Smart Mobile Devices Research Articles

Application of Smart Mobile Devices in Electronic Design Education: Multidimensional Interaction Model and Learning Outcomes Assessment

Application of Smart Mobile Devices in Electronic Design Education: Multidimensional Interaction Model and Learning Outcomes Assessment

Exploring the use of Smart Mobile Devices and their applications for infocommunication and PKM in the personal lives of teachers

This article is part of a PhD research and investigates the impact of rapid technological evolution, focusing on the adoption of smart mobile devices (SMD), such as smartphones, tablets and laptops, and applications (app) in the personal lives of higher education professors in the area of Information Science. In addition, gaps are identified in existing research, emphasizing the need to understand how SMD is used in personal activities for personal knowledge management (PKM) practices and infocommunication. Specific objectives include identifying the most commonly used SMDs and apps, the frequency of such use, and how they are used for those practices. The methodology is qualitative, triangulating the integrative review with a questionnaire (N-10). The results point to interest in device ecologies for PKM and Info-communication, with communication apps (text and voice) and e-mail being the most important for teachers. It is hoped that the results of this study can contribute to the advancement of knowledge about the use of SMD by teachers and to the development of a generic descriptive model of infocommunicational behaviour and PKM that can be applied in other contexts.

Trust enhanced POI recommendation with collaborative learning

With the rapid development and popularization of smart mobile devices, users tend to share their visited points-of-interest (POIs) on the network with attached location information, which forms a location-based social network (LBSN). LBSNs contain a wealth of valuable information, including the geographical coordinates of POIs and the social connections among users. Nowadays, lots of trust-enhanced approaches have fused the trust relationships of users together with other auxiliary information to provide more accurate recommendations. However, in the traditional trust-aware approaches, the embedding processes of the information on different graphs with different properties (e.g., user-user graph is an isomorphic graph, user-POI graph is a heterogeneous graph) are independent of each other and different embedding information is directly fused together without guidance, which limits their performance. More effective information fusion strategies are needed to improve the performance of trust-enhanced recommendation. To this end, we propose a Trust Enhanced POI recommendation approach with Collaborative Learning (TECL) to merge geographic information and social influence. Our proposed model integrates two modules, a GAT-based graph autoencoder as trust relationships embedding module and a multi-layer deep neural network as a user-POI graph learning module. By applying collaborative learning strategy, these two modules can interact with each other. The trust embedding module can guide the selection of user’s potential features, and in turn the user-POI graph learning module enhances the embedding process of trust relationships. Different information is fused through the two-way interaction of information, instead of travelling in one direction. Extensive experiments are conducted using real-world datasets, and results illustrate that our suggested approach outperforms state-of-the-art methods.

The use of mobile technology in higher education: implications for students and instructors

Mobile learning has become a prominent topic in educational settings today due to its significant developments in information and communication technology (ICT). This flexibility not only benefits student-teachers who can learn at their own pace and convenience but also empowers instructors to engage with their student-teachers more dynamically. Literature indicates that mobile learning encourages collaboration and active participation, fostering critical thinking skills and creating a more engaging learning experience. Despite the many advantages of integrating technology into the classroom, research indicates that most faculties higher education are resistant to incorporating technology into their activities since there are no institutional norms. The study aims at evaluating the use of mobile learning in higher education in Lesotho and the implications it has on both instructors and student-teachers when using mobile learning. The study adopted a systematic literature review to review the available research using an integrative method of studies conducted on mobile learning in higher education between 2013 and 2023. The study found that some instructors ban the use of mobile devices because they distract from teaching and learning. In addition, not all devices can handle the demands of remote learning. Student-teachers believe that mobile technology enhances communication with peers and instructors. Among other factors, limited Internet connectivity and funding are the most common inhibiting factors for instructors to adopt technology into the teaching and learning process. Other inhibiting factors are the high costs of mobile data access and smart mobile devices. The study recommends that ICT policy must be in place to manage and control mobile learning for effective adoption.

