Usage Of Smart Devices Research Articles

Social impact of decarbonization objectives through smart homes: Survey and analysis

The technological development and economic growth of renewable energy are the key solution for almost full decarbonization of the energy systems. The sustainability and energy efficiency, artificial intelligence and connectivity will be the determining elements towards reaching this ambitious goal. Household appliances and tools for controlling energy consumption, temperature, lighting, etc. In the smart home will play a key role to increase awareness of families' energy consumption. IoT (Internet of Things) devices for the smart home allow us to monitor consumption and pay attention to efficiently energy use. However, savings are not the only important element of the smart home. Together with the digital control of the home these are elements that include a fundamental dimension: decarbonization, which is the cornerstone of the energy transition, which must involve an epochal change of mentality to our society where digitalization and people's well-being meet. The paper deals with social analysis of large cities population perception on integrating renewable energy sources and necessary devices to transform existing houses in smart homes towards economic wellbeing and decarbonization of the energy sector. Different classes of respondents were interviewed – owned and rented fome, rural and urban inhabitants, different segments of age, various number of inhabitants in urban areas and the capital, women and men, high-income and low-income, different levels of education – to support or disregard smart home technological development and reveal the different perception towards further usage of smart devices for daily benefits.

Too Much Is Never Enough: An Analysis of Smart Device Purchase Intention

ABSTRACT This study investigates users’ willingness to purchase additional smart devices to enhance their personal digital ecosystems. Specifically, it delves into how information system continuance intention and media system dependency influence the intention to purchase additional smart devices. Drawing on the assemblage theory, the study also examines the potential moderating effects of the satisfaction level and the number of smart devices already owned. Based on analysis of a sample comprising 400 smart device users, the study reveals that usage continuance intention and dependency have a significant and positive relation to purchase intention. Furthermore, this study sheds light on the positive impact of owning multiple smart devices on these relations. It is noteworthy that among users with a large number of smart devices, the link between continuance intention and purchase intention is significantly stronger when the satisfaction level is low. This finding underscores the potential risk of fostering a form of addiction.

The Varieties of Agency in Human–Smart Device Relationships: The Four Agency Profiles

This paper investigates how users of smart devices attribute agency both to themselves and to their devices. Statistical analyses, tag cloud analysis, and sentiment analysis were applied on survey data collected from 587 participants. As a result of a preliminary factorial analysis, two independent constructs of agency emerged: (i) user agency and (ii) device agency. These two constructs received further support from a sentiment analysis and a tag cloud analysis conducted on the written responses provided in a survey. We also studied how user agency and device agency relate to various background variables, such as the user’s professional knowledge of smart devices. We present a new preliminary model, where the two agency constructs are used to conceptualize agency in human–smart device relationships in a matrix composed of a controller, collaborator, detached, and victim. Our model with the constructs of user agency and device agency fosters a richer understanding of the users’ experiences in their interactions with devices. The results could facilitate designing interfaces that better take into account the users’ views of their own capabilities as well as the capacities of their devices; the findings can assist in tackling challenges such as the feeling of lacking agency experienced by technologically savvy users.

Assessment of ownership of smart devices and the acceptability of digital health data sharing

Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18–25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

Smart Devices in Optometry: Current and Future Perspectives to Clinical Optometry.

There is a huge unmet need for eye care with more than a hundred million people living without basic eye care services and facilities. There is an exigency to deploy adequate resources in terms of manpower and equipment to address this. The usage of smart devices in optometry and eye care practice has been gaining momentum for last half a decade, due to the COVID-19 pandemic and technological advancements in telemedicine. These smart devices will help facilitate remote monitoring of important visual functions, ocular signs and symptoms, thus providing better eye care services and facilities and promoting outreach services. Smart devices in optometry exist in the form of gadgets that can be worn in the wrist, and spectacle-mounted or head-mounted devices. On the other hand, with the ubiquitous nature of smartphones, a large number of smartphone applications have been developed and tested for advanced optometry and primary eye care practice, which may potentially reduce the burden of inadequate resources and the unmet need for eye care. This article aims to give an overview of the current trends and future perspectives on the application of such smart devices in optometric practice.

