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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]

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]

Smart Home Technologies (SHTs) have recently become popular for a variety of purposes, including healthcare, entertainment, and convenience, among others. While SHT manufacturers promise to provide a range of services relating to home security, health and wellness, automated domestic tasks, entertainment, and beyond, user perceptions vary widely in terms of benefits and drawbacks. Moreover, surveillance studies researchers have warned against normalizing technologies that may exacerbate uneven power dynamics between users and household members, marketing companies, insurance brokers, law enforcement, and others. Through an analysis of the interdisciplinary literature stemming from computer science and engineering, gerontology, the social sciences, and related fields, we explore the extent to which these potential risks and related concerns are reflected upon by smart home users. This scoping review aims to explore SHT user perceptions of privacy attitudes, the purposes of smart home surveillance, risks and benefits, and impacts on home safety. Through our review of sixty-eight relevant studies, we found that many smart home users reported satisfaction over perceived benefits such as an increased sense of safety and home security. Many others displayed limited understandings of data collection practices or expressed privacy concerns. Nonetheless, SHT usage prevailed among these users. Others report a perceived trade-off between privacy and other factors, such as convenience, and some may have resorted to privacy cynicism, a coping mechanism for dealing with ubiquitous surveillance. In order to better understand SHT adoption trends despite concerns, exploring the conflict between user perceptions of privacy, understanding of SHT data collection purposes, risks and benefits, and home safety, is essential. [ABSTRACT FROM AUTHOR]

With the popularization of intelligent devices and the development of internet technology, the Internet of Things technology is receiving increasing attention. However, how to effectively schedule and manage resources in the Internet of Things environment has become an increasingly important issue. In order to solve this problem, a cloud scheduling simulation system for IoT resources in a digital intelligent environment has emerged. This system utilizes cloud computing and IoT technology to simulate the behavior of intelligent devices and deep learning technology, achieving efficient resource scheduling. In addition, experimental data shows that the accuracy of the IoT resource cloud scheduling simulation system based on deep learning algorithm combined with digital intelligent environment is over 70%; the average task processing time is within 0.3 seconds; the average task response time is within 0.6 seconds. From this, it can be seen that combining deep learning algorithms with the system can quickly respond and solve problems, improving the reliability and stability of the system. The application range of this system is very wide, which can be used to optimize resource scheduling in many fields such as production, and can also be applied in fields such as smart home, smart medical, and intelligent transportation. With the continuous development of technology, this system would also be further improved and expanded to better serve people's lives and work. [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]

Access control (AC) in IoT (Internet of Things) systems presents significant challenges. These systems are evolving, heterogeneous, and dynamic, combined with the lack of appropriate tools to specify and design Access Control Policies (ACP). Despite various proposals, complexity persists, especially in restrictive environments like IoT. In this perspective, Higher-Order Attribute-Based Access Control (HoBAC) was proposed as a general AC model that extends Attribute Based AC (ABAC). It allows the design of flexible AC models and policies applicable to IoT and non-IoT systems. The work presented in this paper focuses on addressing the need for tools to support the adoption of HoBAC for IoT systems by proposing HoBACDSL, a Domain Specific Language (DSL). HoBACDSL abstracts the complexities of HoBAC and makes its concepts more accessible. We illustrate how this DSL is concretely used to specify, and generate AC policies in the Extensible Access Control Markup Language (XACML) standard for a Smart Home use case. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices in smart homes facilitates and the increase of financial transactions like reordering groceries by a smart refrigerator but also introduces significant cybersecurity risks. This paper examines the inherent vulnerabilities of IoT devices, including weak authentication, rare software updates, and susceptibility to malware. These vulnerabilities can expose smart homes to unauthorized access, leading to unauthorized money transfers and identity theft. Additionally, as the IoT ecosystem is interconnected, the risk increases, as a compromised device becomes an entry point for a broader network breach. The lack of standardized security protocols and regulatory frameworks further exacerbates this challenge. Therefore, addressing these risks requires collaboration among manufacturers, regulatory authorities, and users to establish robust security standards. In this way, the security of financial transactions using smart homes can be enhanced, and potential threats can be mitigated. [ABSTRACT FROM AUTHOR]

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users' preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context-aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems' awareness of their users' context. [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]

Access control (AC) in IoT (Internet of Things) systems presents significant challenges. These systems are evolving, heterogeneous, and dynamic, combined with the lack of appropriate tools to specify and design Access Control Policies (ACP). Despite various proposals, complexity persists, especially in restrictive environments like IoT. In this perspective, Higher-Order Attribute-Based Access Control (HoBAC) was proposed as a general AC model that extends Attribute Based AC (ABAC). It allows the design of flexible AC models and policies applicable to IoT and non-IoT systems. The work presented in this paper focuses on addressing the need for tools to support the adoption of HoBAC for IoT systems by proposing HoBACDSL, a Domain Specific Language (DSL). HoBACDSL abstracts the complexities of HoBAC and makes its concepts more accessible. We illustrate how this DSL is concretely used to specify, and generate AC policies in the Extensible Access Control Markup Language (XACML) standard for a Smart Home use case. [ABSTRACT FROM AUTHOR]