The Application of Touch Sensors

Touch sensors have a wide range of applications in smart homes, enabling the control of lighting, air conditioning, curtains, and other equipment. This paper summarizes the principles, applications, and future developments of touch sensors, including capacitive, resistive, and piezoelectric sensors. The capacitive touch sensor operates based on the capacitance formula, the resistive sensor on the resistivity formula, and the piezoelectric sensor on the piezoelectric effect. Capacitive touch sensors can detect the movement and position of objects and respond quickly, but they require power. Resistive sensors have a fast response time, long durability, low cost, and high sensitivity, but they also require power. Piezoelectric sensors do not require power, can detect dynamic physical quantities, but are unable to detect static ones. Both capacitive and piezoelectric sensors are used in smart mobile devices, while resistive sensors are used in all-in-one machines. Given their principles and current applications, touch sensors are expected to be utilized in the touch screens of operation interfaces in more machines and the bionic skin of androids.

Comparison of Infrared Smart Mobile Phone Thermometer to Non-contact Forehead Infrared Thermometer in Pediatric Emergency Room Patients

Background: Non-contact infrared thermometers (NCITs) are frequently used for temperature measurements today due to their numerous advantages. With technological developments, NCITs have been integrated into smart mobile devices, aiming to provide ease of use. Objectives: This study aimed to compare body temperature measurements taken with an infrared smart mobile phone thermometer (SMT) to those taken with a non-contact forehead infrared thermometer (NCFIT) in children. Methods: Pediatric patients admitted to the emergency department of the University of Health Sciences, Zeynep Kamil Maternity and Children’s Hospital between June 2023 and July 2023 were included in the study. Body temperature measurements were performed using an SMT (USB K8) and an NCFIT (ThermoFlash LX-26). Results: A total of 1116 children were included in this prospective study. The mean difference between the NCFIT and SMT (2 cm near) measurements was 1.2°C. The comparison of the measurements was significantly different (P < 0.05). Although all three measurements were statistically significantly correlated, the correlation between the NCFIT and SMT’s readings at both 2 cm and 8 cm was weak, with r = 0.38 and r = 0.34, respectively (P < 0.01). There were 33 children with fever, defined as a body temperature ≥ 38°C with NCFIT. The mean measurements in febrile children from NCFIT, SMT (2 cm), and SMT (8 cm) were 38.2 ± 0.3°C, 36.3 ± 0.5°C, and 35.8 ± 0.9°C, respectively (P < 0.01). The SMT was not able to detect any body temperature ≥ 38°C. The cut-off value for SMT in patients with fever was determined as 36.15°C, which had a sensitivity of 72% and a specificity of 78%. Conclusions: The measurements of SMT from distances of 2 cm and 8 cm were correlated with each other, suggesting that it may be valuable for families in clinical settings to detect deviations in their children's baseline measurements, follow-up on antipyretic usage, and monitor different fever levels. However, it is not useful for screening fever in children and must be used cautiously.

Analysis of the Content of Ideological and Political Education for College Students from the Perspective of Online Communication

With the popularity of the Internet and mobile smart devices, online communication has become increasingly common among college students. However, online communication also brings some negative influences, such as information overload, false information, and cyberbullying. Therefore, ideological and political education for college students needs to strengthen the perspective of online communication, guiding them to use online communication tools correctly, avoid negative influences, and improve their online communication literacy and civility.

Combined Feature Set with Logistic Regression Model to Detect Credit Card Frauds in Real Time Applications

