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Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs. [ABSTRACT FROM AUTHOR]

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios. [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]

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]

Human activity recognition (HAR) serves as a fundamental component for the various applications of smart homes. However, although existing methods can extract temporal features of sensor event sequences, they do not consider the activation time, location specificity, and long-term dependence of sensor events. This article proposes a new HAR approach based on RGB activity images and a deep convolutional attention network (DCAN). First, the raw data is segmented according to the activity labels after preprocessing. Subsequently, a sliding window is utilized to partition the segmented activity instances into several fixed-length parts. Next, the windowed part is converted into an RGB activity image, where the x-coordinate represents the event sequence, the y-coordinate represents the sensor pattern, and the pixel value is a triplet composed of the event's start time, end time, and duration. Then DCAN is used to extract features and classify these RGB activity images. We test the proposed method on the Aruba dataset and the weighted F1 scores of 10-fold cross-validation for 10 and 8 activities are 0.953 and 0.992, respectively, indicating that our proposed RGB activity image effectively improves classification and our DCAN classifier outperforms existing methods. [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]

Situated within the broader Social Internet of Things (SIoT) paradigm, where objects autonomously communicate, learn, and establish social relationships (Machine 2 Machine and Machine 2 Human), this study investigates via Partial Least Square Structural Equation Modelling (PLS-SEM) the social awareness processes at hand and establishes the drivers of consumers' perceived value of SIoT based online communities (OCs). We leverage the case of smart kitchen robot appliances, such as Cookeo by Moulinex, and a sample of female respondents (n = 335) from France who all own and use such SIoT kitchen robot devices. The model demonstrates that the perceived value of SIoT based OCs stems from three SIoT community related activities, mutual aid, participation, and enjoyment, that are respectively driven by three motivational factors, including the willingness to co-produce, the willingness to connect to others, and the willingness to learn from the community. The vibrant exchanges in these cooking OCs reveal evolving relationships' configuration and demands among participants integrating SIoT technology reach in the meaning and historical specificity of cooking as a cultural practice. Our contributions relate to the identification of consumers' perceived value of SIoT based OCs drivers and to the critical debate about gender in SIoT technology and services development. [ABSTRACT FROM AUTHOR]

This paper focused ona smart, cloud-enabled internet of things system that optimizes home energy distribution, safety, and consumption. The materials used in this paper include Servo motor, lamp holder, energy bulbs, 13A socket, rectifier, jumper wires, electric cables, Blynk interface, motion sensor, temperature/humidity sensor, Esp32, and smoker sensor. The development of the system's code, which was essential for optimizing the system's operations, was carried out using the Arduino Integrated Development Environment. A testbed comprising of the designed work was done in house located in Anambra State, Nigeria over a six-month period, where eight appliances were used to gather used energy by deploying smart plugs, and a smart meter that measures the major energy load of the house. Results show that both implementations collected, stored, and controlled the energy according to the smart, cloud-enabled internet of things system. This work showed that energy providers, and technology developers implement a cloud-enabled Internet of Things system for the best possible household energy usage, safety, and distribution. This technology can significantly increase energy efficiency, save costs, and improve home safety to 100%. [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]

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]

AbstractThe study focuses on analyzing the design characteristics of display and control interface navigation in smart home terminals for users of different ages. By examining the design features of the current typical smart home terminal display and control interface navigation, interface navigation design is the research object. Experimental research is conducted with young adult group (n = 15) and middle-aged to elderly group (n = 15) participants to identify the optimal interface navigation design through specific behavioral indicators, eye movement indicators, and subjective indicators. The results show that different interface layouts impact task completion performance. Middle-aged and older individuals perform better with multi-level highlighting operations. Both age groups prefer interface layouts featuring less-level dual navigation, specifically the left-side type, and multi-level highlighting. These results can improve the versatility of smart home interface design, expand the user base, and provide information for developing smart products. [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]

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]

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]

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]

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures. [ABSTRACT FROM AUTHOR]

By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO). [ABSTRACT FROM AUTHOR]

