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Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement.

A smart device unlocking scheme based on ultrasonic gesture recognition is proposed, allowing users to unlock their devices by customizing the unlock code through gesture movements. This method utilizes ultrasound to detect multiple consecutive gestures, identifying micro-features within these gestures for authentication. To enhance recognition accuracy, an unsupervised segmentation algorithm is employed to accurately segment the gesture feature region and extract the time-frequency domain data of the gestures. Additionally, two-stage data enhancement techniques are applied to generate user-specific data based on a small sample size. Finally, the user-specific model is deployed to mobile devices via transfer learning for on-device, real-time inference. Experimental validation on a commercial smartphone (Redmi K50) demonstrates that the entire authentication pipeline, from signal acquisition to decision, processes 8 types of gestures in a sequence in sequence in approximately 1.2 s, with the core model inference taking less than 50 milliseconds. This ensures that the raw biometric data (ultrasonic echoes) and the recognition results never leave the user’s device during authentication, thereby safeguarding privacy. It is important to note that while model training is performed offline on a server to leverage greater computational resources for personalization, the deployed system operates fully in real time on the edge device. Experimental results demonstrate that our system achieves accurate and robust identity verification, with an average five-fold cross-validation accuracy rate of up to 93.56%, and it shows robustness across different environments.

PurposeThe Internet of Things (IoT) platform empowers the digital transformation of the manufacturing industry by providing information technology services. Simultaneously, it enters the market by offering smart products to consumers. In light of different service fee scenarios, this article explores the optimal decision-making for the platform. It investigates the pricing models and entry decisions of IoT platforms.Design/methodology/approachIn this study, we have formulated a game-theoretic model to scrutinize the influence of the IoT platform ventured into the smart device market on the pre-existing suppliers operating under subscription-based and usage-based pricing agreements.FindingsOur outcome shows that introducing an IoT platform’s smart device has a differential effect on manufacturers depending on their contract type. Notably, our research indicates that introducing the platform’s own smart device within the subscription-based model does not negatively impact the profitability of incumbent manufacturers, so long as there is a noticeable discrepancy in the quality of the smart devices. However, our findings within the usage-based model demonstrate that despite the variance in smart device quality differentiation, the platform’s resolution to launch their device and impose their pricing agreements adversely affects established manufacturers. Additionally, we obtain valuable Intel regarding the platform’s entry strategies and contractual inclinations. We demonstrate that the platform is incentivized to present its smart device when reasonable entry costs remain. Furthermore, the platform prefers subscription-based contracts when the subscription fee is relatively high in non-platform entry and entry cases.Originality/valueThese findings hold significant practical implications for firms operating in an IoT-based supply chain.

- The paper presents a smart home type network structure application which was studied and simulated as close as possible to the reality. The Cisco Packet Tracer program was used, with the help of which smart devices, sensors, were configured, that control the safe operation of key components of the smart home. The operation of smart devices was highlighted by customizing some decision scripts and algorithms. The simulation results show the values of various quantities through which the good management of the smart home is monitored.

Smart speakers, such as Amazon's Echo and Google's Nest Home, combine natural language processing with a conversational interface to carry out everyday tasks, like playing music and finding information. Easy to use, they are embraced by older adults, including those with limited physical function, vision, or computer literacy. While smart speakers are increasingly used for research purposes (eg, implementing interventions and automatically recording selected research data), information on the advantages and disadvantages of using these devices for studies related to health promotion programs is limited. This study evaluates the feasibility and acceptability of using smart speakers to deliver a physical activity (PA) program designed to help older adults enhance their physical well-being. Community-dwelling older adults (n=18) were asked to use a custom smart speaker app to participate in an evidence-based, low-impact PA program for 10 weeks. Collected data, including measures of technology acceptance, interviews, field notes, and device logs, were analyzed using a concurrent mixed analysis approach. Technology acceptance measures were evaluated using time series ANOVAs to examine acceptability, appropriateness, feasibility, and intention to adopt smart speaker technology. Device logs provided evidence of interaction with and adoption of the device and the intervention. Interviews and field notes were thematically coded to triangulate the quantitative measures and further expand on factors relating to intervention fidelity. Smart speakers were found to be acceptable for administering a PA program, as participants reported that the devices were highly usable (mean 5.02, SE 0.38) and had strong intentions to continue their use (mean 5.90, SE 0.39). Factors such as the voice-user interface and engagement with the device on everyday tasks were identified as meaningful to acceptability. The feasibility of the devices for research activity, however, was mixed. Despite the participants rating the smart speakers as easy to use (mean 5.55, SE 1.16), functional and technical factors, such as Wi-Fi connectivity and appropriate command phrasing, required the provision of additional support resources to participants and potentially impaired intervention fidelity. Smart speakers present an acceptable and appropriate behavioral intervention technology for PA programs directed at older adults but entail additional requirements for resource planning, technical support, and troubleshooting to ensure their feasibility for the research context and for fidelity of the intervention.