While sales of smart home technology are increasing, some are still hesitant to use such products. These differences in smart home technology acceptance could potentially be explained with the extended Unified theory of acceptance and use of technology (UTAUT2). However, the explanatory power of UTAUT2 in this context is still relatively unclear, and additional extensions, such as the inclusion of trust, have been proposed recently. In this systematic review, we address this issue by synthetizing evidence from 32 articles dealing with the relationship between the UTAUT2 components, trust, and smart home technology acceptance. Our results reveal that the UTAUT2 components and trust are all consistently correlated with behavioral intentions. In contrast, multivariate results show that only performance expectancy, hedonic motivation, and price value are consistent predictors of technology acceptance. In the discussion, we outline possible explanations for such results and highlight the limitations of our review.. [ABSTRACT FROM AUTHOR]

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future. [ABSTRACT FROM AUTHOR]

This paper proposes a Distributed Software Architecture (DSA) for Smart Building (SB) based on the Reactive Manifesto (RM) principles. To follow the RM principles, we analyze the usage of different deployment approaches, particularly the impact of using a container orchestrator on the application layer. After running performance tests on the different configurations, the container orchestrator usage led to enhanced distributed processing, lowering the latency, increasing flexibility, enhancing security, and providing cost-effectiveness and scalability. We introduce the implementation of a modern DSA, developed following state-of-the-art cloud patterns and compliant with the RM for the SB context. Furthermore, we have ensured the reproducibility of this implementation by making the initial tests and overall architecture code available in public repositories. The research follows the Design Science Research (DSR) methodology for elaborating each phase until we get the artifact (DSA) and, with this, contribute to the Knowledge Base. The architecture was properly tested, considering the performance as the principal test layer. This solution is tailored for application in domains of the Internet of Things (IoT), focusing on the SB and a case study involving the Laboratory of Distributed Systems and Concurrent Programming (LaSDPC) at São Paulo University. Moreover, its applicability extends to IoT domains like smart home, smart campus, smart city, and health-related applications. [ABSTRACT FROM AUTHOR]

The synergy of Edge Computing, Artificial Intelligence, and Internet of Things in smart city camera-based surveillance offers notable advantages in resource allocation. Traditional approaches involving constant video data streaming to central servers incur significant bandwidth and storage costs. Our contribution involves proposing a cost-effective, lightweight Edge AI Enabled IoT Framework for Secure Smart Home Infrastructure, utilising a Raspberry Pi single-board computer and the open-source software motion for camera surveillance. The motion program monitors video signals from various cameras and triggers specific actions upon detecting movement. The framework efficiently notifies the smart home owner via email and smartphone message when motion is detected. We integrated four advanced motion detection and alert notification methodologies, conducting a thorough evaluation that positioned our framework as superior to existing solutions. Our research showcases impressive accuracy rates of 91% and 85% in indoor and outdoor scenarios, with minimal average delays of 12.8 seconds for email alerts and 1.6 seconds for messages which is approximately 41.6 % less than the state of the art methodologies. This innovative integration not only elevates surveillance capabilities but also establishes a swift and reliable alert system, contributing significantly to the efficiency and security of smart home environments. [ABSTRACT FROM AUTHOR]

The synergy of Edge Computing, Artificial Intelligence, and Internet of Things in smart city camera-based surveillance offers notable advantages in resource allocation. Traditional approaches involving constant video data streaming to central servers incur significant bandwidth and storage costs. Our contribution involves proposing a cost-effective, lightweight Edge AI Enabled IoT Framework for Secure Smart Home Infrastructure, utilising a Raspberry Pi single-board computer and the open-source software motion for camera surveillance. The motion program monitors video signals from various cameras and triggers specific actions upon detecting movement. The framework efficiently notifies the smart home owner via email and smartphone message when motion is detected. We integrated four advanced motion detection and alert notification methodologies, conducting a thorough evaluation that positioned our framework as superior to existing solutions. Our research showcases impressive accuracy rates of 91% and 85% in indoor and outdoor scenarios, with minimal average delays of 12.8 seconds for email alerts and 1.6 seconds for messages which is approximately 41.6 % less than the state of the art methodologies. This innovative integration not only elevates surveillance capabilities but also establishes a swift and reliable alert system, contributing significantly to the efficiency and security of smart home environments. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a set of devices that contain electronic software, sensors, and actuators and exchange information using the Internet or network. Because of multiple applications, the number of IoT devices and sensors is increasing daily. Therefore, the traditional addressing security protocols cannot guarantee performance in all scenarios. The most crucial factor is the requirement of a unique addressing and secure scheme that addresses each smart IoT device or application and ensures secure communication. Therefore, robust and lightweight authentication protocols are crucial to ensuring security in smart home IoT (SH-IoT) networks. The proposed scheme provides a remote authentication protocol for the secure exchange of information between the user and the smart home IoT network. Informal and formal security analyses using the ROR model are presented in this work. The presented scheme is validated using the AVISPA tool. [ABSTRACT FROM AUTHOR]