Prevalence of internet addiction and anxiety, and factors associated with the high level of anxiety among adolescents in Hanoi, Vietnam during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic and the resulting isolation measures created an increase in the usage of smart devices and internet among adolescents. This study aims to estimate the prevalence of internet addiction, the prevalence of high level of anxiety as well as to examine factors associated with the high level of anxiety among adolescents in Hanoi, Vietnam during the COVID-19 pandemic.MethodData was collected using respondent-driven sampling and Google online survey forms from a sample of 5,325 school students aged 11–17 in Hanoi between October and December 2021. A short scale consisting of 5 items was used to measure internet addiction and the GAD-7 was used to measure adolescent anxiety level.ResultsThe findings revealed that 22.8% and 7.32% of adolescents experienced moderate and severe anxiety. About 32.7% of the study sample exhibited at least three internet addiction indicators. Logistic regression analysis identified significant predictors for high levels of adolescent anxiety. Being female, family experiencing economic difficulties, and exposure to domestic violence were associated with higher risk of anxiety disorder (OR 1.78, 1.45, and 2.89, respectively). Both average daily online time and internet addiction demonstrated gradient association with high level of anxiety.ConclusionThe prevalence of internet addiction and high level of anxiety were high among adolescents in Hanoi, Vietnam during the COVID-19 pandemic. The study highlights the importance of implementing measures at the family and school levels to promote a balanced and healthy approach to smart device use among adolescents.

Towards cyber awareness among smart device users: an interactive, educational display of IoT device vendors compromise history

Towards cyber awareness among smart device users: an interactive, educational display of IoT device vendors compromise history

Air pollution may increase the sleep apnea severity: A nationwide analysis of smart device-based monitoring

Obstructive sleep apnea (OSA) can lead to sleep deprivation, accidents, and cardiovascular diseases. However, research on the short-term effects of air pollutants on OSA severity is limited and inconsistent. We conducted a novel case time series analysis using a nationwide dataset among Huawei smart device users to assess the association between air pollution and OSA severity in a population at moderate-to-severe risk of OSA. Fixed-effects regression models were used to assess the associations between air pollution and the risk of OSA exacerbation, apnea-hypopnea index (AHI), and oxygen saturation. A total of 51,842 participants who were at moderate-to-severe risk of OSA (mean age [SD]: 45.4 [11.0], 95.5% male) were included, with 6,232,056 person-days of monitoring. The associations of fine particulate matter, nitrogen dioxide, carbon monoxide, and sulfur dioxide with OSA severity could occur during the sleep period, and last for 2days. An increase of 1 interquartile range in the moving average concentrations of air pollution during the sleep period and the 2 previous days was associated with a 1.14%-4.31% increase in the risk of OSA exacerbation, an increase in AHI by 0.05-0.17 events/h, and a decrease in oxygen saturation (%) by 0.003-0.014. The exposure-response curves were almost linear. The associations between air pollutants and OSA were consistently stronger in participants aged 45 years or older. By virtue of the smart device-based technology, this large-scale, nationwide, longitudinal study provides compelling evidence that short-term exposure to air pollution may worsen sleep apnea. Our findings highlight the significance of ongoing efforts to improve air quality in mitigating OSA severity and the relevant disease burden in an aging era.

Human Activity Recognition with Smartwatch Data by using Mahalanobis Distance-Based Outlier Detection and Ensemble Learning Methods

Recognition of human activities is part of smart healthcare applications. In this context, the detection of human actions with high accuracy has been a field that has been working for many years. With the increase in the usage of smart devices, smartphones and smartwatches have become the constant equipment of these studies thanks to their internal sensors. Sometimes abnormal data are included in data sets due to the way the data were collected and for reasons arising from the sensors. For this reason, it becomes important to detect outlier data. In this study, step counter and heart rate sensors were used in addition to an accelerometer and gyroscope in order to detect human activities. Afterward, the outliers were detected and cleared with a Mahalanobis distance-based approach. With the aim of achieving a better classification performance, machine learning methods were used by strengthening them with ensemble learning methods. The obtained results showed that step counter, heart rate sensors, and ensemble learning methods positively affect the success of the classification. In addition, it was found that the Mahalanobis distance-based outlier detection method increased the classification accuracy significantly.

