaggregation of total energy consumption into the consumption of individual appliances. The classification of electrical appliances in a smart home remains a challenging task for machine learning algorithms. In the present study, we propose comparing and evaluating the performance of two different algorithms, namely Multi-Label K-Nearest Neighbors (MLkNN) and Convolutional Neural Networks (CNN), for NILM in two different scenarios: without and with data augmentation (DAUG). Our results show how the classification results can be better interpreted by generating a scalogram image from the power consumption signal data and processing it with CNNs. The results indicate that the CNN model with the proposed data augmentation performed significantly higher, obtaining a mean F1-score of 0.484 (an improvement of + 0.234), better than the other methods. Additionally, after performing the Friedman statistical test, it indicates that it is significantly different from the other methods compared. Our proposed system can potentially reduce energy waste and promote more sustainable energy use in homes and buildings by providing personalized feedback and energy savings tips. [ABSTRACT FROM AUTHOR]

The application and use cases for conversational agents (CAs) are versatile. Smart speakers such as Alexa and Google Home are used in smart home environments, digital agents are integrated into car systems and chatbots are increasingly used in customer service processes. However, human–computer interaction researchers identify and investigate a wide-ranging variety of aspects impeding the usage of CAs by end-users. In general, impediments differ depending on use case contexts, user group characteristics and the CA's technological infrastructure. Hence, it is difficult and often ambiguous for designers and developers to generate an appropriate awareness about aspects impeding CA usage. We address this problem, by conducting a systematic review of 65 publications surveying impeding aspects of the usage of CAs. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) is a crucial task in numerous applications, including healthcare, smart homes, security, and fitness tracking. This study explores the effectiveness of Long Short-Term Memory (LSTM) networks in accurately recognizing and classifying human activities from sensor data. Leveraging the ability of LSTM to capture temporal dependencies and long-term patterns, we employ a deep learning approach that processes sequential data collected from accelerometers and gyroscopes. Our proposed model demonstrates significant improvements in recognition accuracy. We validate the performance of our approach on benchmark datasets, achieving an accuracy of over 95%. The findings underscore the potential of LSTM networks in advancing HAR systems, offering reliable and precise activity classification that can be integrated into various real-world applications. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence, the home care environment for elderly users is becoming increasingly intelligent and systematic. The context aware human–computer interaction technology of sustainable aging-friendly smart homes can effectively identify user needs, enhance energy efficiency, and optimize resource utilization, thereby improving the convenience and sustainability of smart home care services. This paper reviews literature and analyzes cases to summarize the background and current state of context-aware interaction experience research in aging-friendly smart homes. Targeting solitary elderly users aged 60–74, the study involves field observations and user interviews to analyze their characteristics and needs, and to summarize the interaction design principles for aging-friendly smart homes. We explore processes for context-aware and methods for identifying user behaviors, emphasizing the integration of green, eco-friendly, and energy-saving principles in the design process. Focusing on the living experience and quality of life for elderly users living alone, this paper constructs a context-aware user experience model based on multimodal interaction technology. Using elderly falls as a case example, we design typical scenarios for aging-friendly smart homes from the perspectives of equipment layout and innovative hardware and software design. The goal is to optimize the home care experience for elderly users, providing theoretical and practical guidance for smart home services in an aging society. Ultimately, the study aims to develop safer, more convenient, and sustainable home care solutions. [ABSTRACT FROM AUTHOR]

Flexible temperature sensors have diverse applications and a great potential in the field of temperature monitoring, including healthcare, smart homes and the automotive industry. However, the current flexible temperature sensor preparation generally suffers from process complexity, which limits its development and application. In this paper, a nickel oxide (NiO) flexible temperature sensor based on a low-temperature sintering technology is introduced. The prepared NiO flexible temperature sensor has a high-resolution temperature measurement performance and good stability, including temperature detection over a wide temperature range of (25 to 70 °C) and a high sensitivity performance (of a maximum TCR of −5.194%°C−1 and a thermal constant of 3938 K). The rapid response time of this temperature sensor was measured to be 2 s at 27–50 °C, which ensures the accuracy and reliability of the measurement. The NiO flexible temperature sensor prepared by electrohydrodynamic direct writing has a stable performance and good flexibility in complex environments. The temperature sensor can be used to monitor the temperature status of the equipment and prevent failure or damage caused by overheating. [ABSTRACT FROM AUTHOR]

In the landscape of modern smart homes, the prevalence of intelligent devices, notably smart televisions (TVs), has surged, emphasizing the need for sophisticated content recommendation systems. However, the automatic provision of personalized content recommendations for smart TV users remains an underexplored domain. Existing literature has delved into recommendation systems across diverse applications, yet a distinctive void exists in addressing the intricate challenges specific to smart TV users, particularly the incorporation of the smart TV camera module for user image capture and validation. This research introduces a pioneering Intelligent Recommendation System for smart TV users, incorporating a novel Convolutional Neural Tensor Network (CNTN) model. The implementation of this innovative approach involves training the CNN algorithm on two distinct datasets "CelebFaces Attribute Dataset" and "Labeled Faces in the Wild-People" for proficient feature extraction and precise human face detection. The trained CNTN model processes user images captured through the smart TV camera module, matching them against a 'synthetic dataset.' Exploiting this matching process, a hybrid filtering technique is proposed and applied, seamlessly facilitating the personalized recommendation of programs. The proposed CNTN algorithm demonstrates an impressive training performance, achieving approximately 97.18%. Moreover, the hybrid filtering technique produces commendable results, attaining an approximate recommendation accuracy of 89% for single-user scenarios and 86% for multi-user scenarios. These findings underscore the superior efficacy of the hybrid filtering approach compared to conventional content-based and collaborative filtering techniques. The integration of the CNTN architecture and the hybrid filtering methodology collectively contributes to the development of an advanced and effective recommendation system tailored to the nuanced preferences of smart TV users in the context of grid data analysis. [ABSTRACT FROM AUTHOR]