Background The increase in smart device use, including smartphones and tablets, among young children has raised concerns about its impact on health, particularly on body mass index (BMI). However, the bidirectional associations between smart device use and BMI in preschoolers remain unclear. This study examined the longitudinal associations, considering the moderating effects of mother-child interactions and child sex. Methods Data were obtained from the Longitudinal Examination Across Prenatal and Postpartum Health in Taiwan, a cohort study conducted in Taipei, Taiwan. In total, 590 preschoolers were assessed at ages 3, 4, and 5 years. Smart device use, BMI z-scores, and mother-child interaction quality were evaluated using validated parent-reported questionnaires. The random-intercept cross-lagged panel model was used to investigate bidirectional associations, adjusting for stable confounders. Multiple-group models examined the moderating effects of mother-child interactions and child sex. Model estimates were reported as standardized coefficients. Results Higher BMI z-scores at age 4 years were linked to increased device use at age 5 years (β = 0.36; 95% CI, 0.05–0.67). Multiple-group models revealed that among dyads with lower mother-child interactions, higher device use at age 3 years was associated with higher BMI at age 4 years (β = 0.40; 95% CI, 0.07 to 0.72), which was subsequently linked to greater device use at age 5 years (β = 0.50; 95% CI, 0.10 to 0.90). Additionally, higher device use at age 4 years was associated with higher BMI at age 5 years (β = 0.65; 95% CI, 0.31 to 1.00). A similar bidirectional pattern was observed among boys, while no significant cross-lagged associations were found among girls. In contrast, high-quality mother-child interactions revealed higher device use at age 4 years was associated with lower BMI at age 5 years, suggesting a protective role against prolonged device use and subsequent BMI increases. Conclusions Our study indicates bidirectional associations between smart device use and BMI among preschoolers, emphasizing the protective role of high-quality mother-child interactions. Interventions should focus on enhancing parent-child relationships, limiting device use, and promoting active engagement. Future studies should investigate the effect of media content and children’s self-regulation on these associations.

Background Healthy lifestyles are crucial for preventing chronic diseases. Nonetheless, approximately 90% of Chinese community residents regularly engage in at least one unhealthy lifestyle. Mobile smart devices-based health interventions (mHealth) that incorporate theoretical frameworks regarding behavioral change in interaction with the environment may provide an appealing and cost-effective approach for promoting sustainable adaptations of healthier lifestyles. We designed a randomized controlled trial (RCT) to evaluate the effectiveness of a socioecological model-guided, smart device-based, and self-management-oriented lifestyles (3SLIFE) intervention, to promote healthy lifestyles among Chinese community residents. Methods This two-arm, parallel, cluster-RCT with a 6-month intervention and 6-month follow-up period foresees to randomize a total of 20 communities/villages from 4 townships in a 1:1 ratio to either intervention or control. Within these communities, a total of at least 256 community residents will be enrolled. The experimental group will receive a multi-level intervention based on the socioecological model supplemented with a multi-dimensional empowerment approach. The control group will receive information only. The primary outcome is the reduction of modifiable unhealthy lifestyles at six months, including smoking, excess alcohol consumption, physical inactivity, unbalanced diet, and overweight/obesity. A reduction by one unhealthy behavior measured with the Healthy Lifestyle Index Score (HLIS) will be considered favorable. Secondary outcomes include reduction of specific unhealthy lifestyles at 3 months, 9 months, and 12 months, and mental health outcomes such as depression measured with PHQ-9, social outcomes such as social support measured with the modified Multidimensional Scale of Perceived Social Support, clinical outcomes such as obesity, and biomedical outcomes such as the development of gut microbiota. Data will be analyzed with mixed effects generalized linear models with family and link function determined by outcome distribution and accounting for clustering of participants in communities. Discussion This study will provide evidence concerning the effect of a mHealth intervention that incorporates a behavioral change theoretical framework on cultivating and maintaining healthy lifestyles in community residents. The study will provide insights into research on and application of similar mHealth intervention strategies to promote healthy lifestyles in community populations and settings. Trial registration number ChiCTR2300070575. Date of registration: April 17, 2023. https://www.chictr.org.cn/index.aspx .