Online payment methods are gaining popularity and are widely used, both in-store and online. Because to the Internet and smart mobile devices, conducting such transactions is quick, simple, and stress-free. However, online payment fraud is common due to the open nature of the internet, which allows criminals to use techniques such as eavesdropping, phishing, infiltration, denial-of-service, database theft, and man-in-the-middle assault. Online payment fraud is on the rise, and it is a big contributor to global economic losses. Financial services, healthcare, insurance, and other industries have long been plagued by fraud. Online fraud has developed in tandem with the use of digital payment systems such as credit/debit cards, PhonePe, Gpay, and Paytm. Furthermore, fraudsters and criminals are adept at evasion strategies, allowing them to steal more. Developing a secure system for client authentication and fraud protection is tough since there is always a workaround. This means that fraud detection systems play an important role in preventing financial crimes. Over time, victims of internet transaction fraud have incurred tremendous financial losses. The growth of cutting-edge technologies and global connection has led to a surge in online fraud. To reduce these expenses, it is critical to develop effective fraud detection systems. Machine learning and statistical tools make detecting dishonest money deals much easier. The scarcity of data, the sensitive nature of the data, and the uneven class distributions make it challenging to implement efficient fraud detection models. Given the delicate nature of the information, it is difficult to draw conclusions and construct more accurate models. This study offers a Linked Feature Set with Combined Feature Set with Logistic Regression (CFS-LoR) Model for accurate detection of online payment frauds. In comparison to extant models, the proposed model exhibits a highly accurate detection capability.

Attribute-based linear homomorphic signature scheme based on key policy for mobile crowdsensing

Compared with traditional wireless sensor networks, mobile crowdsensing networks have advantages of low cost, easy maintenance, and high scalability, which will play a role in city-level data sensing scenarios in the future. So far, linear homomorphic signatures based on Public Key Instruction,identity, as well as certificateless, have been proposed in wireless sensor networks to resist the data contamination. However, these signature schemes cannot perform finer-grained signature verification, and these signature schemes do not realize the separation of users’ sensitive information from their data. To solve the above problems, we design an attribute-based linear homomorphic signature scheme for large-scale wireless network built with mobile smart devices. First, we give the definition of the attribute-based linear homomorphic signature scheme based on key policy (KP-ABLHS). Second, we construct KP-ABLHS by incorporating attribute-based signature and linear homomorphic coding signature scheme. Finally, we prove our protocol is secure in random oracle model (ROM) and use Python pairing-based cryptography library (pypbc) to implement the scheme. The experimental results show that our scheme is as efficient as Li et al.’s scheme and has the advantage of signing the set of attributes, while the efficiency of our scheme is significantly better than that of scheme Boneh et al.’s scheme.

Smart medical English pedagogy on a smart teaching platform

The widespread adoption of smart mobile devices has sparked significant innovations in China's smart education landscape in recent years. Smart teaching platforms play a pivotal role in shaping the dynamics of smart teaching. This study aimed to enhance medical English pedagogy by implementing smart teaching on the smart teaching platform (Xuexitong). The researchers divided their 178 nursing-majored students into two control classes and two experimental classes to evaluate the effectiveness of smart teaching. Traditional classroom instruction was adopted in the control classes while smart pedagogy was applied in the experimental classes. After a semester-long teaching experiment, two assessments were conducted: a comparison of exam scores and a course feedback survey. The findings revealed that students in the experimental classes significantly outperformed the control group in these two assessments. This highlights the substantial effectiveness of smart medical English pedagogy demonstrating its ability to enhance students' language proficiency and comprehension in medical contexts far beyond traditional teaching methods. The study concluded that smart teaching can significantly improve teaching outcomes and facilitate pedagogical innovations among new technological advancements. These insights offer valuable guidance for future research to enhance the integration of smart mobile technologies with smart language pedagogy.

AoI-Guaranteed Incentive Mechanism for Mobile Crowdsensing With Freshness Concerns

With the explosive spread of smart mobile devices, Mobile CrowdSensing (MCS) has been becoming a promising paradigm, by which a platform can coordinate a group of workers to complete large-scale data collection tasks using their mobile devices. In this paper, we investigate the incentive mechanism design in MCS systems, taking the freshness of collected data and social benefits into consideration. First, the Age of Information (AoI) metric is introduced to measure the freshness of data. Then, we model the incentive mechanism design with AoI guarantees as an incomplete information two-stage Stackelberg game with multiple constraints. Next, we consider the scenario that all participants share the public utility function parameters of the Stackelberg game. By deriving the optimal remuneration paid by the platform and the optimal data update frequency for each worker, and proving the existence of a unique Stackelberg equilibrium, we propose an AoI-guaranteed Incentive Mechanism (AIM) that enables the platform and all workers to maximize their utilities simultaneously. Furthermore, we extend AIM to a general scenario where each participant has no prior knowledge of the utility function parameters of the game. By resorting to the Deep Reinforcement Learning (DRL) technique and modeling the two-stage Stackelberg game as a Markov decision process, we propose a DRL-based Incentive Mechanism (DIM) with AoI guarantees, which makes each participant effectively seek its optimal strategy through trial and error. Meanwhile, the system can guarantee that the AoI values of all data uploaded to the platform are not larger than a given threshold. Finally, numerical experiments on real-world traces are conducted to validate the efficacy and efficiency of AIM and DIM.

