Search

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]

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]

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]

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]

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]

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]

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]

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 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]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

This scientific paper highlights some aspects related to the design and real implementation of a smart miniature camera, which can represent a modern home automation solution. It integrates sensors controlled by the ESP-NOW communication protocol using the ESP32 microcontroller. The miniature camera consists of a living space incorporated with various sensors to measure temperature, humidity, air quality, and other parameters necessary for environmental comfort. The sensors are connected to a development board, which is programmed to collect, process, and interpret data from the sensors. Additionally, a software application has been developed using the C# programming language for a computer, which allows the user to remotely monitor and control the camerațs sensor data. The proposed smart miniature camera can be implemented in various scenarios such as smart homes, offices, and public spaces to provide a comfortable and efficient environment for occupants. [ABSTRACT FROM AUTHOR]

In this research paper, the problem of scheduling the home appliances is addressed. Generally, the householder's main attention is focused on minimizing his electricity payment by moving the use of the appliance from hours of high electricity rates to hours of low electricity rates. Consequently, the problem of peak demand is created. Thus, the main target behind this work is to propose an efficient scheduling strategy so that the electricity payment is reduced, the peak demand is minimized, and the user's comfort is maintained. The proposed scheduling problem is mathematically elaborated as Mixed-Integer Linear Programming where the decision variables indicate whether the electrical appliances are switched ON/OFF. The proposed strategy is implemented in four different scenarios with 10 appliances and proved to be efficient, simple, and reliable. To scrutinize the efficiency of the suggested method, similar studies in literature are used for comparison. Findings prove that the proposed method accurately minimizes the electricity payments, the peak demand while completing the 100% task required at the end of the day with lower computational time response. [ABSTRACT FROM AUTHOR]

Any set of devices for controlling home appliances that link to a common network and may be controlled independently or remotely are typically referred to as smart home technology. Smart homes and home automation are not completely unknown to people anymore; smart devices and sensors are part of daily life in the 21st century. Among other benefits of home automation devices, it is possible to manage home appliances and monitor resource usage, and security. It is essential to find practical information about smart home users, and possible use cases. The current survey covers smart home usage benefits and challenges for the users. The study presents the result of the collected information from different countries, and the participants are people from a variety of age groups and occupations. The questionnaire that contains both qualitative and quantitative questions was distributed through internet channels such as blog posts and social network groups. Furthermore, to generate the survey questions we conducted a literature review to gain a better understating of the subject and the related work. The research provides a better foundation for future smart home development. As a result of this survey-based study and in addition to finding the desirable home automation features, we discovered the amount of money users are ready to spend to automate their homes. Connecting the favourite smart home features to its users and the amount of money they are ready to spend on them can provide a bigger picture for the smart home industry as a whole and particularly be beneficial for developers and start-ups. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence of things (AIoT) is becoming increasingly widespread. With its rapid development, it has raised many complex network security issues. These have brought crushing security threats and risks to all stakeholders. AIoT cloud platform in the smart home plays a vital role in the security management of devices and is the current research hotspot. This paper discusses the security of the AIoT cloud platform, represented by the smart home. The indicator system describing the protection of the smart home cloud platform is proposed, and the security risk evaluation method of the cloud service is formed. Based on the evaluation method, a lightweight security evaluation framework for smart home cloud service is designed to evaluate the typical cloud platform's security. The evaluation identifies some technical and administrative issues. Finally, to improve the security of the cloud console of AIoT and discover potential security risks in time, a security self-test model of the scalable cloud console is proposed. [ABSTRACT FROM AUTHOR]

The aim of the study was to study the intelligent management of electrical automation through Internet platform. This article takes the example of smart home to analyze the current state of smart home, explores relevant technologies for smart home applications, and offers solutions. The results are as follows: the ZigBee gateway control model, consisting of the ARM9 S3C2440 and the wireless radio frequency chip CC2530, developed the ZigBee wireless star network structure and introduced the ZigBee regulator and terminal node control flow. A four-state switch that may be operated manually and automatically, that utilizes IoT, and that ultimately is in the off-mode state was devised and used in the proposed control system. The suggested model is an extremely dependable system that offers a practical and satisfying solution for electrical management in the agricultural, industrial, and home consumption sectors. To show how well the suggested control system works, the model is applied to a real-world case study. The design controls the sending and receiving of SMS with a global system for mobile communication module, has a remote control for opening and closing of smart home equipment and real-time alarm, and reduces the response time by about 60%. It has made a new attempt for the development of smart home market. [ABSTRACT FROM AUTHOR]

With the development of the Internet and hardware equipment, the Internet of Things (IoT) can be seen everywhere in our life. In 2005, the International Telecommunication Union (ITU) pointed out that the era of the "Internet of Things" is coming. Whether in a city, an industry, or a home, we can use the IoT to connect devices in everywhere. In addition, there are reports indicated that the number of smart home devices increased, and more and more people choose to use smart homes to manage their home devices. However, with the increase in the number of people using smart homes, there are more and more doubts about smart homes. Smart homes are full of information about various devices in the house. These data are closely related to the privacy of everyone in the home. However, in the process of protecting these data, the computing power of the device must be considered. Because the computing power of the devices in the home is usually limited, this thesis proposes an identity and packet verification mechanism that gives the smart home a lightweight computing and secure environment. [ABSTRACT FROM AUTHOR]