Smart homes have numerous remotely controlled IoT devices that make life more convenient but also pose security and privacy risks. Understanding the three categories of challenges, vulnerabilities, and countermeasures is crucial in securing IoT devices for smart homes. The study identified potential security risks and vulnerabilities associated with IoT devices in smart homes. It also proposed mitigation strategies to enhance the safety and security of smart homeowners. The findings provided a clear insight into the challenges related to IoT devices, which could be helpful for beginners to this technology. The research provides extensive knowledge of cybersecurity awareness in IoT devices for new smart home users and recommends appropriate countermeasures. The research focused on the impact of the latest technologies on IoT devices, making it relevant for anyone interested in this topic. For this study, the research employed a grounded theory approach, specifically using the grounded theory methodology. Theories were developed based on data analysis that emerged from questionnaires and focus groups using qualitative methodology. The overall theory focused on the effectiveness of the software, which depends on the user's ability to understand how authentication protects data and defends against adversaries. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive methodology for precisely identifying smart home devices through the intricate analysis of network traffic data. The research framework comprises a multifaceted approach, encompassing critical stages such as in-depth exploratory data analysis, sophisticated feature engineering, robust machine learning modeling, iterative model refinement, and meticulous interpretation of results. The study evaluates various machine learning algorithms, with the Random Forest Classifier emerging as the optimal choice due to its superior performance in device recognition. Subsequently, the Random Forest Classifier's effectiveness is substantially bolstered through an exhaustive hyperparameter tuning procedure. This optimization effort culminates in a final accuracy score of 87.79%, demonstrating the excellent efficacy of this approach in precisely identifying smart home devices based on intricate network traffic patterns. Future research endeavors may concentrate on further refining these models and exploring additional feature sets to comprehensively augment their capabilities, thereby addressing the constantly evolving landscape of smart home technology and its associated security concerns. [ABSTRACT FROM AUTHOR]

Wireless sensor-based smart home systems are increasingly gaining prevalence, offering new opportunities for automation and an improved quality of life. However, the seamless integration of multi-layer wireless sensor networks remains a significant challenge. This article delves into a comprehensive approach to integrate wireless networks that employ distinct protocols, such as Zigbee, Z-Wave, Wi-Fi, Bluetooth, and LoRa, within the context of Home Assistant-based smart homes. The article explores architectural solutions and data transformation methods, and presents the outcomes of performance experiments, as well as the stability of multi-layered wireless sensor networks. These findings culminate in the identification of an optimal configuration. The formulation of this research problem sets the stage for novel perspectives in the development of efficient and adaptive smart homes. [ABSTRACT FROM AUTHOR]

Over the past decades, the evolution of new wireless technology has led to increased attention toward Smart Home Automation Systems (SHAS). In the smart home, numerous smart devices are interconnected with the proliferation of the Internet of Things (IoT) technology to provide users with a more comfortable lifestyle. Prior research on the smart home system has enacted machine learning and deep learning techniques to forecast the consecutive activities in the smart home. This research paper aims to enhance the future decisionmaking of energy consumption with the assistance of environmental factors and home appliances by exploiting the Tensorflow-based deep ensemble learning technique. The enhancement of future decisionmaking in smart home automation systems primarily involves the classification of energy consumption levels from the knowledge of external environmental factors and energy consumption levels of home appliances through the phases of data preprocessing, feature selection, fuzzy logic-based data labeling, and finally, classification of energy consumption using TensorFlow based deep ensemble learning technique. The data obtained from the effective feature selection technique is subjected to labeling via the fuzzy logic system to classify the energy consumption of smart home appliances. Finally, this work classifies the level of energy consumption based on the labeled knowledge of smart home data using a tensorflow-based deep ensemble learning model. The experimental model implements the proposed deep ensemble learning model in the tensorflow framework, which improves the decision-making performance of energy utilization in the smart home system. Experimental results illustrate that the proposed deep ensemble learning model yields superior classification performance than the other baseline classifiers, such as Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) on the smart home dataset. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is fragmented into many different smart environments, each requiring specific technical solutions for their use cases. This is a problem that has worsened over the years as new standards and technologies are constantly being developed. The term "horizontal integration" is used in literature to describe the attempt to reunite this fragmented IoT landscape. Abstract (in the sense of the OSI model) solutions are often proposed for this. However, the limitations of the underlying technologies are rarely sufficiently pointed out. This paper examines the integration of two smart environments as examples - the Smart Home and the Smart City. More specifically, it takes a theoretical look at how technologies can be connected between two Smart Home systems with the help of a Smart City technology and the problems that arise with it. ZigBee and LoRa are used for this purpose as exemplary technologies. Various possible application scenarios are shown to carry out a qualitative assessment of the feasibility of such a solution. The resulting problems of such an integration are shown and possible solutions for these are discussed. [ABSTRACT FROM AUTHOR]