Call to Action: Investigating Interaction Delay in Smartphone Notifications.

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.

Deep Learning Based Security Schemes for IoT Applications: A Review

Due to its widespread perception as a crucial element of the Internet of the future, the Internet of Things (IoT) has garnered a lot of attention in recent years. The Internet of Things (IoT) is made up of billions of sentients, communicative "things" that expand the boundaries of the physical and virtual worlds. Every day, such widely used smart gadgets generate enormous amounts of data, creating an urgent need for rapid data analysis across a range of smart mobile devices. Thankfully, current developments in deep learning have made it possible for us to solve the issue tastefully. Deep models may be built to handle large amounts of sensor data and rapidly and effectively learn underlying properties for a variety of Internet of Things applications on smart devices. We review the research on applying deep learning to several Internet of Things applications in this post. Our goal is to provide insights into the many ways in which deep learning techniques may be used to support Internet of Things applications in four typical domains: smart industrial, smart home, smart healthcare, and smart transportation. One of the main goals is to seamlessly integrate deep learning and IoT, leading to a variety of novel ideas in IoT applications, including autonomous driving, manufacture inspection, intelligent control, indoor localization, health monitoring, disease analysis, and home robotics. We also go over a number of problems, difficulties, and potential avenues for future study that make use of deep learning (DL), which is turning out to be one of the most effective and appropriate methods for dealing with various IoT security concerns. The goal of recent research has been to enhance deep learning algorithms for better Internet of Things security. This study examines deep learning-based intrusion detection techniques, evaluates the effectiveness of several deep learning techniques, and determines the most effective approach for deploying intrusion detection in the Internet of Things. This study uses Deep Learning (DL) approaches to better expand intelligence and application skills by using the large quantity of data generated or acquired. The many IoT domains have drawn the attention of several academics, and both DL and IoT approaches have been explored. Because DL was designed to handle a variety of data in huge volumes and required processing in virtually real-time, it was indicated by several studies as a workable method for handling data generated by IoT.

An Efficient and Differential Privacy-Based Scheme for Aggregating Mobility Datasets

Mobile smart devices, such as mobile phones, wearable devices, and in-vehicle navigation systems, bring us convenience and have become necessities in modern daily life. The built-in global positioning system (GPS) of these mobile devices collects the users’ mobility data to support path planning, navigation and other location-related applications, which also inevitably causes privacy issues. Previous research has shown that employing count-min sketch (CMS) to aggregate mobility datasets is a valid privacy-preserving method for resisting the reconstruction attack on population distributions. However, as the utility/accessibility of the protected datasets is excessively correlated with the size of CMS, decreasing the data transmission cost has become an unsolved issue of that approach. In this paper, we propose an efficient scheme with differential privacy to protect mobility datasets, which releases the privacy-preserving population distributions and achieves better utility as well as a much smaller data transmission cost compared to the CMS-based method. Our proposed scheme is comprised of two collaborative components, global sketch and temporal sketch. The global sketch is responsible for aggregating the raw mobility data and decreasing the data transmission cost, while the temporal sketch is in charge of guaranteeing the utility of the population distributions aggregated by the global sketch. Besides, to enhance the privacy preservation, we employ the Laplace mechanism to make the transmitted data satisfy ϵ-differential privacy. Through our analysis and empirical experiments, compared to the other three state-of-the-art privacy-preserving methods on mobility datasets, our scheme could preserve the privacy of the mobility datasets with much less data transmission cost under the same utility loss.