Among the plethora of IoT (Internet of Things) applications, the smart home is one of the fastest-growing. However, the rapid development of the smart home has also made smart home systems a target for attackers. Recently, researchers have made many efforts to investigate and enhance the security of smart home systems. Toward a more secure smart home ecosystem, we present a detailed literature review on the security of smart home systems. Specifically, we categorize smart home systems' security issues into the platform, device, and communication issues. After exploring the research and specific issues in each of these security areas, we summarize the root causes of the security flaws in today's smart home systems, which include the heterogeneity of internal components of the systems, vendors' customization, the lack of clear responsibility boundaries and the absence of standard security standards. Finally, to better understand the security of smart home systems and potentially provide better protection for smart home systems, we propose research directions, including automated vulnerability mining, vigorous security checking, and data-driven security analysis. [ABSTRACT FROM AUTHOR]

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer's energy requirements. In many regions, energy utilization in the housing area is 30%-40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents' actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches. [ABSTRACT FROM AUTHOR]

The expediential increase in Internet communication technologies leads to its expansion to interests beyond computer networks. MEMS (Micro Electro Mechanical Systems) can now be smaller with higher performance, leading to tiny sensors and actuators with enhanced capabilities. WSN (Wireless Sensor Networks) and IoT (Internet of Things) have become a way for devices to communicate, share their data, and control them remotely. Machine-to-Machine (M2M) scenarios can be easily implemented as the cost of the components needed in that network is now affordable. Some of these solutions seem to be more affordable but lack important features, while other ones provide them but at a higher cost. Furthermore, there are ones that can cover great distances and surpass the limits of a Smart Home, while others are more specialized for operation in small areas. As there is a variety of choices available, a more consolidated view of their characteristics is needed to figure out the pros and cons of each of these technologies. As there are a great number of technologies examined in this paper, they are presented regarding their connectivity: Wired, Wireless, and Dual mode (Wired and Wireless). Their oddities are examined with metrics based on user interaction, technical characteristics, data integrity, and cost factor. In the last part of this article, a comparison of these technologies is presented as an effort to assist home automation users, administrators, or installers in making the right choice among them. [ABSTRACT FROM AUTHOR]

• This study proposes an hour-ahead demand response (DR) for PV-based smart homes utilizing the AI method in digital twin while taking into account customer energy consumption and discomfort; • This study adopts a MARL approach for ensuring optimum and decentralized decision-making among several devices; • This study presents a steady cost prediction method utilizing ANN for overcoming the uncertainties in upcoming costs; • This study also compares the energy prices under 2 various scenarios including and excluding AI-enabled DR. Energy management (EM) systems need to have the flexibility to take the optimum real-time decision in the face of constantly varying market factors. Demand response (DR) has become the newest method of improving the performance and reliability of the electrical system. Here, an hour-ahead DR algorithm is proposed for EM at home. This paper presents an artificial neural network approach that uses stable cost predictions as a method for dealing with upcoming price uncertainties. For making optimum and decentralized decisions for various household devices, multi-agent reinforcement learning has been used along with predicted upcoming costs. This paper conducts the simulation with shiftable, non-shiftable, and controllable loads to determine the effectiveness of this suggested EM strategy. Based on the outcomes of the experiments, this suggested DR algorithm has capable of handling EM for a number of devices, minimizing consumer electricity expenses and discomfort prices, and helping the consumer considerably reduce its energy expenses in comparison to a benchmark using no DR. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to identify the product attributes that influence customer satisfaction related to AI-enabled products with a strong forecast of growth. This study is very timely as the market for AI-based smart home products is still at a nascent stage in emerging economies (Vaishali Dar, 2021). Adding evidence to literature on the experience of customers, identifying the attributes that lead to customer satisfaction and those that lead to dissatisfaction are essential for products that were launched recently during the pandemic. This study makes valuable addition to the existing literature. Design/methodology/approach: Online reviews (12,326 reviews) for EchoDot products launched by Amazon India during the pandemic were collected for the study. The attributes were identified based on the most frequently discussed attributes in the text reviews. Further review valence was determined to group the reviews under positive reviews indicating customer satisfaction and negative reviews indicating customer dissatisfaction. Logistic regression was used to identify the attributes that significantly impact customer satisfaction/dissatisfaction. Findings: The results of the study indicate that while EchoDot is seen to add value to the smart home environment (home variable), video (video variable) and speaker (speaker variable) quality also contribute to customer satisfaction. Further, consumers found the product truly worth their money (value for money variable). Other attributes like a complaint about Bluetooth features are seen to increase the probability of customer dissatisfaction. Originality/value: This study identifies the most important attributes that influence customer satisfaction in AI-enabled product sales on e-commerce platforms in pandemic times for India. Such studies are scarce in the emerging markets where the AI-based home appliance market is still in its nascent stage, and the customers are trying out their experiences with these products. What the customers expect and are these products fulfill these expectations is an important question that needs to be addressed by research. This study addresses this gap and adds to this literature. [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]

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]