Time-series forecasting is challenging in the real world. Both short-term and long-term forecasting are important in various fields of research and industry. Most forecasting algorithms perform great in providing one-step predictions, i.e., predicting only the next value in the time series data, but do not perform well while predicting multiple steps into the future. On top of that, concept drift makes it more challenging. The aim of this paper is to develop a lightweight recurrent neural networks (RNN)-based model that can do forecasting in the short or long term with the ability to detect concept drift and adapt to it automatically using a recent window of the data stream. The suggested model performs better than current techniques, with the lowest Root Mean Square Error (RMSE) of 0.0701, demonstrating increased accuracy in adaptive time series forecasting for temperature control in smart homes. [ABSTRACT FROM AUTHOR]

The rapid evolution of the IoT has paved the way for new opportunities in smart city domains, including e-health, smart homes, and precision agriculture. However, this proliferation of services demands effective SLAs between customers and service providers, especially for critical services. Difficulties arise in maintaining the integrity of such agreements, especially in vulnerable wireless environments. This study proposes a novel SLA management model that uses an SDN-Enabled WSN consisting of wireless nodes to interact with smart contracts in a straightforward manner. The proposed model ensures the persistence of network metrics and SLA provisions through smart contracts, eliminating the need for intermediaries to audit payment and compensation procedures. The reliability and verifiability of the data prevents doubts from the contracting parties. To meet the high-performance requirements of the blockchain in the proposed model, low-cost algorithms have been developed for implementing blockchain technology in wireless sensor networks with low-energy and low-capacity nodes. Furthermore, a cryptographic signature control code is generated by wireless nodes using the in-memory private key and the dynamic random key from the smart contract at runtime to prevent tampering with data transmitted over the network. This control code enables the verification of end-to-end data signatures. The efficient generation of dynamic keys at runtime is ensured by the flexible and high-performance infrastructure of the SDN architecture. [ABSTRACT FROM AUTHOR]

Smart home manufacturers widely produce Wireless Lamp Socket and Power Plug devices, which offer various features ranging from basic on/off control to more complex functionalities such as monitoring energy consumption, power losses, and harmonics. However, these devices tend to be expensive, as indicated by market surveys. To address this issue, the current study aimed to develop low-cost wireless control nodes for smart homes that operate using relays. The two nodes consisted of lamp socket and power plug built with lowcost electronic components, including a Wi-Fi built-in Microcontroller ESP8266 (Wemos D1 mini model) as the backbone to create ESP-Mesh wireless network and to provide control ports for high/low logic, a relay module, an AC-to-DC converter module, and terminals (E27 screw for lamp socket node and C-type plug for the power plug node). This paper primarily focuses on the hardware aspects. In order to evaluate the effectiveness of the nodes, the following tests are conducted: (1) product demonstration to assess the product functions, (2) power measurement in idle and active conditions, (3) ESP-Mesh connection testing, and (4) RSSI measurement. Functional testing is done using a smartphone with the UPISmartHome version 2.0 Android application, which successfully controlled the nodes wirelessly. In idle conditions, power plug and lamp socket nodes consume 426.36 mWatt and 418.275 mWatt of power, respectively. Further, in active conditions, power plug and lamp socket nodes consume 435.704 mWatt and 440.583 mWatt of power, respectively. RSSI testing results show that both nodes can be controlled within an optimal range of 60 meters (with reference to RSSI below -85 dBm) without the Internet, utilizing the ESP-Mesh feature of ESP8266. This range is deemed reasonable for smart homes of 21, 36, or 45 square meters. Both nodes could be controlled under the ESP-Mesh network that gets build. We also present the comparison with other products of competitors in this paper. [ABSTRACT FROM AUTHOR]

Image captioning is a promising technique for remote monitoring of patient behavior, enabling healthcare providers to identify changes in patient routines and conditions. In this study, we explore the use of transformer neural networks for image caption generation from surveillance camera footage, captured at regular intervals of one minute. Our goal is to develop and evaluate a transformer neural network model, trained and tested on the COCO (common objects in context) dataset, for generating captions that describe patient behavior. Furthermore, we will compare our proposed approach with a traditional convolutional neural network (CNN) method to highlight the prominence of our proposed approach. Our findings demonstrate the potential of transformer neural networks in generating natural language descriptions of patient behavior, which can provide valuable insights for healthcare providers. The use of such technology can allow for more efficient monitoring of patients, enabling timely interventions when necessary. Moreover, our study highlights the potential of transformer neural networks in identifying patterns and trends in patient behavior over time, which can aid in developing personalized healthcare plans. [ABSTRACT FROM AUTHOR]

The popularization of the Internet of Things has made our daily life more and more convenient. By using the Internet of Things, we can create an intelligent home control system that makes our rooms safer, more comfortable, and can meet our growing demand for items. This article discusses how to use the Internet of Things to improve the efficiency of smart homes, and focuses on how to use wireless sensor networks, embedded systems, and various technologies such as network communication, audio and video to achieve these goals. This can enable these intelligent home control systems to provide efficient services. This article also introduces how to use Internet of Things technology in the design process of home control systems to better handle wiring and other related issues. We will also explore the differences between traditional and smart homes. The results showed that four buyers rated the smart home system, with an average score of 4 before use indicating a lower score, and an average score of 9 after use indicating a higher score. [ABSTRACT FROM AUTHOR]

