Smart Home Devices Research Articles

Advanced Deep Learning Approach for Smart Home Appliance Identification Using Recurrent Neural Networks with LSTM

In the Internet of Things (IoT) domain, vast numbers of smart devices are interconnected, generating large volumes of data requiring advanced management mechanisms. One major challenge in smart environments is the ability to accurately distinguish and categorize the various types of objects within these systems. To address this issue, the study introduces a recurrent neural network (RNN) model designed for classifying data from smart home devices. Using a dataset from Kaggle, the research outlines the processes of data collection, loading, normalization, and model development. The RNN, enhanced with long short-term memory (LSTM) layers, was trained and evaluated, showing notable improvements in training and validation accuracy over ten epochs. The model achieved a test accuracy of 83.25%, a loss of 35.4%, a precision of 85%, and a recall of 81%. The evaluation of the model on the test set includes a detailed analysis using ROC curves, area under the curve (AUC) scores for multi-class classification, and a confusion matrix. With an AUC score of 0.9896, the model demonstrated exceptional performance in accurately classifying IoT device categories. These results suggest that the LSTM-equipped RNN offers strong learning efficiency and generalization, making it a highly suitable approach for IoT device classification. Additionally, the article explores the concept of IoT and reviews recent advancements in using deep learning models across various IoT sectors, including smart homes, industrial systems, and healthcare. Future research could aim to improve the model’s real-time processing abilities and scalability and incorporate a wider variety of IoT data types to enhance its practical applications and expand its utility across more IoT environments.

Global Visualization and Contemporary Movements of Smart Home Devices: A Bibliometric Analysis

Global Visualization and Contemporary Movements of Smart Home Devices: A Bibliometric Analysis

Secure Search of Smart Home Data Based on Access Control Encryption

Abstract: In recent years, the increasing variety of smart home devices, such as smart locks, surveillance cameras, and various sensors, has led to a significant rise in related data. To facilitate the sharing of smart home data, manufacturers store these data on the Internet of Things (IoT). However, since IoT servers are not fully trustworthy and the data often contain sensitive commercial information, any leakage could result in a commercial crisis for manufacturers. To address security concerns, manufacturers encrypt the data before storing them on the IoT. A challenge arises: how can users search for target data in encrypted form? This paper proposes a new search encryption scheme for smart home data, addressing the limitations of existing attribute-based encryption (ABE) searchable encryption schemes. The proposed scheme integrates access control trees and bilinear mapping technology, supporting multiple access policies including AND gates, OR gates, and threshold gates. Experimental results demonstrate a comprehensive analysis of the attribute-based searchable encryption protocol under a multi-authority architecture, covering four dimensions: correctness, security, complexity, and real-world application scenarios. In specific scenarios, when a data user, out of privacy concerns, is unwilling to reveal all attribute information to a single authority during a query for shared datathus avoiding identity disclosureor when the users identity information is managed by multiple independent authorities, this protocol proves highly effective. It ensures smooth data sharing while maintaining data security for both data owners and users, fully safeguarding data privacy and shareability.

“They Didn’t Buy Their Smart TV to Watch Me with the Kids”: Comparing Nannies’ and Parents’ Privacy Threat Models for Smart Home Devices

Smart home devices raise privacy concerns among not only primary users, but also bystanders like domestic workers. We conducted 25 qualitative interviews with nannies and 16 with parents who employed nannies, in the US, to explore and compare their views on and privacy threat models for smart home devices. We found device-specific purposes of use inspired different perspectives among nanny participants. Most were comfortable with employers’ smart speakers and smart TVs, whose purpose had nothing to do with them. However, with indoor smart cameras, nanny participants were often not just bystanders but targets of monitoring; in such situations, they had a wider range of attitudes. In contrast, parent participants tended to have more similar views across devices. We found notable disconnects regarding disclosure, where nanny participants often hesitated to ask about cameras, but parent participants assumed nannies just didn’t care. We recommend prioritizing interventions supporting disclosure, discussion, and sharing control.

Deep learning approaches for protecting IoT devices in smart homes from MitM attacks

The primary objective of this paper is to enhance the security of IoT devices in Software-Defined Networking (SDN) environments against Man-in-the-Middle (MitM) attacks in smart homes using Artificial Intelligence (AI) methods as part of an Intrusion Detection and Prevention System (IDPS) framework. This framework aims to authenticate communication parties, ensure overall system and network security within SDN environments, and foster trust among users and stakeholders. The experimental analysis focuses on machine learning (ML) and deep learning (DL) algorithms, particularly those employed in Intrusion Detection Systems (IDS), such as Naive Bayes (NB), k-Nearest Neighbors (kNN), Random Forest (RF), and Convolutional Neural Networks (CNN). The CNN algorithm demonstrates exceptional performance on the training dataset, achieving 99.96% accuracy with minimal training time. It also shows favorable results in terms of detection speed, requiring only 1 s, and maintains a low False Alarm Rate (FAR) of 0.02%. Subsequently, the proposed framework was deployed in a testbed SDN environment to evaluate its detection capabilities across diverse network topologies, showcasing its efficiency compared to existing approaches.