취침 전 스마트기기 사용 감소를 위한 디자인 개입 연구

Due to the rapid proliferation of various smart devices in recent times, the usage of smart devices has been increasing rapidly. Particularly, the time spent using smart devices before bedtime constitutes a significant portion of overall smart device usage. Excessive use of smart devices before bedtime has negative effects on physical, mental, and social aspects. Therefore, in this study, we propose methods for bringing a behavior change based on behavioral theory, going beyond technology-based physical interventions, with the aim of reducing the usage time of smart devices before sleep. To achieve this, we designed two types of design-led interventions based on the feedback and feedforward concepts of behavioral theory and conducted a three-week long experiment. These proposed interventions were provided to the participants in each experimental group at a specific time (before going to bed). Based on the differences between the two groups, the study discussed the effectiveness of design-led interventions for instigating sustainable behavior change. This study holds significance as it offers practical guidance essential for the development of models and applications that may effectively utilise this particular design strategy.

Network management schemes for IoT environment towards 6G: A comprehensive review

Usage of smart devices, expansion of Internet of Things (IoT) network, and its adoption in variety of applications grows exponentially day-by-day. It leads to various issues such as network privacy, security management, unreliable network management (NM) scheme, resource utilization, energy utilization, decentralization, transparency, and data interoperability. Development of the NM platform/framework for secure data dissemination in IoT networks is tedious due to heterogeneity and imbalanced IoT protocol stack. Recently, traditional network scheduling methods, Software Defined Networks (SDN), Network Function Virtualization (NFV), and hybrid approaches are widely used for managing the IoT network. But, most of these approaches are controlled by a centralized server, which may not be appropriate due to the heterogeneity of the IoT devices. Motivated from the aforementioned discussion, In this paper, we present a systematic survey on various NM schemes used to secure data dissemination for the IoT environment. We present a detailed taxonomy of all the existing NM schemes for IoT networks. Pros and cons of the traditional NM methods applicable for IoT environment are discussed in detail. The paper discusses a state-of-art survey on integrating blockchain (BC) technology in the existing IoT NM techniques. In this paper, our prime focus is to understand the potential of BC for enabling secured IoT management. Finally, a case study on BC implication for Unmanned Aerial Vehicle (UAV) network using 6G is also discussed along with future directions.

Rectifying Skewed Kernel Page Reclamation in Mobile Devices for Improving User-Perceivable Latency

A crucial design factor for users of smart mobile devices is the latency of graphical interface interaction. Switching a background app to foreground is a frequent operation on mobile devices and the latency of this process is highly perceivable to users. Based on an Android smartphone, through analysis of memory reference generated during the app-switching process, we observe that file (virtual) pages and anonymous pages are both heavily involved. However, to our surprise, the amounts of the two types of pages in the main memory are highly imbalanced, and frequent I/O operations on file pages noticeably slows down the app-switching process. In this study, we advocate to improve the app-switching latency by rectifying the skewed kernel page reclaiming. Our approach involves two parts: proactive identification of unused anonymous pages and adaptive balance between file pages and anonymous pages. As mobile apps are found inflating their anonymous pages, we propose identifying unused anonymous pages in sync with the app-switching events. In addition, Android devices replaces the swap device with RAM-based zram, and swapping on zram is much faster than file accessing on flash storage. Without causing thrashing, we propose swapping out as many anonymous pages to zram as possible for caching more file pages. We conduct experiments on a Google Pixel phone with realistic user workloads, and results confirm that our method is adaptive to different memory requirements and greatly improves the app-switching latency by up to 43% compared with the original kernel.

Exploring the usage intention of AI-powered devices in smart homes among millennials and zillennials: the moderating role of trust

PurposeThis study aims to investigate how artificial intelligence (AI)-powered smart home devices affect young consumers' requirements for convenience, support, security and monitoring, as well as their ability to advance environmental sustainability. This study also examines the variables that impact users' motivation to use AI-powered smart home devices, such as perceived value, ease of use, social presence, identity, technology security and the moderating impact of trust.Design/methodology/approachThe responses from residents of Sarawak, Malaysia, were collected through online questionnaires. This study aimed to examine the perceptions of millennials and zillennials towards their trust and adoption of AI-powered devices. This study used a quantitative approach, and the relationships among the study constructs were analysed using partial least squares - structural equation modelling.FindingsThe present study found that perceived usefulness, ease of use and social presence were the main motivators among actual and potential users of smart home devices, especially in determining their intentions to use and actual usage. Additionally, there was a moderating effect of trust on the relationship between perceived ease of use, social presence, social identity and intention to use AI-powered devices in smart homes.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies to examine the factors influencing smart technology adoption. This study provided meaningful insights on the development of strategies for the key stakeholders to enhance the adoption and usage of AI-powered smart home devices in Sarawak, one of the promising Borneo states. Additionally, this study contributed to the growing body of knowledge on the associations between technology acceptance model dimensions, intention and actual usage of smart technology, with the moderating impact of trust.