Efficient energy management is greatly facilitated by accurately predicting energy consumption in a smart home, benefiting both consumers and utilities alike. The conventional forecasting techniques rely on pre-trained statistical models built upon extensive historical data, which may experience performance degradation due to the dynamic nature of power load demands. To address this limitation, this study proposes a novel approach employing Windowbased Support Vector Regression (WSVR) to accurately estimate energy requirements from a smart grid within a smart home. The dataset utilized for this research is sourced from Pecan Street in Texas, USA. To assess the efficacy of the proposed model, it is compared to several other time series data prediction models, including ARIMA, Holt Winter's, Linear Regression, Support Vector Machine, and Support Vector Regression. The performance of each model is evaluated, and the results are thoroughly examined and discussed. [ABSTRACT FROM AUTHOR]

Every homeowner wants maximum security for their home from various disturbances and threats. Many homeowners have installed a surveillance camera (CCTV) as their home security system. Apart from its relatively high price, the security camera system has weaknesses, which are difficult to integrate with stable and secure user devices in real-time. This research proposes a security system that integrates the Internet of Things (IoT) with the Arduino Uno microcontroller, PIR, and Ultrasonic sensors. The two sensors are used as scouts connected to a servo motor, an automatic locking device. The proposed system can be utilized as a safe and relatively inexpensive preventive measure. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has recently taken on a crucial role in numerous areas of our life, offering convenience and efficiency. One notable area of its application is in the growing adoption of smart home applications. This rapid growth has emphasized the pressing need to enhance the IT infrastructure to ensure the transparency, security, and privacy of user data. In this regard, blockchain technology has emerged as a promising solution to fulfill these critical demands. Hence, this study suggests an approach utilizing Hyperledger Fabric to enhance the security of smart home systems through blockchain technology. The proposed solution addresses the security limitations often encountered in existing permissioned blockchain approaches. The architecture consists of four layers: Cloud Storage Layer, Blockchain Platform Layer, Application layer and IoT Devices Layer. We have integrated a cloud storage layer to take benefit of its inherent advantages in terms of efficiency and availability. Smart home devices frequently require computing resources and substantial storage capacity, both of which can be effectively provided by the cloud. [ABSTRACT FROM AUTHOR]

The issue of modeling holds significant importance for the applicability of control theory, particularly at higher control levels where the verbal description of control objects becomes increasingly crucial. However, a notable disadvantage of such verbal descriptions is the inherent ambiguity and vagueness of the obtained results. This article aims to address this challenge by proposing an approach that utilizes Petri nets (PN) to model and control a specific component within an intelligent home. Additionally, the Simulink modeling tool and Petri net analysis tools are introduced as aids in the modeling process. The article focuses on creating a model that accurately represents the temperature regulation system in the home, considering the constant rise in energy consumption and costs. Furthermore, the transfer of the model to the Simulink environment using IF THEN rules is demonstrated, allowing for an evaluation of its performance. Through the comprehensive application of this approach, the aim is to optimize energy usage and reduce costs within household environments. [ABSTRACT FROM AUTHOR]

Adopting new technologies can be overwhelming, even for people with experience in the field. For the general public, learning about new implementations, releases, brands, and enhancements can cause them to lose interest. There is a clear need to create point sources and platforms that provide helpful information about the novel and smart technologies, assisting users, technicians, and providers with products and technologies. The purpose of these platforms is twofold, as they can gather and share information on interests common to manufacturers and vendors. This paper presents the "Finde-Dein-SmartHome" tool. Developed in association with the Smart Home & Living competence center [5] to help users learn about, understand, and purchase available technologies that meet their home automation needs. This tool aims to lower the usability barrier and guide potential customers to clear their doubts about privacy and pricing. Communities can use the information provided by this tool to identify market trends that could eventually lower costs for providers and incentivize access to innovative home technologies and devices supporting long-term care. [ABSTRACT FROM AUTHOR]