We propose a framework for predicting sensor event sequences (SES) in smart homes, which can proactively support residents' activities and alert them if activities are not completed as intended. We leverage ongoing activity recognition to enhance the prediction performance, employing a GPT2-based model typically used for sentence generation. We hypothesize that the relationship between ongoing activities and SES patterns is akin to the relationship between topics and word sequence patterns in natural language processing (NLP), enabling us to apply the GPT2-based model to SES prediction. We empirically evaluated our method using two real-world datasets in which residents performed their usual daily activities. Our experimental results demonstrates that the use of the GPT2-based model significantly improves the F1 value of SES prediction from 0.461 to 0.708 compared to the state-of-the-art method, and that leveraging knowledge on ongoing activity can further improve performance to 0.837. Achieving these SES predictions using the ongoing activity recognition model required simple feature engineering and modeling, yielding a performance rate of approximately 80%. [ABSTRACT FROM AUTHOR]

Abstract Smart home power hardware makes it possible to collect a large number of measurements from the distribution grid with low latency. However, the measurements are imprecise, and not every node is instrumented. Therefore, the measured data must be corrected and augmented with pseudo-measurements to obtain an accurate and complete picture of the distribution grid. Hence, we present and evaluate a novel method for distribution grid monitoring. This method uses smart plugs as measuring devices and a feature propagation algorithm to generate pseudo-measurements for each grid node. The feature propagation algorithm exploits the homophily of buses in the distribution grid and diffuses known voltage values throughout the grid. This novel approach to deriving pseudo-measurement values is evaluated using a simulation of SimBench benchmark grids and the IEEE 37 bus system. In comparison to the established GINN algorithm, the presented approach generates more accurate voltage pseudo-measurements with less computational effort. This enables frequent updates of the distribution grid monitoring with low latency whenever a measurement occurs.

The Internet of Things (IoT) presents immense opportunities for driving Industry 4.0 forward. However, in scenarios involving networked control automation, ensuring high reliability and predictable latency is vital for timely responses. To meet these demands, the contemporary wireless protocol time-slotted channel hopping (TSCH), also referred to as IEEE 802.15.4-2015, relies on precise transmission schedules to prevent collisions and achieve consistent end-to-end traffic flow. In the realm of diverse IoT applications, this study introduces a new traffic-aware autonomous multi-objective scheduling function called OPTIMAOrchestra. This function integrates slotframe and channel management, adapts to varying network sizes, supports mobility, and reduces collision risks. The effectiveness of two versions of OPTIMAOrchestra is extensively evaluated through multi-run experiments, each spanning up to 3600 s. It involves networks ranging from small-scale setups to large-scale deployments with 111 nodes. Homogeneous and heterogeneous network topologies are considered in static and mobile environments, where the nodes within a network send packets to the server with the same and different application packet intervals. The results demonstrate that OPTIMAOrchestra_ch4 achieves a current consumption of 0.72 mA while maintaining 100% reliability and 0.86 mA with a 100% packet delivery ratio in static networks. Both proposed Orchestra variants in mobile networks achieve 100% reliability, with current consumption recorded at 6.36 mA. Minimum latencies of 0.073 and 0.02 s are observed in static and mobile environments, respectively. On average, a collision rate of 5% is recorded for TSCH and RPL communication, with a minimum of 0% collision rate observed in the TSCH broadcast in mobile networks. Overall, the proposed OPTIMAOrchestra scheduler outperforms existing schedulers regarding network efficiency, time, and usability, significantly improving reliability while maintaining a balanced latency–energy trade-off.

The expansion of smart home devices has revolutionized residential living by offering convenience and improved energy efficiency. However, this also increases potential vulnerabilities. Traditional centralized intrusion detection systems (IDS) are unsuitable for smart homes due to privacy, scalability, and distribution concerns. Consequently, Machine Learning (ML)-based IDSs have emerged as efficient solutions. Nevertheless, conventional ML approaches are centralized, which poses privacy risks and increases communication overhead. To address these issues, this paper proposes a novel fog-federated learning (2FIDS)-based IDS for the smart home ecosystem. The 2FIDS adopts a decentralized architecture, allowing devices to train a detection model while preserving local data privacy collaboratively. Furthermore, it is deployed at the fog layer, enabling efficient and localized anomaly detection. Extensive evaluations using three datasets, the Bot-IoT, TON-IoT, and MQTTset datasets, demonstrate that the 2FIDS model achieves high detection accuracy, consistently exceeding 96% across various attack types in the Bot-IoT and TON-IoT datasets and 86% for MQTTset dataset. It exhibits robust scalability, maintaining performance as fog nodes increase from 5 to 15. The federated learning approach effectively preserves data privacy while improving upon traditional centralized methods in terms of communication efficiency and latency reduction. [ABSTRACT FROM AUTHOR]