Korean elementary school students have the lowest life satisfaction levels among OECD countries. The use of smart devices has led to smartphone addiction, which seriously affects their quality of life. This study aims to establish and test variables that affect the quality of life (QOL) of elementary school students based on the Predisposing, Reinforcing and Enabling Constructs in Educational Diagnosis and Evaluation (PRECEDE) model, using smart device-related parental intervention, self-efficacy, social support, health promotion behaviors, family environment, smart device addiction, and QOL as measurement variables. Three elementary schools in the Republic of Korea completed self-report questionnaires. Descriptive statistical analysis and hypothetical model fit and test were used for data analysis. The model was found to be valid. Smart device addiction directly affected QOL. In contrast, health promotion behaviors, self-efficacy, social support, and smart device parental intervention indirectly affected QOL. Health-promoting behaviors also directly affected smart device addiction, self-efficacy, and family environment had a direct effect on health-promoting behavior. Therefore, to improve the QOL of elementary school students, the government should focus on developing programs that can help them actively perform health promotion activities and improve self-efficacy, social support, and parental intervention for smart devices that indirectly affect them.

Background To evaluate the change of accommodation and ocular discomfort according to the display size, using quantitative measurements of accommodation and ocular discomfort through subjective and objective metrics. Methods Forty six subjects without any ophthalmic disease history were asked to watch the documentary movie, using two different sizes of smart devices; smartphones and tablets. Before and after using devices, the near point accommodation (NPA) and the near point convergence (NPC) were measured, and objective accommodation was measured using an auto refractometer/keratometer. The subjective ocular discomfort was assessed through a survey. Results Both devices showed a decrease in post-use NPA and NPC, and the change after use of the smartphone was significantly severe, 1.8 and 2.5 folds respectively, compared to tablet ( p  = 0.044, p  = 0.033, respectively). Neither smartphone nor tablet showed significant changes in the accommodative response induced by dynamic accommodative stimulus of auto refractometer/keratometer ( p  = 0.240 and p  = 0.199, respectively). Subjects showed a more severe increase in ocular discomfort after using smartphones ( p  = 0.035) and reported feeling tired even with shorter use times ( p  = 0.012). Conclusions Both devices showed significant decreases in NPA and NPC, and the larger changes were seen when using the small display smartphone. Even within 20minutes of using, subjects start to feel ocular discomfort, and it was more severe and faster after smartphones than tablets. Therefore, the smaller the display size, the greater the adverse impact on eyes, and thus, appropriate display size will need to be selected depending on the time and purpose of use.

Smart devices are equipped with technology that facilitates communication among devices connected via the Internet. These devices are shipped with a user interface that enables users to perform administrative activities using a web browser linked to the device’s server. Cross-site scripting (XSS) is the most prevalent web application vulnerability exploited by attackers to compromise smart devices. In this paper, the authors have designed a framework for shielding smart devices from XSS attacks. It is a machine learning-based attack detection framework which employs self-organizing-map (SOM) to classify XSS attack string. The input vector to the SOM is generated based on attack ontology and the changing behavior of the attack strings in different input fields in the device web interface. Additionally, it also sanitizes the injected attack string to neutralize the harmful effects of attack. The experimental results are obtained using the real-world dataset on the XSS attack. We tested the proposed framework on web interface of two smart devices (TP-link Wi-Fi router and HP color printer) containing hidden XSS vulnerabilities. The observed results unveil the robustness of the proposed work against the existing work as it achieves a high accuracy of 0.9904 on the tested dataset. It is a platform-independent attack detection system deployed on the browser or server side.