In recent years, smart devices have been more prevalent in people's homes. In this context, this study analyzes the role of trust and privacy in technology acceptance of those home devices. Derived from theoretical considerations, we form eleven hypotheses and test them by structural equation modeling (SEM). The analysis relies on data from a quantitative survey on smart thermostats with 324 participants from Germany. The results indicate a strong positive total effect of trust and a negative impact of privacy concerns on the intention to use, showing the special relationships of those factors in the context of smart home technology. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an interconnected network of electronic objects, machines, and physical items in our environment. Wireless Sensor Network is the key component of Internet of Things. Wi-Fi is used by IoT to link home appliances to the internet so that they may be managed and controlled remotely. Sensors were used in WSN technology to sense and gather data from various smart home components, then send it to a gateway. In this paper, the performance of IoT based WSN had been examined when the Jammers interfered with the normal operation of the network and caused deficiency in network from the delay, throughput and data dropped points of view which they are important parameters to examine the performance of any network. The aim of this paper is to improve the degradation in performance caused by Jammers using three Adhoc routing protocols (AODV, GRP and OLSR) in number of different Riverbed Modeler Simulation scenarios for different audio and video applications. The results showed that these routing protocols had a significant role in network performance. AODV had been investigated a better delay and data dropped improvement with acceptable throughput improvement while OLSR had the best throughput among other routing protocols with acceptable improvement in delay and data dropped. According to the research, an intelligent system should be used to decide whether a route is used for applications with a high throughput requirement or low delay and data loss. [ABSTRACT FROM AUTHOR]

This study examined the challenges in implementing a service delivery process of mainstream smart home technology (MSHT) as assistive technology devices (ATDs) among four provider groups using a systems thinking approach. Researchers conducted semi-structured interviews with each group using guiding questions based on the assistive technology service delivery process and summarized the challenges as case studies. All four provider groups intended to follow a current assistive technology service delivery process when delivering MSHT as ATDs. While the populations they serve and the funding environments are different, they all faced challenges with the assessment and technology recommendations for MSHT. In addition, the provision services including installation/setup, customization, and training are usually bundled, which often leaves no or less time for customization and training. The study provided insights into areas where support is likely needed for providing MSHT services to people with disabilities. [ABSTRACT FROM AUTHOR]

The emergence of smart homes, driven by the rapid growth and development of technology, has brought numerous benefits to human life, including convenience and improved wellbeing. However, the incorporation of IoT devices into smart homes and their connection to the Internet have created new security and privacy challenges that affect the confidentiality, integrity, and availability of data collected and exchanged by these devices. Such challenges have led to security threats that render IoT devices in smart homes vulnerable to various vector attacks. To provide a comprehensive picture of the security of smart homes, this paper applies the STRIDE [1] threat model to identify potential threats at different layers, namely the IoT device, communication, and application layers. Additionally, a risk-rating security threat model, DREAD, is used to assess the risks of these threats. Finally, this paper proposes a risk mitigation strategy to respond to the rated risks and improve the security of smart home IoT systems. The primary aim of this paper is to enhance the understanding of the various security threats insmart homes and provide a security baseline to enhance the security of smart home IoT systems. [ABSTRACT FROM AUTHOR]

The primary challenge that smart home users faced is the high energy consumption bills they received every month and they do not know how to optimize the energy consumption of their smart home devices. This increases the country's energy demand and accelerates the greenhouse effect worldwide. Additionally, when users run non-shiftable appliances unevenly, their energy consumption may surpass the maximum permissible power consumption limit. As a result, a short blackout happened in smart homes. To address such challenges, this study builds a mobile application that allows smart home users to control the energy consumption of their smart home appliances effectively. A scheduling task based on particle swarm optimization (PSO) is used to monitor and optimize energy consumption based on human activities in the home. Two experiments were conducted: with and without the implementation of the PSO algorithm. The result showed that the PSO algorithm performs better in optimizing energy consumption. [ABSTRACT FROM AUTHOR]

Informed by Foucault's governmentality, this article examines the making of the smart home in China. Operating within the nexus of security and risk, smart homes foster a discourse of the 'the good life' that accelerates AI's integration into the population's daily life. Taking Xiaomi (a renowned smart home technology company) as a case study, I trace how commercial practices formulate issues of security and risk in three smart home products: smart door lock, home surveillance camera and virtual home assistant. Drawing on visual and discourse analyses of Xiaomi's promotional materials, this analysis is structured around three levels of relationships: (a) trust and ontological security; (b) the practices of government and the practices of self (c) and the technologisation of Chinese society. This analysis demonstrates that Xiaomi further advances the state-driven technologisation of Chinese society, in which subjects are guided to embrace the positive dimensions of technology for self-actualisation and self-management. However, the technology that makes domestic life and the physical home more reliable, less prone to risks and more secure has at the same time further eroded social relations and trust. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is prevalent in various areas and enables a variety of smart applications such as smart factory, smart home, and smart health. The IoT corresponds to a network of physical objects, fostering the collection and exchange of data over the Internet. Each physical object is equipped with sensors, actuators, and other technologies, and is uniquely identifiable through its embedded computing system. Business Process Management (BPM), in turn, is concerned with the modeling, execution, discovery, analysis, and monitoring of business processes. By enriching business processes with real-world IoT data and IoT-driven business rules, process automation as well as real-time process monitoring and decision making can be enhanced. Despite the increasing interest in IoT-enhanced business process support, IoT-driven business rules have not been well integrated with business process execution so far. In this context, providing integrated lifecycle support for IoT-driven business rules in BPM systems constitutes a challenge. This paper discusses how to integrate lifecycle support of IoT-driven business rules with BPM systems covering the modeling, execution, monitoring, analysis, and evolution of IoT-driven business rules. Furthermore, all lifecycle phases need to be covered by holistic approaches to IoT-driven business rules. For each lifecycle phase, we elaborate on the requirements emerging in this context and discuss existing approaches. [ABSTRACT FROM AUTHOR]