Identifying and Navigating the Current Trends in Business Librarianship and Data Librarianship

These trends in business librarianship and data librarianship matter for the management of today’s academic libraries and this topic is important to discuss because librarians must respond to the developments in data science and big data. Industry leaders such as Yuanqing Yango, CEO of Lenovo refer to “new IT” and the coming revolution stemming from the usage of smart devices, edge and cloud computing, 5G networks, and (AI) Artificial Intelligence (Lenovo, 2022). Lenovo (2022) researchers undertook a study of 500 Chief Technology Officers (CTOs)from diverse industries to ascertain their perceptions about the future of technology. Both scholars and industry leaders alike agree that the technologies that will dominate will be forged so that humanity can meet the challenges of the future and the control of information will be at the forefront of these changes. Information professionals must learn about and master the technologies that industry leaders are reimagining as innovations that will try to improve our lives because librarianship is becoming increasingly data-driven. Faculty, staff, and students rely on information professionals to help them to understand the role of “new IT” and the opportunities that it creates. We also need more informed professionals because research is data-driven. More decision makers are using big data to make effective organizational decisions. Librarians must be cognizant of the trends that are governing innovations in technology to effectively provide information services to key stakeholders.

Prevalence of impaired upper extremity neural mobility among smart device users during the COVID-19 pandemic.

Both the development of various countries and people's health were impacted by the unforeseen global COVID-19 outbreak. Many countries prefer to do their daily business online. Although it was incredibly useful at the time, it still had a problem that was not properly addressed, especially among the student population. The objective of this study was to examine the prevalence of upper extremity neural mobility among students who were using smart devices during the COVID-19 pandemic. 458 students who had previously taken home-based online classes during the COVID-19 pandemic and used a smart device for more than six hours were included in this study. The study was conducted in three phases. After being examined in the first two stages of the study, a total of 72 people were chosen for the final phase. Peripheral nerve mobility tests were performed on these 72 subjects. This study observed that 15.72% of smart device users have been affected by forward neck posture and impaired peripheral nerve mobility in the cervical spine. The study concludes that forward neck posture is associated with impaired peripheral nerve mobility among smart device users who were in home-based online classes during the COVID-19 pandemic lockdown. Hence, we suggest an appropriate treatment strategy focusing on preventing the forward neck posture through timely analysis and self-care therapies.

Enhancing the design of data-related privacy controls for smart home devices

PurposePast research shows that users of smart home devices (SHDs) have privacy concerns. These concerns have been validated from technical research that shows SHDs introduce a lot of privacy risks. However, there is limited research in addressing these concerns and risks. This paper aims to bridge this gap by informing the design of data-related privacy controls for SHDs.Design/methodology/approachIn this paper, the authors follow a user-centered design approach to design data-related privacy controls from design requirements backed by literature. The authors test the design for usability and perceived information control using psychometrically validated scales. For this purpose, two variations of the prototype (MyCam1 with a listing of data-related privacy controls and MyCam2 with three privacy presets) were created and tested them in a between-subjects experimental setting. Study participants (n = 207) were recruited via Mechanical Turk and asked to use the prototype app. An online survey was distributed to the participants to measure some usability and privacy-related constructs.FindingsFindings show that the presented prototype designs were usable and met the privacy control needs of users. The prototype design with privacy presets (MyCam2) was found to be significantly more usable than the list of privacy controls (MyCam1).OriginalityThe findings of this paper are original and build on the paper presented at the International Symposium on Human Aspects of Information Security and Assurance (HAISA 2022). This paper contributes improved and usable designs of privacy controls for smart home applications.