This paper presents a latent diffusion model and convolutional recurrent neural network for detecting sound event, fusing advantages of different networks together to advance security applications and smart home systems. The proposed approach underwent initial training using extensive datasets and subsequently applied transfer learning to adapt to the desired task to effectively mitigate the challenge of limited data availability. It employs the latent diffusion model to get a discrete representation that is compressed from the mel-spectrogram of audio. Subsequently a convolutional neural network (CNN) is linked as the front-end of recurrent neural network (RNN) which produces a feature map. After that, an attention module predicts attention maps in temporal-spectral dimensions level, from the feature map. The input spectrogram is subsequently multiplied with the generated attention maps for adaptive feature refinement. Finally, trainable scalar weights aggregate the fine-tuned features from the back-end RNN. The experimental findings show that the proposed method performs better compared to the state-of-art using three datasets: the DCASE2016-SED, DCASE2017-SED and URBAN-SED. In experiments on the first dataset, DCASE2016-SED, the performance of the approach reached a peak in F1 of 66.2% and ER of 0.42. Using the second dataset, DCASE2017-SED, the results indicate that the F1 and ER achieved 68.1% and 0.40, respectively. Further investigation with the third dataset, URBAN-SED, demonstrates that our proposed approach significantly outperforms existing alternatives as 74.3% and 0.44 for the F1 and ER. [ABSTRACT FROM AUTHOR]

The rapid expansion of Cyber-Physical Systems (CPS) is crucial for enhancing connectivity in the smart world. Encompassing smart homes, cities, agriculture, and healthcare, the broad application environment of CPS demands robust security due to diverse devices, communication protocols, and dispersed nodes. In this multi-domain landscape, ensuring authenticity becomes paramount, leading to the introduction the Secured Lightweight Authentication and Key Agreement for Cyber-Physical System (SLAKA_CPS) protocol. This protocol facilitates authentication across heterogeneous CPS devices in a resource-constrained manner, addressing communication and security concerns. Comparative analysis, including computational complexity and communication cost, reveals that SLAKA_CPS outperforms existing systems with a reduction of 11% in computing complexity, 24% in communication, and 50% in storage costs. Formal verification processes such as AVISPA, BAN logic, and ROR model reinforced the effectiveness of the proposed protocol. [ABSTRACT FROM AUTHOR]

The home environment is where humans spend a significant amount of their time engaging in meaningful activities. It goes beyond being a mere physical structure, actively influencing the well-being and mental health of its occupants. However, the field of housing faces significant challenges in providing responsive living conditions. Often, the focus is on standardized and non-technology-based solutions with predetermined configurations, which may not fully meet the diverse needs of residents, prompting them to actively seek homes aligned with their preferences. This study introduces smart solutions utilizing sensing technology to achieve adaptive housing that optimizes the residents' quality of life and creates user-centric living spaces that are responsive to their psychological needs. A questionnaire was conducted to evaluate the acceptance and willingness of potential smart home users, and descriptive analysis was applied to analyze the gathered data. The findings reveal a level of unawareness and doubt among respondents regarding the ability of smart systems to understand their psychological needs. Additionally, cost emerges as a significant factor influencing their willingness to adopt these technologies, yet responses demonstrate a general interest in presented home automation solutions. Overall, this research highlights the need for a robust framework to guide the future development of smart homes which prioritize the psychological well-being, physical comfort, and user-friendliness of the residents. By addressing these fundamental aspects, housing can undergo a profound transformation, evolving into spaces that greatly enrich individuals' lives and promote their overall well-being. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) facilitates connectivity in businesses and smart homes by integrating embedded technology, wireless sensor networks and data aggregation. Regular monitoring of energy usage in IoT networks is crucial due to the high energy consumption and delays in transmitting data to the Base Station (BS) by the sensor nodes. The most significant challenges in IoT include energy depletion and transmission delays. In this research, the proposed Differential Evolution with Sailfish Optimization (DESFO) model addresses large network handling, achieves maximum convergence rates, and reduces energy consumption. The Differential Evolution (DE) mutation and crossover operators enhance exploration capabilities, while SFO adaptive movement strategies improve the exploitation of the search space. Together, they achieve high convergence rates, prevent falling into local optima, provide iterative control and manage high-dimensional networks effectively. The DESFO method exhibits superior performance when compared to the existing methods, Firefly Optimization and Aquila Optimization (FF-AO), Fixed-Parameter Tractable Approximation Clustering (FPTAC), and Cluster based Reliable Data Aggregation-Sunflower Optimization (CRDA-SFO). The proposed DESFO method yields impressive results, achieving a Packet Delivery Ratio (PDR) of 96.12% at 250 nodes, a Delay of 3ms at 250node, Energy consumption of 12J at 250 respectively. [ABSTRACT FROM AUTHOR]

This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion matrices, showed the model's proficiency in distinguishing between normal activities and falls. This study contributes to the advancement of smart home safety, presenting a robust framework for real-time anomaly monitoring. [ABSTRACT FROM AUTHOR]