The identification of electrical/electronic loads is one of the Smart Grids (SGs) relevant problems. An accuracy of 100 % can be achieved in SGs with non-similar loads. However, to the best of the authors knowledge, this level of accuracy was not described in the literature, considering SGs with similar loads. Therefore, the development of an intelligent classification system of similar electrical/electronic loads is described in this paper, aiming at the achievement of 100% accuracy with minimum parameters of the neural classifiers. An experimental platform containing an arrangement with four high similar loads was implemented. In such arrangement, all possible combinations are realized, that is, from all loads turned on until all turned off, resulting in non-similar and very similar loads, as found in real scenarios. The electrical signals demanded by the connected load arrangements are acquired and submitted to two different neural networks. The first neural network is an Extreme Learning Machine (ELM) classifier, where a Particle Swarm Optimization (PSO) technique is applied to select the best ELM model, whereas the second network is a Convolutional Neural Network (CNN) classifier. The ELM-PSO system is based on pre-processed data because it uses information based on the spectrum amplitude of the demand signals (features), whereas the CNN system is based on the use of the raw data, thus requiring a greater processing capacity when compared to the ELM-PSO. The simulation results show that the proposed CNN based system provides success rates considerably higher than the proposed ELM-PSO system, i.e., 100 % of accuracy with 381, 512 network parameters against 85 % with 14, 989 parameters of the ELM-PSO-based system. [ABSTRACT FROM AUTHOR]

Home automation based on the Internet of Things (IoT) includes various components such as lighting, security, and remote control. Smart Home (SH) components should be varied and customised according to the user's specific needs. Therefore, in this study, a SH system needed by users with specials is designed and implemented on a model. The NodeMCU microcontroller with ESP8266 Wi-Fi module, Radio Frequency Identification (RFID) tags, temperaturehumidity, motion detection, gas and moisture sensors and several actuators are used to build the system. Thanks to If This Then That (IFTTT), Google Assistant, and Blynk, the SH components can be managed remotely and with voice commands via a user-friendly Android-based mobile interface. The multiple control system and the ability to control the home components with different methods make this study comprehensive. In particular, the barrier-free stairs design has added innovation to the SH system for disabled people. Thus, the accessibility, security, and comfort requirements of disabled people are met, and their quality of life is improved to live independently. [ABSTRACT FROM AUTHOR]

Internet of Things is a paradigm that aims at connectivity for everything, such as computing devices, machines, objects, people and so on. Messaging protocols and electronic platforms are needed in the implementation of Internet of Things(IoT) based smart homes. These messaging protocols and electronic platforms enable the communication between home appliances and devices to happen. This paper would like to determine the commonly used messaging protocols and electronic platforms, and the messaging protocols' performance comparison, in building smart homes purposes, by conducting a Systematic Literature Review (SLR). Based on our study, we found out that the commonly used messaging protocols are MQTT, CoAP, AMQP and DDS, and it shows that MQTT performs better than other protocols as it has wide network bandwidth and a low size packets transferred. The commonly used electronic platforms are Raspberry Pi, Arduino and DDS. [ABSTRACT FROM AUTHOR]

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes. [ABSTRACT FROM AUTHOR]

With the development of information technology and human-display interaction (HDI), smart TV is gradually becoming one of the integral segments of daily life. Smart TVs serve as a core part of the smart home and have a wide range of user groups. However, manufacturers cannot fully anticipate their customers to develop a more engaging smart TV experience. Researchers believe that HDI on rotating display related to smart TV features can provide a better home environment for entertainment along with the full-size screen compared to the mobile phone, which can effectively enhance the user interaction with the smart TV and attract the user to engage in the smart life. In this study, quantitative analysis methods are used to analyze user behavior data which were collected by the features of TCL XESS rotating TV A200S and A200PRO. The findings were discussed in various usage factors, including the feature ranking by all users, the frequency of use, and most and least usage. To conclude, the intelligent design approach on HDI and TV features will support the way to suggest user preference in the next phase of smart TV development. [ABSTRACT FROM AUTHOR]

