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Home appliances connected to Internet-of-Things (IoT) platforms have been extensively installed in smart homes. In this context, home energy management systems (HEMSs) have emerged as a viable solution for reducing energy costs. Although several studies have analyzed the implementation of HEMSs, a majority of these studies were based on the installation of numerous sensors. Owing to the complexity and costs associated with the installation of multiple sensors, implementation of HEMSs in smart homes is challenging. This paper presents an energy management scheme for an HEMS that minimizes the energy cost in a smart home with data obtained from IoT-based appliances typically used in a smart home. Case studies were conducted to demonstrate the effectiveness of the proposed method. Prototype software (version 11) based on the proposed method for a HEMS and a test house with IoT-based appliances were also implemented in the case studies. [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]

The concept of smart houses has grown in prominence in recent years. Major challenges linked to smart homes are identification theft, data safety, automated decision-making for IoT-based devices, and the security of the device itself. Current home automation systems try to address these issues but there is still an urgent need for a dependable and secure smart home solution that includes automatic decision-making systems and methodical features. This paper proposes a smart home system based on ensemble learning of random forest (RF) and convolutional neural networks (CNN) for programmed decision-making tasks, such as categorizing gadgets as "OFF" or "ON" based on their normal routine in homes. We have integrated emerging blockchain technology to provide secure, decentralized, and trustworthy authentication and recognition of IoT devices. Our system consists of a 5V relay circuit, various sensors, and a Raspberry Pi server and database for managing devices. We have also developed an Android app that communicates with the server interface through an HTTP web interface and an Apache server. The feasibility and efficacy of the proposed smart home automation system have been evaluated in both laboratory and real-time settings. It is essential to use inexpensive, scalable, and readily available components and technologies in smart home automation systems. Additionally, we must incorporate a comprehensive security and privacy-centric design that emphasizes risk assessments, such as cyberattacks, hardware security, and other cyber threats. The trial results support the proposed system and demonstrate its potential for use in everyday life. [ABSTRACT FROM AUTHOR]

Not securing smart home devices has proven a threat to cyberspace. This has underscored the importance of using fear appeals to promote users' information security behavior. We practiced context-specific theorization to enhance fear appeal theory and design. Particularly, we extended Protection Motivation Theory to include avoidant-focused motivation (i.e., users' intent to avoid using their devices), the positive emotion of hope, and information technology (IT)-self extension. Our hypotheses include that fear engenders both protection and avoidant-focused motivations, hope mediates coping appraisal to engender (reduce) protection (avoidant-focused) motivation, and IT-self-extension acts as an antecedent. We conducted four studies, including two surveys and two experiments, and validated our extensions. Our main theoretical contributions include showing that hope is critical in determining which coping mechanism occurs and that it improves the theory's predictive power. In terms of practice, we demonstrate that a fear appeal message with a self-extension component and a strong coping component is more effective. [ABSTRACT FROM AUTHOR]

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts. [ABSTRACT FROM AUTHOR]

A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry. [ABSTRACT FROM AUTHOR]

The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants. [ABSTRACT FROM AUTHOR]