The internet of things (IoT) has transformed the way appliances and devices are connected and especially in the case of smart homes, in which smart devices can communicate through networks to improve everyday activities. However, it might be difficult to provide a high level of security for the data produced by these devices. Current security mechanisms might not always function adequately in all circumstances, especially when the number of devices increases. This research proposes an edge IoT-based smart home authentication scheme that adopts IPv6. For devices that use a smartphone application, it also offers a passwordless user authentication approach through the use of the smartphone ID and biometrics. The proposed authentication scheme was simulated to verify its ease of use and security. Security and cost analysis was also performed by reviewing and comparing the proposed scheme with previous research on IoT authentication systems. This research finds that the proposed authentication scheme is efficient at shielding home IoT networks from possible attacks, as well as maintaining a high level of usability. [ABSTRACT FROM AUTHOR]

The majority of the power distribution problems are addressed in this study by outlining a scheduling and allocation system that is based on rules. The smart home environment incorporates the suggested model as the intermediate layer. In a smart home, managing overload and power failure is the primary goal of the suggested max-max-based demand response scheduling method. The proposed model is an extension of the demand response measure while considering the load and failure rate analysis. This model is applied in a real-time environment that processes the historical information of power usage in the environment. This model captures the information available by the resources, centralized database information, and the current request parameters. The control and configuration unit are defined to process the load, history, and demand of the users. In this model, effective resource allocation and scheduling are provided. The proposed model is compared against conventional first come-first serve (FCFS), shortest job first (SJF), longest job first (LJF), demand response, and fuzzybased demand response methods. The comparative evaluation is done on average delay, failure rate, and task-switching parameters. The analysis results obtained against these parameters confirm that the presented maxmax-based parametric demand response scheduling and resource allocation method enhanced the reliability and effectiveness of the smart home environment. [ABSTRACT FROM AUTHOR]

Nowadays, the increased demand for energy and electrification associated with higher production costs from renewable and cleaner sources has driven up prices, impacting the industrial, commercial, and residential sectors. With a direct influence on the development of these economic sectors, its direct and indirect impacts to products and services have become important to find more efficient ways and best practices on energy use to support sustainable development. Aiming to shed light on this topic, and how individuals and society behave in this energy market transformation, this article explores opportunities for reducing electricity consumption through the use of modern technologies, such as of monitoring, optimization, automation, and adjustment of routines. At the same time, it is also our intention to bring to the surface a discussion around the rational use of everyday resources and raising the awareness of its impact to individuals and institutions. At its core, this work consists of continuous data collection of single devices and equipment in regard to status, energy consumption, and other relevant data of a typical household. Through behavioral changes and introduction of smart home automation techniques, it was possible to trace a parallel comparison between different scenarios and their influence on the energy consumption without negative impact to the comfort of individuals. Seeking a continuous improvement approach, extensive iterations were conducted, and it was possible to notice not only an energy efficiency improvement, but at the same time gains in living standards and safety. The significant results observed over subsequent months and years highlight not only practical and financial benefits, but also increased awareness and behavioral changes toward the rational use of electricity in households.

As the Internet of Things (IoT) continues to revolutionize the way we interact with our living spaces, the concept of smart homes has become increasingly prevalent. However, along with the convenience and connectivity offered by IoT-enabled devices in smart homes comes a range of security challenges. This paper explores the landscape of smart-home security. In contrast to similar surveys, this study also examines the particularities of popular categories of smart devices, like home assistants, TVs, AR/VR, locks, sensors, etc. It examines various security threats and vulnerabilities inherent in smart-home ecosystems, including unauthorized access, data breaches, and device tampering. Additionally, the paper discusses existing security mechanisms and protocols designed to mitigate these risks, such as encryption, authentication, and intrusion-detection systems. Furthermore, it highlights the importance of user awareness and education in maintaining the security of smart-home environments. Finally, the paper proposes future research directions and recommendations for enhancing smart-home security with IoT, including the development of robust security best practices and standards, improved device authentication methods, and more effective intrusion-detection techniques. By addressing these challenges, the potential of IoT-enabled smart homes to enhance convenience and efficiency while ensuring privacy, security, and cyber-resilience can be realized. [ABSTRACT FROM AUTHOR]