A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s life and are being actively used in several verticals, such as transportation, healthcare, and smart homes. IoT devices, which usually are resource-constrained, often need to communicate with other devices, such as fog nodes and/or cloud computing servers to accomplish certain tasks that demand large resource requirements. These communications entail unprecedented security vulnerabilities, where malicious parties find in this heterogeneous and multiparty architecture a compelling platform to launch their attacks. In this work, we conduct an in-depth survey on the existing intrusion detection solutions proposed for the IoT ecosystem which includes the IoT devices as well as the communications between the IoT, fog computing, and cloud computing layers. Although some survey articles already exist, the originality of this work stems from the three following points: 1) discuss the security issues of the IoT ecosystem not only from the perspective of IoT devices but also taking into account the communications between the IoT, fog, and cloud computing layers; 2) propose a novel two-level classification scheme that first categorizes the literature based on the approach used to detect attacks and then classify each approach into a set of subtechniques; and 3) propose a comprehensive cybersecurity framework that combines the concepts of explainable artificial intelligence (XAI), federated learning, game theory, and social psychology to offer future IoT systems a strong protection against cyberattacks.

Factors influencing smart device addiction among preschool children: An extended protection-risk model perspective.

Today, users of smart devices are from increasingly younger generations, and their use is very common among preschoolers. The problem of smart device addiction in preschool children has attracted widespread attention, due to which this study explores the influencing factors in children aged 2-5 years. Based on the protection-risk model, 236 Chinese parents were surveyed, and the data was analyzed using partial least squares structural equation modeling. The findings show that parental emotion regulation significantly and negatively influences children's depression and social withdrawal symptoms, whereas it has a significant and positive influence on parental self-control as well as outdoor intention. Also, children's depression and social withdrawal symptoms have a significant and positive influence on their smart device addiction, whereas parental self-control and outdoor intentions have no significant influence on it. Moreover, children's social withdrawal and depression have a mediating effect between parental emotion regulation and children's smart device addiction, but parental self-control and outdoor intention have no mediating effect between the two. This study identifies the factors influencing children's smart device addiction from a new perspective, providing theoretical support to address this problem of addiction.

IoT and Fog-Computing-Based Predictive Maintenance Model for Effective Asset Management in Industry 4.0 Using Machine Learning

The assets in Industry 4.0 are categorized into physical, virtual, and human. The innovation and popularization of ubiquitous computing enhance the usage of smart devices: RFID tags, QR codes, LoRa tags, etc., for asset identification and tracking. The generated data from the Industrial Internet of Things (IIoT) ease information visibility and process automation in Industry 4.0. Virtual assets include the data produced from IIoT. One of the applications of the industrial big data is to predict the failure of the manufacturing equipment. Predictive maintenance enables the business owner to decide, such as repairing or replacing the component before an actual failure that affects the whole production line. Therefore, Industry 4.0 requires an effective asset management to optimize the task distributions and predictive maintenance model. This article presents the genetic algorithm (GA)-based resource management integrating with machine learning for predictive maintenance in fog computing. The time, cost, and energy performance of GA along with MinMin, MaxMin, FCFS, and RoundRobin are simulated in the FogWorkflowsim. The predictive maintenance model is built in two-class logistic regression using real-time data sets. The results demonstrate that the proposed technique outperforms MinMin, MaxMin, FCFS, RoundRobin in execution time, cost, and energy usage. The execution time is 0.48% faster, 5.43% lower cost and energy usage is 28.10% lower in comparison with second-best results. The training and testing accuracy of the prediction model is 95.1% and 94.5%, respectively.

Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems

The information obtained from external sources within the cloud and the resulting computations are not always reliable. This is attributed to the absence of tangible regulations and information management on the part of the information owners. Although numerous techniques for safeguarding and securing external information have been developed, security hazards in the cloud are still problematic. This could potentially pose a significant challenge to the effective adoption and utilization of cloud technology. In terms of performance, many of the existing solutions are affected by high computation costs, particularly in terms of auditing. In order to reduce the auditing expenses, this paper proposes a well-organised, lightweight system for safeguarding information through enhanced integrity checking. The proposed technique implements a cryptographic hash function with low-cost mathematic operations. In addition, this paper explores the role of a semi-trusted server with regard to smart device users. This facilitates the formal management of information prior to distribution through the IoT-cloud system. Essentially, this facilitates the validation of the information stored and exchanged in this environment. The results obtained show that the proposed system is lightweight and offers features such as a safeguarding capability, key management, privacy, decreased costs, sufficient security for smart device users, one-time key provision, and high degree of accuracy. In addition, the proposed method exhibits lower computation complexity and storage expenses compared with those of other techniques such as bilinear map-based systems.