The number of smart homes globally has increased to 300 million, and the smart home market is expected to reach approximately $181.4 billion by 2025. These new developments, however, are accompanied by related security risks. The attack surface for smart home devices poses latent dangers because of inadequate security that enables cybercriminals to gain access to such devices. This Article proposes extending product liability to address security vulnerabilities in smart home devices. Part I examines the ubiquity of smart home devices. Part II sets forth the breadth of security vulnerabilities in connected devices, confirming the need to clarify that product liability applies to software and to create a global standard that reduces compliance costs for smart home device makers. Part III develops a detailed global standard for smart home device product liability, aligning U.S. product liability law with the proposed revision of the European Union's Product Liability Directive 85/374/EEC. [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 evolution of the smart grid has enabled residential users to manage the ever-growing energy demand in an efficient manner. The smart grid plays an important role in managing this huge energy demand of residential households. A home energy management system enhances the efficiency of the energy infrastructure of smart homes and provides an opportunity for residential users to optimize their energy consumption. Smart homes contribute significantly to reducing electricity consumption costs by scheduling domestic appliances effectively. This residential appliance scheduling problem is the motivation to find an optimal appliance schedule for users that could balance the load profile of the home and helps in minimizing electricity cost (EC) and peak-to-average ratio (PAR). In this paper, we have focused on appliance scheduling on the consumer side. Two novel home energy management models are proposed using multiple scheduling options. The residential appliance scheduling problem is formulated using the multiple knapsack technique. Serial and parallel scheduling algorithms of home appliances namely MKSI (Multiple knapsacks with serial implementation) and MKPI (Multiple knapsacks with parallel implementation) are proposed to reduce electricity cost and PAR. Price-based demand response techniques are incorporated to shift appliances from peak hours to off-peak hours to optimize energy consumption. The proposed algorithms are tested on real-time datasets and evaluated based on time of use pricing tariff and critical peak pricing. The performance of both the algorithms is compared with the unscheduled scenario and existing algorithm. Simulations show that both proposed algorithms are efficient methods for home energy management to minimize PAR and electricity bills of consumers. The proposed MKSI algorithm achieves cost reduction of 20.26% and 42.53% for TOU and CPP, respectively as compared to the unscheduled scenario while PAR is reduced by 45.07% and 39.51% for TOU and CPP, respectively. The proposed MKPI algorithm achieves 22.33% and 46.36% cost reduction compared to the unscheduled case for TOU and CPP while the PAR ratio is reduced by 46.47% and 41.16% for TOU and CPP respectively. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks. [ABSTRACT FROM AUTHOR]

Buildings account for a significant portion of total energy consumption, and the introduction of intelligent buildings represents a significant step forward in efficiently managing energy utilization. The proposed solutions represent a significant step forward in the development of intelligent residential environments. Beginning the process of achieving improved building intelligence necessitates a thorough evaluation and prediction of the necessary heating and cooling energy requirements, taking into account all relevant influencing factors. This study describes methodologies for using data mining models to predict the heating and cooling energy requirements of intelligent buildings during the construction phase. Data mining techniques, specifically Support Vector Machines (SVM) and Random Forest, are used, demonstrating their superior efficiency over alternative methods. Metaheuristic algorithms, particularly the Owl Search Algorithm (OSA), are described as effective tools for optimizing results across a wide range of problem resolutions. OSA is described and proposed alongside novel data mining methods, demonstrating that this combination of algorithms improves the performance of Random Forest and SVM-based models by 11% and 24%, respectively. The proposed models can generate predictions with a small number of parameters, eliminating the need for complex software and tools. This user-friendly approach makes the prediction process more accessible to a wider audience. While specialized equipment and professional-grade tools will be used, the proposed models are accessible to a wide range of individuals interested in participating in the prediction process. [ABSTRACT FROM AUTHOR]

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual. [ABSTRACT FROM AUTHOR]

Human Activity Recognition (HAR) has several applications in healthcare, security, and assisted living systems used in smart homes. The main aim of these applications or systems is to classify body movement read from the built in sensors such as accelerometers and gyroscopes. Some actions could be performed in response to the output of these HAR systems. The number of smartphone users increases, whereas the sensors are widely available in different sizes and shapes (internal or external sensors). Recent advances in sensor technology and machine learning have led researchers to conduct studies on sensor technology such as HAR. HAR systems typically use a combination of sensors, such as accelerometers, gyroscopes, and cameras, to collect images or signal data that can be classified by machine learning algorithms. HAR research has focused on several key challenges including dealing with variability in sensor data, handling missing data or noise, and dealing with large amounts of sensor-generated data. In this work, several machine learning algorithms were tested in predefined settings using the KU-HAR dataset in a series of experiments. Subsequently, various performance metrics were calculated to assess the chosen algorithms' performance. The experimental findings showed that the LightGBM classifier surpassed the other machine learning algorithms in performance metrics, such as accuracy, F1 score, precision, and recall. Although Gradient Boosting has lengthy training time, the other classifiers complete their training in an acceptable time period. [ABSTRACT FROM AUTHOR]