The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) technology is a revolutionary paradigm that aims to transform physical objects into connected devices that can be accessed ubiquitously. Due to heterogeneous manufacturers and supported infrastructures, IoT technology introduces a wide range of communication protocols. Device interoperability is a significant problem when two or more IoT applications are developed using different application-layer protocols. Conspicuously, a formalization of the system design is proposed using the Model Driven Architecture (MDA) approach for IoT interoperability. Specifically, a comprehensive MDA-based approach is proposed that reduces the processing time and effort to develop IoT interoperable systems. Metamodel, UML profile, and transformation rules are developed to make heterogeneous application-layer protocols interoperable using devices as intermediates. The objective is to elucidate the extent to which various layers of the MDA framework are encompassed. Furthermore, the smart home-based power consumption analysis environment is presented as the application scenario. For validation purposes, the proposed model is simulated using a challenging dataset obtained from an online UCI repository. Results depict that the proposed technique is more effective as compared to state-of-the-art techniques in terms of Temporal Delay (109.68s), Data Classification Accuracy (92.06%), Statistical Efficiency (92.36%), Reliability (86.23%), and Stability (71.23%). [ABSTRACT FROM AUTHOR]

People with disability often require long-term support within the home. Advances in technology have made home automation more readily available to support people living with disability. However, few studies describe the perspectives of people using home automation. The aim of this study was to explore the experience of individuals living with long term serious disability using home automation. A phenomenological approach was adopted. In-depth, semi-structured interviews were conducted. Data were analysed using an inductive approach to identify themes. Two overarching categories of themes were identified: 'benefits' and 'challenges'. Benefits captured the outcomes experienced by people living with disability using home automation and the impact upon their lives. Participants described several challenges with using home automation such as self-advocating to receive home automation, long waiting periods in the assessment and installation process, frustrations when home automation did not work, and the challenges experienced from being without home automation. This research identified the benefits and challenges of home automation experienced by people with long term serious disability. The findings can be used to understand the importance of home automation and the impact it has upon the lives of people living with disability. It is recognised that home automation can have a positive impact upon the lives of people living with disability. Funding for home automation is a complicated process with long waiting times. This process needs to be readdressed in order for people to receive home automation in a timely manner to prevent negative experiences. Home automation within the community could support people living with disability to access the community more. It is important to develop facilities and communities that are accessible and inclusive for people with disabilities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) represents a network framework comprising identifiable entities that interact through the Internet. One of its applications is the smart home, where household devices can be remotely monitored and controlled. This has led to an increased demand for reliable security solutions in IoT systems. Security presents a significant challenge in IoT smart home devices and must be carefully considered. Unauthorized access to a smart home system, facilitated by means such as jamming or replay attacks, could pose risks by manipulating sensors and controls, potentially allowing unauthorized entry. This review paper concentrates specifically on the security and privacy aspects of IoT smart home access control devices. It begins with a concise overview of smart home security and privacy, then delves into various techniques within the smart home system taxonomy, such as authentication, access control, blockchain, and cryptography-based methods. Furthermore, the paper compares the advantages and disadvantages of these techniques. It also examines various types of attacks on smart home IoT access control systems and evaluates risk factors such as methodologies, attack frequency, severity, probability, and ranking. Finally, the paper discusses challenges, applications, conclusions, and future directions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been widely employed in the development of smart locker systems over the last decade. However, some of these systems are based on authentication methods which lack flexibility. Such systems did not consider the possibility that an authentication method could be unavailable for different reasons, namely access card loss, camera or mice break, etc. Moreover, such systems do not consider dual-authentication methods that enhance security. This paper aims to develop a smart locker system that considers several authentication methods including dual authentication (phone number and One Time Password (OTP)), fingerprint, face recognition, and emergency code utilizing IoT technology. Dual authentication method is the considered base authentication method. The system has been fabricated and evaluated taking into account different scenarios including monitoring door status, ensuring access for authorized users, and denying access to unauthorized users. [ABSTRACT FROM AUTHOR]