Ambient Intelligence is an emerging technology that allows for reasoning of activities that are carried out in a smart environment. Smart sensors play a vital role in activity recognition tasks. Though a great number of sensors provide more pinpointed information about where and when the interaction in the environment occurred, addition of sensors also results in higher power consumption and greater cost constraints. The number of training samples used for activity recognition tasks also increases and becomes computationally expensive. Thus, to overcome these constraints sensor optimization in smart environment is carried out. Mapping of sensors to a user's activity becomes tedious when more than one activity is carried out at the same timestamp. Unambiguous sensor-activity relationship is derived with the help of spatial and temporal context represented by ontology model that captures real time sensor data. Since there exits spatial noise during activity annotation process, it is eliminated with help of pointwise mutual information score. Spatial and temporal data of an activity helps sensor optimization in multi resident living with concurrent activity occurrence. Inactive sensors are removed using mutual information score and redundant sensors are contextually paired based on where an activity happens. By doing so, only unique set of sensors are paired every time even when concurrent activity occurs. The quality of features derived is tested by testing the accuracy of the model by activity recognition task using Random Forest algorithm. Nearly 24% of sensors were identified as inactive in the smart environment. The accuracy of Human Activity Recognition (HAR) task remained constant even after removing the inactive sensors. Addition of spatial context helped to improve HAR with increased accuracy. Contextual pairing of redundant sensors resulted in minimal drop in the model's accuracy. [ABSTRACT FROM AUTHOR]

The Internet of Things is poised to become an important crossroads for several technologies. As a result, it will be possible to connect smart physical goods and enable smart decision-making across a wide range of applications. Various devices, including computers, actuators, and sensors, may link to one another and exchange data in a networked environment known as the Internet of Things (IoT). Smart refers to how humans communicate with technology, whereas "smart objects" describes the machines themselves. As the market's supply of gadgets grows, ensuring their safety becomes more critical than ever. The availability of cutting-edge methods and tools has been instrumental in the current surge of interest in IoT security studies. To guarantee success, these applications need the characteristics of the environment provided by the IoT framework. This essay dives deep into the IoT's layered architecture, layer-by-layer protocols, cutting-edge use cases, and pervasive security concerns. In addition to analysing the current security structure, this paper provides a research taxonomy for IoT. Our study is more comprehensive than many that have come before it on the topic of the Internet of Things; we look at everything from sensors to real-world applications. [ABSTRACT FROM AUTHOR]

Nanotechnology has recently emerged as a pivotal field with wide-ranging implications. Its integration into the 6G-enabled Internet of Things (IoT) has given rise to the 6G-enabled IoNT (Internet of Nano Things) paradigm, impacting sectors such as healthcare, industries, smart homes, aerospace, and defense. This technology offers opportunities to revolutionize existing methodologies and enhance efficiency. Research efforts are now focusing on developing secure, scalable network infrastructures tailored for the healthcare sector at the nanoscale, leading to the concept of the Internet of Nano Medical Things (IoNMT). However, the unique characteristics of nanotechnology pose security challenges, particularly concerning privacy, confidentiality, dependability, latency, and the expensive consequences of blockchain-based storage. Authentication and transparency are vital for ensuring secure data handling in IoNMT networks, necessitating a secure access mechanism resistant to unauthorized interference. To tackle these challenges, this study proposes a smart contract-based authentication protocol developed specifically for 6G-IoNMT networks. The framework aims to manage real-time information with minimal latency through decentralized peer-to-peer cloud servers while addressing security and privacy concerns. Thorough security and privacy assessments, including ROR model evaluations, Scyther tool analysis, and informal security evaluations, validate the protocol's effectiveness. Moreover, the simulation highlights that this protocol offers superior security and efficiency as well as energy consumption compared to existing protocols. [ABSTRACT FROM AUTHOR]

Smart home IoT systems and devices are susceptible to attacks and malfunctions. As a result, users' concerns about their security and safety issues arise along with the prevalence of smart home deployments. In a smart home, various anomalies (such as fire or flooding) could happen due to cyber attacks, device malfunctions, or human mistakes. These concerns motivate researchers to propose various anomaly detection approaches. Existing works on smart home anomaly detection focus on checking the sequence of IoT devices' events but leave out the temporal information of events. This limitation prevents them from detecting anomalies that cause delay rather than missing/injecting events. To fill this gap, in this paper, we propose a novel anomaly detection method that takes the inter-event intervals into consideration. We propose an innovative metric to quantify the temporal similarity between two event sequences. We design a mechanism for learning the temporal patterns of event sequences of common daily activities. Delay-caused anomalies are detected by comparing the sequence with the learned patterns. We collect device events from a real-world testbed for training and testing. The experiment results show that our proposed method achieves accuracies of 93%, 88%, and 89% for three daily activities. [ABSTRACT FROM AUTHOR]

The smart home usually has poor security and is vulnerable to attack since it adopts embedded processors that are limited by volume and power consumption. To improve the communication security of the smart home system, identity-based signature schemes are widely used in wireless network communications. However, the user's signature private key is generally stored in a single device, it is easy to be stolen by attackers to control the smart home devices. To reduce the risk of leakage of the signature private key, a two-party collaborative signature scheme based on the SM9 algorithm is proposed in this paper. The user's signature private key is generated through the collaboration of the two-party key generation center (KGC), and the integer secrets related to the signature private key are stored in two devices respectively. During the signing process, the two devices sign collaboratively to prevent the complete private key from being leaked. The security of the scheme is proved in the random oracle model. Theoretical analysis and experimental results show that our proposed scheme can achieve higher security with lower computation cost and communication cost when compared with the existing two-party SM9 signature schemes. [ABSTRACT FROM AUTHOR]