The trade, control, and management of transactive energy have gained significant relevance and are receiving a lot of interest from academia and industry. New strategies for their progress and implementation are emerging. The prosumer concept involves the integration of household loads with solar PV and battery storage systems. It is suggested as a practical technique to include renewable energy sources and reduce strain on the distribution grid. This study indicates establishing a home-smart community connected to a power grid, explicitly emphasizing prosumers. The district employs cooperative game theory to enable the sharing, controlling, and coordinating of excess energy generated by distributed energy resources (DERs) with neighboring prosumers experiencing energy shortages. The energy community examined in this study comprises six identical prosumers, encompassing residential energy use, solar photovoltaic (PV) installations, and battery storage systems. Furthermore, the community consists of an energy community manager and an energy retailer, both tied to a distribution system. The intended market model assesses the economic advantages of incorporating renewable sources into a centralized transactive energy trading system This approach reduces transaction costs and power expenditures, offering cost savings to producers and consumers. The results show that the suggested intelligent method has successfully decreased consumer electricity costs in various circumstances. Indeed, the proposed approach seeks to oversee the exchange, organization, and control of the community's household electricity consumption effectively. Furthermore, its objective is to diminish energy usage, enhance system effectiveness, and minimize operational strain and carbon emissions. [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]

In response to social distancing measures during the COVID-19 pandemic, there was a need to increase the frequency of internet enabled behaviors (IEBs). To date, little is known about how the pandemic impacted IEBs in older adults, a population that has historically been linked to lower digital literacy and utilization. We administered an online survey between April and July 2021 to 298 adults who were over age 50 (mean age = 73 years; 93.5% non-Hispanic white; 94% smart phone owners; 83.5% retired). Older adults self-reported IEBs for social, shopping, medical, and leisure activities during the pandemic, plans for continued use of these behaviors, and completed measures of psychosocial functioning. 66.8% of respondents reported an overall increase in IEBs during the pandemic, most notably for online meeting attendance. More frequent online meeting use was associated with less depression (r  = −0.12, p  = .04) and less loneliness (r = − 0.14, p  = .02). With regard to plans for continued use, 82.5% of the sample reported at least one IEB (M  = 2.18, SD  = 1.65) that they increased during the pandemic and planned to maintain over time (e.g. online shopping for household goods). Plans for continued use were more likely in participants who used IEBs more overall during the pandemic (r  = 0.56, p  < .001), and who frequently sought technical support on search engines (r  = 0.22, p  < .001), or online video sites (r  = 0.16, p  = .006). In summary, IEBs during the pandemic were associated with favorable psychosocial functioning and expectations for continued use in this sample of predominantly white older adults who had some baseline technological familiarity. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of solutions based on artificial intelligence (AI) in the urban energy sector, with a focus on their applications and impacts. The study employed a literature review methodology to analyze recent research on AI's role in energy-related solutions, covering the years 2019 to 2023. The authors classified publications according to their main focus, resulting in two key areas of AI implementation: residential and individual user applications, and urban infrastructure integration for society. The objectives of this review of the literature are the following: O1: to identify trends, emerging technologies, and applications using AI in the energy field; O2: to provide up-to-date insights into the use of AI in energy-related applications; O3: to gain a comprehensive understanding of the current state of AI-driven urban energy solutions; O4: to explore future directions, emerging trends, and challenges in the field of AI-driven energy solutions. This paper contributes to a deeper understanding of the transformative potential of AI in urban energy management, providing valuable insights and directions for researchers and practitioners in the field. Based on the results, it can be claimed that AI connected to energy at homes is used in the following areas: heating and cooling, lighting, windows and blinds, home devices, and energy management systems. AI is integrating into urban infrastructure through the following solutions: enhancement of electric vehicle charging infrastructure, reduction in vehicle emissions, development of smart grids, and efficient energy storage. What is more, the latest challenges associated with the implementation of AI-driven energy solutions include the need to balance resident comfort with energy efficiency in smart homes, ensuring compatibility and cooperation among various devices, preventing unintended energy consumption increases due to constant connectivity, the management of renewable energy sources, and the coordination of energy consumption. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management system (EMS) in smart grid based on Internet of Things (IoT) configuration using hybrid approach. The proposed approach is joint execution of Multi-fidelity meta-optimization and Turbulent Flow of water based optimization (TFWO), hence it is known as M2FWO technique. The main objective of this proposed EMS is to better manage power with the resources of Smart Grid by constantly monitoring data from the IoT-based communication framework. Here, each home device is connected to data acquisition module is utilized to facilitate the demand response (DR) growth for energy management system in smart grid. The framework collects DR from every smart home device and then transmits the data to the centralized server. The transmitting data is enabled by M2FWO method. The smart grid IoT framework enhances the feasibility of these networks makes better use of obtainable resources. The proposed system is in charge to satisfy the total supply with energy requirement. Finally, the proposed model is stimulated on MATLAB/Simulink site, then the efficiency is examined with existing methods. [ABSTRACT FROM AUTHOR]