Depression significantly impacts the wellbeing of older Australians, posing considerable challenges to their overall quality of life. This study aimed to detect in-home movement patterns of participants that could be indicative of depressive states. Utilising data collected over a 12-month period via smart home ambient sensors, this feasibility study conducted a comparative analysis using machine learning techniques on features derived from motion sensors, sociodemographic variables, and the Geriatric Depression Scale. Three machine learning models, specifically Extreme Gradient Boost (XGBoost), Random Forest (RF), and Logistic Regression (LR), were implemented. Results showed that the performance of XGBoost was relatively higher compared to RF and LR, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.67. Feature analysis indicated that bathroom and kitchen movements and the level of home care support were among the top influential features influencing depression assessment. This is consistent with clinical evidence on appetite, hygiene, and overall mobility changes during depression. These findings underscore the feasibility of leveraging in-home movement monitoring as an indicator of health risks among older adults. [ABSTRACT FROM AUTHOR]

In the distributed computing era, cloud computing has completely changed organizational operations by facilitating simple access to resources. However, the rapid development of the IoT has led to collaborative computing, which raises scalability and security challenges. To fully realize the potential of the Internet of Things (IoT) in smart home technologies, there is still a need for strong data security solutions, which are essential in dynamic offloading in conjunction with edge, fog, and cloud computing. This research on smart home challenges covers in-depth examinations of data security, privacy, processing speed, storage capacity restrictions, and analytics inside networked IoT devices. We introduce the Trusted IoT Big Data Analytics (TIBDA) framework as a comprehensive solution to reshape smart living. Our primary focus is mitigating pervasive data security and privacy issues. TIBDA incorporates robust trust mechanisms, prioritizing data privacy and reliability for secure processing and user information confidentiality within the smart home environment. We achieve this by employing a hybrid cryptosystem that combines Elliptic Curve Cryptography (ECC), Post Quantum Cryptography (PQC), and Blockchain technology (BCT) to protect user privacy and confidentiality. Additionally, we comprehensively compared four prominent Artificial Intelligence anomaly detection algorithms (Isolation Forest, Local Outlier Factor, One-Class SVM, and Elliptic Envelope). We utilized machine learning classification algorithms (random forest, k-nearest neighbors, support vector machines, linear discriminant analysis, and quadratic discriminant analysis) for detecting malicious and non-malicious activities in smart home systems. Furthermore, the main part of the research is with the help of an artificial neural network (ANN) dynamic algorithm; the TIBDA framework designs a hybrid computing system that integrates edge, fog, and cloud architecture and efficiently supports numerous users while processing data from IoT devices in real-time. The analysis shows that TIBDA outperforms these systems significantly across various metrics. In terms of response time, TIBDA demonstrated a reduction of 10–20% compared to the other systems under varying user loads, device counts, and transaction volumes. Regarding security, TIBDA's AUC values were consistently higher by 5–15%, indicating superior protection against threats. Additionally, TIBDA exhibited the highest trustworthiness with an uptime percentage 10–12% greater than its competitors. TIBDA's Isolation Forest algorithm achieved an accuracy of 99.30%, and the random forest algorithm achieved an accuracy of 94.70%, outperforming other methods by 8–11%. Furthermore, our ANN-based offloading decision-making model achieved a validation accuracy of 99% and reduced loss to 0.11, demonstrating significant improvements in resource utilization and system performance. [ABSTRACT FROM AUTHOR]

Various smart home companies are adding displays to smart home control devices and are also releasing smart home control functions for devices with displays. Since smart home management devices with displays are multifunctional, they can store more digital evidence than traditional management devices. Therefore, we propose a smart home environment forensic methodology focused on wallpads, which are smart home management devices with displays. And we validate the proposed methodology by building a smart home environment centered around wallpads and conducting tests with three vendors (Samsung, Kocom, and Commax). Following the proposed methodology, we identified the software and hardware specifications of devices within the testbed, particularly the wallpads. Based on this, we were able to extract network packets, disk images, and individual files stored internally using methods such as packet capture, vulnerability exploits, serial ports, and chip-off. Through analysis, we confirmed that significant user-related information and videos are stored in these control devices. The digital evidence obtained through the proposed methodology can be used as critical legal evidence, and this study contributes to efficiently analyzing important security issues and evidential data in various smart home IoT environments. [ABSTRACT FROM AUTHOR]