Values as causes of emotions and acceptability in the digital risk context: an extension of the values scale with privacy

Values have been proposed to be important predictors of emotions about products, which in turn have been proposed to be associated with acceptability of these products. While support for this theory has been found in the environmental domain, it has not been studied in other domains, such as the domain of digital risks. The main objective of this study was to examine if three values (hedonism, personal security, and privacy) are predictors of emotions about a smart home device (a smart speaker), and if, in turn, those emotions influence the acceptability of the device (operationalized as adoption attitude). Additional objectives were to test whether the relation between risk-related values and emotions strengthens when people receive a value-tailored warning, and whether privacy as a value is a valuable addition to the 19 value scale by Schwartz and colleagues. With a scenario study (N = 623), we found, first, that privacy as a value could indeed be distinguished from the other values. Second, the values hedonism and privacy, but not personal security, predicted either or both positive and negative emotions towards a smart device, and in turn the acceptability of the smart device. Third, the effects of the values privacy and personal security on emotions were not strengthened by a value-tailored warning. Fourth, two additional values were identified that also explained emotions and in turn acceptability of the smart device, being power-resources and tradition. Fifth, privacy was found to be a valuable addition to the 19 value measurement, which explained emotions over and above the other 19 values. The study thus shows that values are also important predictors of emotions and in turn acceptability in the context of digital risks, with privacy as an important newly developed value that contributes to a more in-depth understanding of consumer decision making in this context.

Prediction of the Future Trends of Housing Project Development with Modified Delphi Technique

This research was initiated to study the trend of the housing projects in the next ten years (2022-2031), with the applied Modified Delphi Technique. The research was conducted by interviewing five experts with more than ten years of knowledge and experience in design, construction, technology, location, and marketing. The gathered trends were applied to create questionnaires in order to conduct two rounds of surveys to assess the consensus level of ten experts with at least ten years of experience in the real estate industry, according to the consensus analysis method from the Modified Delphi Technique. Thirteen trends were found from the study. There are four design trends: more beautiful façade, soundproof wall, universal design furniture, and universal design house building. In addition, eco-friendly materials and modular construction systems were found to be construction trends while smart home device installation and active air flow technology were found to be technology trends. Regarding the trend of the project location, the projects tend to be located in areas expanding into the suburbs. Finally, four marketing trends were also reported: Virtual Reality utilization in sales presentation, advertising to reach more customers, inviting social media influencers to play a role in reviewing projects, providing credit consulting services and refunding of reservation money if a customer’s loan is not approved. The results of the study provide information to all parties involved in planning strategies and operating a business in developing housing estates in the future.

"My Best Friend's Husband Sees and Knows Everything": A Cross-Contextual and Cross-Country Approach to Understanding Smart Home Privacy

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

Optimal economic operation of smart home devices based on home microgrids and time-of-use tariffs

Abstract In this paper, a two-layer model is proposed for smart devices in a home energy management system in order to optimize residential energy use under time-of-use tariffs and home microgrids. Home smart devices are modelled based on their power consumption attributes and building thermal resistance. An HVAC-transferable device cooperative operation strategy is introduced to construct the economic optimization of the upper-level HVAC operation and the lower-level transferable device operation. This strategy reduces home power costs while maintaining user comfort. By iteratively solving the two-layer model, the optimal power consumption scheme for home smart devices is determined.

Anomaly detection and prediction of energy consumption for smart homes using machine learning

AbstractAs technology advances, smart homes are being increasingly adopted, thus generating massive data that pose new research challenges. We propose a machine learning framework for monitoring energy consumption in smart home devices. The proposed framework involves an anomaly detection module, followed by a predictive model to forecast energy consumption patterns in a typical smart home. We employ three outlier‐based techniques for anomaly detection: (1) local outlier factor, (2) connectivity‐based outlier factor, and (3) cluster‐based local outlier factor. Furthermore, we apply random forest, linear regression, decision tree, and the ensemble techniques of adaptive, gradient, and extreme gradient boosting to anomaly free data to develop baseline models that predict the energy consumption patterns of smart home devices. The framework is evaluated on three publicly available energy datasets collected from various smart homes. The experimental results reveal that the cluster‐based local outlier factor with extreme gradient boosting achieves promising results with high prediction accuracy.

An adaptive approach for integrating primary user behavior in compressive spectrum sensing

Compressive sensing (CS) has been widely used to sense the wideband spectrum with fewer measurements by taking advantage of radio spectrum underutilization. As new smart devices, such as IoT devices, smart home devices, and wearables, use batteries and have limited memory, more research is needed to reduce the overuse of cognitive radio (CR) resources through spectrum sensing. To reduce the number of compressive measurements required for spectrum recovery, researchers proposed approaches like weighted and sequential compressive sensing. In this paper, we estimate the primary user’s (PU) behavior statistics and use the estimated information in a novel weighted sequential compressive spectrum sensing approach. Our proposed approach can reduce and adapt the number of measurements and the sensing time to the changing number of active channels in a dynamically changing wideband spectrum.