Monitoring systems have become increasingly prevalent in order to increase the safety of elderly people who live alone. These systems are designed to raise alerts when adverse events are detected, which in turn enables family and carers to take action in a timely manner. However, monitoring systems typically suffer from two problems: they may generate false alerts or miss true adverse events. This motivates the two user studies presented in this paper: (1) in the first study, we investigate the effect of the performance of different monitoring systems, in terms of accuracy and error type, on users' trust in these systems and behaviour; and (2) in the second study, we examine the effect of recommendations made by an advisor agent on users' behaviour. Our user studies take the form of a web-based game set in a retirement village, where elderly residents live in smart homes equipped with monitoring systems. Players, who "work" in the village, perform a primary task whereby they must ensure the welfare of the residents by attending to adverse events in a timely manner, and a secondary routine task that demands their attention. These conditions are typical of a retirement setting, where workers perform various duties in addition to keeping an eye on a monitoring system. Our main findings pertain to: (1) the identification of user types that shed light on users' trust in automation and aspects of their behaviour; (2) the effect of monitoring-system accuracy and error type on users' trust and behaviour; (3) the effect of the recommendations made by an advisor agent on users' behaviour; and (4) the identification of influential factors in models that predict users' trust and behaviour. The studies that yield these findings are enabled by two methodological contributions: (5) the game itself, which supports experimentation with various factors, and a version of the game augmented with an advisor agent; and (6) techniques for calibrating the parameters of the game and determining the recommendations of the advisor agent. [ABSTRACT FROM AUTHOR]

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Internet of Things (IoT) on a global scale has facilitated the convergence of revolutionary technologies such as artificial intelligence (AI), blockchain, and cloud computing. The integration of these technologies has paved the way for the development of intricate infrastructures, such as smart homes, smart cities, and smart industries, that are capable of delivering advanced solutions and enhancing human living standards. Nevertheless, IoT devices, while providing effective connectivity and convenience, often rely on traditional network interfaces that can be vulnerable to exploitation by adversaries. If not properly secured and updated, these legacy communication protocols and interfaces can expose potential vulnerabilities that attackers may exploit to gain unauthorized access, disrupt operations, or compromise sensitive data. To overcome the security challenges associated with smart home systems, we have devised a robust framework that leverages the capabilities of both AI and blockchain technology. The proposed framework employs a standard dataset for smart home systems, from which we first eliminated the anomalies using an isolation forest (IF) algorithm using random partitioning, path length, anomaly score calculation, and thresholding stages. Next, the dataset is utilized for training classification algorithms, such as K-nearest neighbors (KNN), support vector machine (SVM), linear discriminate analysis (LDA), and quadratic discriminant analysis (QDA) to classify the attack and non-attack data of the smart home system. Further, an interplanetary file system (IPFS) is utilized to store classified data (non-attack data) from classification algorithms to confront data-manipulation attacks. The IPFS acts as an onsite storage system, securely storing non-attack data, and its computed hash is forwarded to the blockchain's immutable ledger. We evaluated the proposed framework with different performance parameters. These include training accuracy (99.53%) by the KNN classification algorithm and 99.27% by IF for anomaly detection. Further, we used the validation curve, lift curve, execution cost of blockchain transactions, and scalability (86.23%) to showcase the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