The design of age-friendly homes benefits vulnerable groups, such as frail people and older adults. Advances in smart home technologies, including robots, have important synergies with homes designed for health needs. Yet, focus on environmental and sustainable housing design and improvements misses important opportunities for collective impact. Stronger involvement of disciplines, such as those from the built environment for technological integration within homes and effects on space and the community, is needed. There is a need for a unified framework integrating the needs and factors of the resident, smart home technologies and robots, and the built environment, and that includes the concept of a "home". With the remodeling of housing towards sustainable and environmental targets, as well as advances in smart home technologies such as robots, the timeliness of shared input for the benefit of residents now and in the future is of the essence. This would help target future research into effective and optimized cohabitation with technology within homes for the purpose of improving the wellbeing of residents. [ABSTRACT FROM AUTHOR]

Nowadays, the increased demand for energy and electrification associated with higher production costs from renewable and cleaner sources has driven up prices, impacting the industrial, commercial, and residential sectors. With a direct influence on the development of these economic sectors, its direct and indirect impacts to products and services have become important to find more efficient ways and best practices on energy use to support sustainable development. Aiming to shed light on this topic, and how individuals and society behave in this energy market transformation, this article explores opportunities for reducing electricity consumption through the use of modern technologies, such as of monitoring, optimization, automation, and adjustment of routines. At the same time, it is also our intention to bring to the surface a discussion around the rational use of everyday resources and raising the awareness of its impact to individuals and institutions. At its core, this work consists of continuous data collection of single devices and equipment in regard to status, energy consumption, and other relevant data of a typical household. Through behavioral changes and introduction of smart home automation techniques, it was possible to trace a parallel comparison between different scenarios and their influence on the energy consumption without negative impact to the comfort of individuals. Seeking a continuous improvement approach, extensive iterations were conducted, and it was possible to notice not only an energy efficiency improvement, but at the same time gains in living standards and safety. The significant results observed over subsequent months and years highlight not only practical and financial benefits, but also increased awareness and behavioral changes toward the rational use of electricity in households. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) research has gained significant momentum due to recent technological advancements, artificial intelligence algorithms, the need for smart cities, and socioeconomic transformation. However, existing computer vision and sensor-based HAR solutions have limitations such as privacy issues, memory and power consumption, and discomfort in wearing sensors for which researchers are observing a paradigm shift in HAR research. In response, WiFi-based HAR is gaining popularity due to the availability of more coarse-grained Channel State Information. However, existing WiFi-based HAR approaches are limited to classifying independent and non-concurrent human activities performed within equal time duration. Recent research commonly utilizes a Single Input Multiple Output communication link with a WiFi signal of 5 GHz channel frequency, using two WiFi routers or two Intel 5300 NICs as transmitter-receiver. Our study, on the other hand, utilizes a Multiple Input Multiple Output radio link between a WiFi router and an Intel 5300 NIC, with the time-series Wi-Fi channel state information based on 2.4 GHz channel frequency for mutual human-to-human concurrent interaction recognition. The proposed Self-Attention guided Bidirectional Gated Recurrent Neural Network (Attention-BiGRU) deep learning model can classify 13 mutual interactions with a maximum benchmark accuracy of 94% for a single subject-pair. This has been expanded for ten subject pairs, which secured a benchmark accuracy of 88% with improved classification around the interaction-transition region. An executable graphical user interface (GUI) software has also been developed in this study using the PyQt5 python module to classify, save, and display the overall mutual concurrent human interactions performed within a given time duration. Finally, this article concludes with a discussion of the possible solutions to the observed limitations and identifies areas for further research. Such a Wi-Fi channel perturbation pattern analysis is believed to be an efficient, economical, and privacy-friendly approach to be potentially utilized in mutual human interaction recognition for indoor activity monitoring, surveillance system, smart health monitoring systems, and independent assisted living. [ABSTRACT FROM AUTHOR]

Smart homes are at the forefront of sustainable living, utilizing advanced monitoring systems to optimize energy consumption. However, these systems frequently encounter issues with anomalous data such as missing data, redundant data, and outliers data which can undermine their effectiveness. In this paper, an artificial neural network (ANN)-based approach for data imputation is specifically designed to deal with the anomalies in smart home energy consumption datasets. Our research harnesses the power of ANNs to model intricate patterns within energy consumption data, enabling the accurate imputation of missing values while detecting and rectifying anomalous data. This approach not only enhances the completeness of the data but also augments its overall quality, ensuring more reliable results. To evaluate the effectiveness of our ANN-based imputation method, comprehensive experiments were conducted using real-world smart home energy consumption datasets. Our findings demonstrate that this approach outperforms traditional imputation techniques like mean imputation and median imputation in terms of accuracy. Furthermore, it showcases adaptability to diverse smart home scenarios and datasets, making it a versatile solution for improving data quality. In conclusion, this study introduces an advanced data imputation technique based on ANNs, tailor-made for addressing anomalies in smart home energy consumption data. Beyond merely filling data gaps, this approach elevates the dataset's reliability and completeness, thereby facilitating a more precise analysis of energy consumption and supporting informed decision-making in the context of smart homes and sustainable energy management. Ultimately, the proposed method has the potential to contribute considerably to the ongoing evolution of smart home technologies and energy conservation efforts. [ABSTRACT FROM AUTHOR]

