Search Results (5)

“Smart” devices, such as contemporary smartphones and PDAs (Personal Digital Assistance), play a significant role in our daily live, be it for navigation or location-based services (LBSs). In this paper, the use of Ultra-Wide Band (UWB) and Wireless Fidelity (Wi-Fi) based on RTT (Round-Trip Time) measurements is investigated for pedestrian user localization. For this purpose, several scenarios are designed either using real observation or simulated data. In addition, the localization of user groups within a neighborhood based on collaborative navigation (CP) is investigated and analyzed. An analysis of the performance of these techniques for ranging the positioning estimation using different fusion algorithms is assessed. The methodology applied for CP leverages the hybrid nature of the range measurements obtained by UWB and Wi-Fi RTT systems. The proposed approach stands out due to its originality in two main aspects: (1) it focuses on developing and evaluating suitable models for correcting range errors in RF-based TWR (Two-Way Ranging) technologies, and (2) it emphasizes the development of a robust CP engine for groups of pedestrians. The results obtained demonstrate that a performance improvement with respect to position trueness for UWB and Wi-Fi RTT cases of the order of 74% and 54%, respectively, is achieved due to the integration of these techniques. The proposed localization algorithm based on a P2I/P2P (Peer-to-Infrastructure/Peer-to-Peer) configuration provides a potential improvement in position trueness up to 10% for continuous anchor availability, i.e., UWB known nodes or Wi-Fi access points (APs). Its full potential is evident for short-duration events of complete anchor loss (P2P-only), where an improvement of up to 53% in position trueness is achieved. Overall, the performance metrics estimated based on the extensive evaluation campaigns demonstrate the effectiveness of the proposed methodologies. Full article

Amid the ongoing COVID-19 pandemic, technical solutions (e.g., smartphone apps, web-based platforms, digital surveillance platforms, etc.) have played a vital role in constraining the spread of COVID-19. The major aspects in which technical solutions have helped the general public (or health officials) are contact tracing, spread prediction, trend forecasting, infection risk estimation, hotspot identification, alerting people to stay away from contaminated places, hospitalization length estimation, clinical severity analysis, and quarantine monitoring, to name a few. Apart from other services, contact tracing has been extensively performed with the help of Bluetooth and GPS-powered smartphone applications when vaccines were unavailable. In this article, we technically analyze the contact tracing platform developed by Google–Apple for constraining the spread of COVID-19. We suggest unexplored technical functionalities that can further strengthen the platform from privacy preservation, service scenarios, and robustness point of view. Lastly, some AI-based and privacy-assured services that can be integrated with the platform to control the pandemic adequately are suggested. The technical analysis demonstrates that while the Google–Apple platform is well-engineered, it is not free of vulnerabilities, weaknesses, and misconfigurations that may lead to its poor adoption in real-life scenarios. This work can serve as a guideline for further enhancing the practicality of contact tracing platform to effectively handle future infectious diseases. Full article

The literature about how Information and Communication Technologies (ICTs) influence farmers’ adoption of sustainable agricultural technology is emerging, studies regarding the effects of particular smartphone-based digital extension services on farmers’ sustainable agricultural technology practices are limited. This study investigates the relationship between a digital extension service (“Zhe’ yang’ shi” WeChat application) and the adoption of soil testing and formula fertilization, a precision fertilization technology. A household choice model is constructed to explain the impact of the application. Based on a household-level data set from a survey of 400 farmers in Zhejiang in 2022, empirical results show that the use of the “Zhe’ yang’ shi” WeChat application significantly increases the adoption of soil testing and formula fertilization. We also discuss the heterogeneous effect by different production scales. The findings enrich the literature regarding ICTs’ influence on farmers’ behavior in adopting sustainable agricultural technology. It provides a valuable example for developing countries to promote sustainable agriculture through digital technology. Full article

The ever-increasing complexity in manufacturing environments has caused delay and uncertainty for on-site personnel when retrieving critical information. Currently, information associated with manufacturing environments is created and stored in centralized terminals and even paper-based logbooks. Any delays in accessing this information can hinder critical decision-making processes that are essential to ensure maximum efficiency and productivity. To increase the competitiveness of manufacturing companies, the authors present a user-oriented markerless augmented reality authoring solution for the manufacturing environment utilizing the capability of a modern smartphone or head-mounted display. With a one-time setup and calibration, the end user is able to augment a physical workspace with a digital layer and annotate any stationary object with application data including maintenance records, service tickets, and usage logs. By directly mapping the data onto a relevant physical object, it allows more efficient reuse and better contextualization of manufacturing information. The backend expandable data structure also enhances extensibility so that the users can modify the digital layer with ease when the physical environment changes. This annotation tool defines a novel way of managing manufacturing information to help companies work more efficiently and effectively. Full article

A model describing the wide variety of human behaviours called personality, is becoming increasingly popular among researchers due to the widespread availability of personal big data generated from the use of prevalent digital devices, e.g., smartphones and wearables. Such an approach can be used to model an individual and even digitally clone a person, e.g., a Cyber-I (cyber individual). This work is aimed at establishing a unique and comprehensive description for an individual to mesh with various personalized services and applications. An extensive research literature on or related to psychological modelling exists, i.e., into automatic personality computing. However, the integrity and accuracy of the results from current automatic personality computing is insufficient for the elaborate modeling in Cyber-I due to an insufficient number of data sources. To reach a comprehensive psychological description of a person, it is critical to bring in heterogeneous data sources that could provide plenty of personal data, i.e., the physiological data, and the Internet data. In addition, instead of calculating personality traits from personal data directly, an approach to a personality model derived from the theories of Carl Gustav Jung is used to measure a human subject’s persona. Therefore, this research is focused on designing an archetype-based modeling of persona covering an individual’s facets in different situations to approach a comprehensive personality model. Using personal big data to measure a specific persona in a certain scenario, our research is designed to ensure the accuracy and integrity of the generated personality model. Full article