The article discusses the legal issues related to the adoption of technological innovations like the Internet of Things (IoT) smart devices and artificial intelligence (AI) in consumer products. Other topics include the need for regulations on who are liable and responsible for the potential harms and damages caused by said technologies, as well as the physical and nonphysical harms that they could cause like physical injuries and algorithmic biases.

Internet of Things (IoT) devices are common in today's computer networks. These devices can be computationally powerful, yet prone to cybersecurity exploitation. To remedy these growing security weaknesses, this work proposes a new artificial intelligence method that makes these IoT networks safer through the use of autonomous, swarm-based cybersecurity penetration testing. In this work, the introduced Particle Swarm Optimization (PSO) penetration testing technique is compared against traditional linear and queue-based approaches to find vulnerabilities in smart homes and IoT networks. To evaluate the effectiveness of the PSO approach, a network simulator is used to simulate smart home networks of two scales: a small, home network and a large, commercial-sized network. These experiments demonstrate that the swarm-based algorithms detect vulnerabilities significantly faster than the linear algorithms. The presented findings support the case that autonomous and swarm-based penetration testing in a network could be used to render more secure IoT networks in the future. This approach can affect private households with smart home networks, settings within the Industrial Internet of Things (IIoT), and military environments. [ABSTRACT FROM AUTHOR]

Over the past decade, the Internet of Things (IoT) has become a necessary technology increasingly applied in many fields of research and development, such as smart cities and homes, health, industry, agriculture, security, and surveillance. In IoT systems, the use of sensors is considered an important common manner in which all devices communicate with a wireless sensor network to form an information system that comprises a massive number of sensor nodes performing accurately to create a smart decision-making method. However, these sensor nodes might be employed in severe environments, where replacing or recharging their batteries is considered an impossible mission. Simultaneously, the limitation of energy resources in sensor nodes presents a challenging issue that reduces the lifespan of individual nodes and the overall network system as a result of energy depletion. These obstacles necessitate energy-efficient routing protocols. According to the literature review, various routing protocols have been introduced, especially those that use clustering techniques. However, they have many drawbacks due to the way of selecting the cluster head (CH), which results in consuming energy dramatically, and consequently shortening the network lifetime. Additionally, instead of using the static sink, which was inefficient in collecting data, many researchers studied the behavior of the mobile sink (MS), which also has many downsides that negatively impact network performance. This paper presents a novel energy-conscious protocol for clusters that incorporates an adaptive movement for mobile stations and utilizes particle swarm optimization (PSO). The circular network area is divided into clusters, each of which has an elected CH based on the PSO technique. The MS aims to distribute energy among nodes to prevent hotspot issues. To achieve better coverage, it moves in a circular pattern with a constant angular velocity, starting from the center of the network area and moving forward and backward along the radius of the network. This research conducts intensive simulations, which run on MATLAB R2018, to assess the performance of our proposed protocol and compare its results with those of pertinent works. The results obtained are encouraging and demonstrate that the protocol we proposed surpasses its counterparts by significantly extending the lifespan of the network. [ABSTRACT FROM AUTHOR]

Distributed smart home systems using wireless communication are increasingly installed and operated in households. Their popularity is due to the ease of installation and configuration. This paper presents a comprehensive technical, quality, and energy analysis of several popular smart home modules. Specifically, it focuses on verifying their power consumption levels, both in standby and active mode, to assess their impact on the energy efficiency of building installations. This is an important aspect in the context of their continuous operation, as well as in relation to the relatively lower power of loads popular in buildings, such as LED lighting. The author presents the results of measurements carried out for seven different smart home modules controlling seven different types of loads. The analysis of the results shows a significant share of home automation modules in the energy balance; in particular, the appearance of reactive power consumption due to the installation of smart home modules is noteworthy. Bearing in mind all the threads of the analysis and discussion of the results of measurement experiments, a short SWOT analysis is presented, with an indication of important issues in the context of further development of smart systems and the Internet of Things with wireless communication interfaces, dedicated to home and building applications. [ABSTRACT FROM AUTHOR]