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AbstractThe benefits obtained from home automation are promising and will become more pronounced as smart home technologies continue to develop. To achieve benefits, users require operational, data, and strategic skills to control and automate smart devices, retrieve and understand collected data, and make informed decisions. These skills were tested by providing assignments in a virtual test environment to 100 Dutch adult participants. The assignments were designed to measure different facets of all skills by using the functions, data, and automations of smart home devices. The results suggest that the Dutch adult population is not sufficiently skilled in using the smart home to its full potential; several skills related problems occurred in the tests. Furthermore, in terms of gender, age, and education, home automation further reinforces existing social-digital inequalities. Thus, earlier digital inequalities will remain present for some time, despite increasing device autonomy. [ABSTRACT FROM AUTHOR]

Intelligent platforms are mainly service platforms composed of machine learning integrations that rely on continuous reasoning and learning. This technology has been widely used in today's the Internet Age, and its analysis system, as an important customer oriented feedback link, is also very important. Therefore, this article would optimize the intelligent platform analysis system by applying virtual digital technology, and use intelligent platforms for ship shipping and smart home platforms as examples. [ABSTRACT FROM AUTHOR]

The Internet of Things provides many useful opportunities and makes everyday life easier. At the same time, when interacting with the Internet of Things, a large amount of confidential information is processed, thereby raising the issue of ensuring security. The presented work researches the process of ensuring the security of smart homes by analyzing network traffic to detect malware, which uses machine learning methods Gradient Descent, K-Nearest Neighbor, Recurrent Neural Networks. The research was conducted using the IoT-23 intrusion detection dataset. To evaluate the methods, the metrics accuracy, recall, precision, and F1 were used. As a result, Gradient Descent achieved the highest performance and can be used for intrusion detection. [ABSTRACT FROM AUTHOR]

This paper investigates the challenge of finding and analyzing security vulnerabilities among widely available low-budget Internet of Things smart home appliances. It considers the identification of security vulnerabilities within the appliances' embedded software and connectivity functions over wired and wireless channels in local networks and external communications with manufacturers' cloud services. To analyze the security of these appliances, a universal laboratory test bench is proposed and a set of methodologies for testing the security of smart home devices is described. The proposed testing platform offers a practical solution for security analysis of Internet of Things smart home devices and it can serve as a reference approach for future research. The results from the research indicated varying levels of susceptibility across different types of devices. A list of recommendations for manufacturers and others to improve the security level of these appliances is provided. The findings emphasize the need for regular security assessments of smart home devices, to maintain the protection of personal and sensitive information. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data. [ABSTRACT FROM AUTHOR]

This article presents two monopole antennas with an endfire radiation pattern in the UHF band that can be installed on dry walls or metallic cabinets as a part of wireless sensor nodes, making them a suitable choice for smart home applications, such as the wireless remote control of house appliances. Two different antennas are proposed to cover the RFID bands of North America (902–928 MHz) and worldwide (860–960 MHz). The antennas have wide horizontal radiation patterns that provide great reading coverage in their communication with a base station placed at a certain distance from the antennas. The structures have two ground planes, one in-plane and the other vertical. The vertical ground helps the antenna to have a directive radiation and also makes it easily installed on walls. The antenna feeding line lies over the vertical ground substrate. The maximum dimensions of the narrow-band antenna are L × W = 0.3 λ   × 0.14 λ , and those for the wide-band antenna are L × W = 0.39 λ   × 0.14 λ. The measured results show that the bandwidth of the proposed antennas for the North America and worldwide RFID bands are from 902 MHz to 939 MHz and 822 MHz to 961 MHz, with maximum gains of 4.2 dBi and 4.9 dBi, respectively. [ABSTRACT FROM AUTHOR]