Optimizing radiology remote reading: leveraging technology to improve efficiency.

Remote work has been increasingly utilized in the profession of radiology over recent years. Setting up your individual workstation offers an opportunity to tailor it to suit your preferences without the restriction of a universal setup to accommodate multiple users. Important considerations when setting up a home workstation include selecting the optimal work location, choosing the proper desk and chair, and configuring an ideal computer monitor layout. The use of peripheral devices, such as programmable mice and hands-free dictation tools can improve efficiency and reduce repetitive strain injuries. This article also explores the use of smart home devices and programmable scripts using AutoHotKey to further streamline workflow and maximize the benefits of a remote workstation.

User-Centric Design in Mobile Application Development for Smart Home Devices

User-Centric Design in Mobile Application Development for Smart Home Devices

Review of Smart-Home Security Using the Internet of Things

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

Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model

ABSTRACTThe Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain‐based deep learning model for IDS. Initially, the input data are preprocessed using min‐max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM‐FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM‐FCVAE model is analyzed by evaluating several metrics. The proposed GM‐FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW‐NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM‐FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.

Analysis of Wireless Security and Networks using COPRAS Method

The wireless networks have become integral to our daily lives, powering everything from smart phones to smart home devices. However, alongside their benefits, wireless networks also bring forth significant security challenges. Wireless networks, unlike traditional wired networks, transmit data over the airwaves, making them susceptible to various forms of unauthorized access and data breaches. Ensuring the security of these networks is crucial to safeguard sensitive information, protect user privacy, and prevent cyber-attacks. The significance of research in wireless security and networks lies in the critical role that wireless technologies play in our interconnected world. As wireless networks become increasingly prevalent in various sectors, including communication, commerce, healthcare, transportation, and more, ensuring their security becomes paramount. Research in wireless security and networks contributes to the advancement of secure and reliable wireless technologies, protecting users' privacy, data, and overall well-being in an increasingly connected world. The COPRAS-G method requires identifying selection criteria, evaluating information related to these criteria, and developing methods to evaluate Meeting the participant's needs Criteria for doing in order to assess the overall performance of the surrogate. Decision analysis involves a Decision Maker (DM) Situation to do consider a particular set of alternatives and select one among several alternatives, usually with conflicting criteria. For this reason, the developed complexity proportionality assessment (COPRAS) method can be used. A genetic algorithm; Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE). Detection Accuracy, Resource Efficiency; Implementation Complexity; Time-to-Deployment. From the result Differential Evolution (DE) is got the first rank whereas is Ant Colony Optimization (ACO) is having the lowest rank

PENGENDALI PINTAR DALAM PENGONTROLAN LAMPU DAN KIPAS MENGGUNAKAN BLUETOOTH

The evolution of mobile technology has revolutionized it from a mere communication tool to a versatile platform capable of handling images, videos, documents, and controlling other devices such as TVs. The Android OS gives developers the flexibility to create applications that meet the diverse needs of users. This shift has made smartphones indispensable, supporting tasks from browsing to entertainment and productivity. The popularity of Android ensures seamless integration with existing hardware for efficient application performance. This research aims to design a smart home device control system, focusing on lights and fans via Android on an Arduino Nano microcontroller. Android's affordability and development through third-party applications make it ideal for smart home systems. Integrating the Arduino Nano and the EMS Blue Shield Bluetooth module improves convenience and energy efficiency. Its limitations include control of basic functions (lights, fan speed) and Android smartphones with Bluetooth. This study aims to design a practical control system and understand the device operation and Android communication. The test results of data transmission response time (latency) show that the average response time of the system for five days is 122.3 ms, with small variations and standard deviations between 2.81 ms and 3.92 ms. The range of response time ranged from 118 ms to 130 ms.

In smartness we trust: consumer experience, smart device personalization and privacy balance

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

Software-Defined-Networking-Based One-versus-Rest Strategy for Detecting and Mitigating Distributed Denial-of-Service Attacks in Smart Home Internet of Things Devices.

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

Comparative Study of Microcontoller: ARDUINO UNO, RASPBERRY PI 4, ESP 32

Abstract: This paper provides a comparative study of three popular microcontrollers: Arduino Uno, Raspberry Pi 4, and ESP32. Arduino Uno is known for its simplicity and low power consumption, making it ideal for educational and small-scale projects. Raspberry Pi 4 offers high computational power and extensive connectivity, suitable for IoT gateways, media centers, and industrial automation. ESP32 combines robust wireless connectivity with efficient processing, perfect for IoT applications, smart home devices, and wearable electronics. The study evaluates their technical specifications, performance, development environments, and use cases, helping developers choose the best microcontroller for their projects