In the modern era, smart home technology is considered as the most growing application of Internet of Things (IoT) as they facilitate users for control home devices remotely. Anonymous and efficient authentication approaches in smart home environments are essential for attaining secure communication as the home and user communication channel is highly vulnerable and insecure. In this paper, Merkle Hash Tree and Strongly Constrained Discrete Hashing Function-based Authentication (MHS-SCDHF-AS) that integrates the transaction history and physical context awareness is proposed for enabling security for Smart Home IoT applications. This MHS-SCDHF-AS prevents the problem of clock synchronization and does not utilize a verification table both during the authentication process. This Strongly Constrained Discrete Hashing Functionis a supervised hashing method which overcomes the limitations of large quantization errors and suboptimal binary codes. It is also capable of learning binary codes and significant in resisting the transmission loss as each information used for verification contains its authentication data in itself. The computation cost and communication cost of the proposed MHS-SCDHF-AS is identified to be lesser than the benchmarked authentication schemes considered for investigation. The formal, informal and model checking-based security investigation of the proposed MHS-SCDHF-AS is also confirmed to be superior over the baseline authentication schemes used for smart home environments. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations' investments into AI and to lay out an agenda for future research. [ABSTRACT FROM AUTHOR]

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme. This research paper advances the state of the art by conducting a security analysis of user-authentication approaches regarding their vulnerability to node capture attacks resulting in revelations of several security weaknesses. To this end, we propose a secure user-authentication scheme to counter node capture attacks in smart home environments. To validate the effectiveness of our proposed scheme, we employ the BAN logic and ProVerif tool for verification. Lastly, we conduct performance analysis to validate the lightweight nature of our user-authentication scheme, making it suitable for IoT-based smart home environments. [ABSTRACT FROM AUTHOR]

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With the ageing of society, the number of households with older individuals or couples living alone is increasing. An "ageing-in-place" approach allows older adults to continue to live at home and receive help only when needed. However, this approach is insufficient for emergencies, such as falls, as well as for individuals with gradually deteriorating health conditions, such as frailty. Unexpected accidents significantly reduce the quality of life (QoL) of older adults. This paper proposes a new framework, the mobility ubiquitous sensor edge environment (MUS3E), to digitally transform ordinary houses to detect the movement of older individuals throughout their home environment and to notify family members and care providers of residents' health status and safety information. The framework can be easily and inexpensively installed in any home, transforming an ordinary house into a smart home with an automated function for monitoring older residents. It uses ambient sensors such as passive infrared ray sensors to automatically measure health conditions by measuring factors such as walking speed. Residents need not interact with or control the system and can go about their daily lives. Since the sensors used in this system are mass-produced consumer products, they are inexpensive and easily replaceable, as there are many alternatives. In this study, we were able to demonstrate the practically and feasibility of this framework using a prototype that uses open architecture Internet of Things (IoT) software (Debian GNU/Linux 11, Arduino 1.8.19, ESP8266 2.7.4, ESP32 1.0.6, PubSubClient 2.8.0, ESPPerfectTime 0.3.0, mosquitto 2.0.11) components to digitally transform the living environment of older individuals. [ABSTRACT FROM AUTHOR]

With the development of the society, people's material needs are increasing rapidly. Based on this background, intelligent household furniture products with high-tech content are integrated into the field of interior design, and then gradually infiltrated into people's production and life. And from a smart technology perspective, we analyze the art design of smart home products based on machine learning algorithm. This algorithm can make the actual classification results of the test samples consistent with the network output values, and the error values can also meet the accuracy requirements, so as to effectively determine the defect types of the parameter samples. Among them, the art design of smart home products includes home equipment network group, embedded gateway implementation, cloud server construction and interaction design, independent product auxiliary control system and other parts. Finally, through the simulation test results of the intelligent positioning function of the product, we can know that the communication efficiency of the product basically meets the requirements. Interior design products are indispensable in people's home life, and play an important role in the whole indoor environment, such as art paintings, bonsai, woven art, etc. In addition, household appliances and lamps will also affect the aesthetic feeling of the whole interior design. This study tries to introduce machine learning technology in the field of interior art design to make it more intelligent, and a kind of effective algorithm design is completed. [ABSTRACT FROM AUTHOR]

In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The features of a smart home system with IoT technology include temperature control, lighting control, surveillance, security, smart electricity, and water sensors. Nowadays, elderly people may not be able to maintain their homes appropriately and may feel uncomfortable performing day-to-day activities. Therefore, Cyborg can be used to carry out the activities of elderly people. Such activities include switching off unnecessary lights, watering plants, gas control, monitoring intruders or unknown persons, alerting elderly people in emergency situations, etc. These activities are controlled by web-based platforms as well as smartphone applications. The issues with the existing algorithms include that they are inefficient, require a long time for implementation, and have high storage space requirements. This paper proposes the k-nearest neighbors (KNN) with an artificial bee colony (ABC) algorithm (KNN-ABC). In this proposed work, KNN-ABC is used with wireless sensor devices for perceiving the surroundings of the smart home. This work implements the automatic control of electronic appliances, alert signal processors, intruder detection, and performs day-to-day activities automatically in an efficient way. GNB for intruder detection in the smart home environment system using the Cyborg human intervention robot achieved an accuracy rate of 88.12%, the Artificial Bee Colony algorithm (ABC) achieved 90.12% accuracy on the task of power saving in smart home electronic appliances, the KNN technique achieved 91.45% accuracy on the task of providing a safer pace to the elderly in the smart home environment system, and our proposed KNN-ABC system achieved 93.72%. [ABSTRACT FROM AUTHOR]

With the continuous development of network information technology, people's dependence on network information is becoming stronger and stronger. The information on the Internet shows a trend of explosion, and information overload has also become a research hotspot. Due to the defects of cold start and sparse data, the traditional personalized recommendation algorithm will show the problem of accuracy degradation in the face of excessive information. Therefore, the traditional methods have been unable to adapt to the current needs of literature and art analysis. The goal of speech enhancement is to remove noise interference from noisy sounds and extract pure sounds as much as possible. Speech enhancement can reduce sound distortion, improve sound quality, and reduce hearing fatigue. At present, voice enhancement technology is widely used in products and fields such as mobile communications, computers, smart phone devices, and smart homes. First, this article will briefly introduce the artistic analysis of film and television works. Starting from the main characteristics of film and television works, according to the characteristics of various data lists based on visualization and visual data mining. Through visual data mining, the experimental data set used in this article is constructed based on various data types such as the main narrative element data set and the character action data set. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology that attracted considerable attention in the last decade to become one of the most researched topics in computer science studies. This research aims to develop a benchmark framework for a public multi-task IoT traffic analyzer tool that holistically extracts network traffic features from an IoT device in a smart home environment that researchers in various IoT industries can implement to collect information about IoT network behavior. A custom testbed with four IoT devices is created to collect real-time network traffic data based on seventeen comprehensive scenarios of these devices' possible interactions. The output data is fed into the IoT traffic analyzer tool for both flow and packet levels analysis to extract all possible features. Such features are ultimately classified into five categories: IoT device type, IoT device behavior, Human interaction type, IoT behavior within the network, and Abnormal behavior. The tool is then evaluated by 20 users considering three variables: usefulness, accuracy of information being extracted, performance and usability. Users in three groups were highly satisfied with the interface and ease of use of the tool, with scores ranging from 90.5% to 93.8% and with an average score between 4.52 and 4.69 with a low standard deviation range, indicating that most of the data revolve around the mean [ABSTRACT FROM AUTHOR]