Implementation of GAGAN augmentation on smart mobile devices and development of a cooperative positioning architecture

Abstract This study presents an Android-based cooperative positioning (CP) architecture to improve the GNSS positioning performance on mobile devices. SBAS (Satellite Based Augmentation System) augmentation increases positioning accuracies significantly by sharing corrections between SBAS-enabled and non-capable devices via wireless connection or using a central server. The Indian GAGAN (GPS Aided GEO Augmented Navigation) is employed and assessed in the experiments. If GAGAN corrections are applied, all three chosen mobile devices showed a positioning accuracy improvement of around 95 %. The average 2D RSME was reduced from 75.23 to 1.35 m for the single-frequency GNSS smartphone Xiaomi Redmi Note 8 and from 33.25 to 1.62 m for the dual-frequency Google Pixel 4. As expected, the third GIS mapping device, Stonex S70 tablet, showed the highest performance, achieving sub-meter positioning accuracies. Thus, the experiment has proven the suitability of GAGAN augmentation for mobile devices, providing positive insight for further development of the CP architecture.

HyTra: Hyperclass Transformer for WiFi Fingerprinting-based Indoor Localization

The emerging demand for a variety of novel Location-based Services (LBS) by consumers and industrial users is driven by the rapid and extensive proliferation of mobile smart devices. Sensors embedded in smart devices or machines provide wireless connectivity and Global Positioning System (GPS) capability, and are co-utilized to acquire location-linked data which are algorithmically transformed into reliable and accurate location estimates. GPS is a mature and reliable technology for outdoor localization but indoor localization in a complex multi-storey building environment remains challenging due to fluctuations in wireless signal strength arising from multipath fading. Location-linked data from wireless access points (WAPs) such as received signal strength (RSS) are acquired as numerical sequences. By conceptualizing a fixed order sequence of WAP measurements as a sentence where the RSS from each WAP are words, we may leverage on recent advances in artificial intelligence for natural language processing (NLP) to enhance localization accuracy and improve robustness against signal fluctuations. We propose the hyper-class Transformer (HyTra), an encoder-only Transformer neural network which learns the relative positions of wireless access points (WAPs) through multiple learnable embeddings. We propose a second network, HyTra-HF, which improves upon HyTra by applying a hierarchical relationship between location classes. We test our proposed networks on public and private datasets varying in sizes. HyTra-HF outperforms existing deep learning solutions by obtaining 96.7\% accuracy for the floor classification task on the UJIIndoorloc dataset. HyTra-HF is amenable to deep model compression and achieves accuracy of 95.95\% with over ten-fold reduction in model size using Sparsity Aware Orthogonal (SAO) initialization and has the best-in-class accuracy for the sparse model.

Crop Guidance Photography Algorithm for Mobile Terminals

The issues of inadequate digital proficiency among agricultural practitioners and the suboptimal image quality captured using mobile smart devices have been addressed by providing appropriate guidance to photographers to properly position their mobile devices during image capture. An application for crop guidance photography was developed, which involved classifying and identifying crops from various orientations and providing guidance prompts. Three steps were executed, including increasing sample randomness, model pruning, and knowledge distillation, to improve the MobileNet model for constructing a smartphone-based orientation detection model with high accuracy and low computational requirements. Subsequently, the application was realized by utilizing the classification results for guidance prompts. The test demonstrated that this method effectively and seamlessly guided agricultural practitioners in capturing high-quality crop images, providing effective photographic guidance for farmers.

AMDDLmodel: Android smartphones malware detection using deep learning model.

Android is the most popular operating system of the latest mobile smart devices. With this operating system, many Android applications have been developed and become an essential part of our daily lives. Unfortunately, different kinds of Android malware have also been generated with these applications' endless stream and somehow installed during the API calls, permission granted and extra packages installation and badly affected the system security rules to harm the system. Therefore, it is compulsory to detect and classify the android malware to save the user's privacy to avoid maximum damages. Many research has already been developed on the different techniques related to android malware detection and classification. In this work, we present AMDDLmodel a deep learning technique that consists of a convolutional neural network. This model works based on different parameters, filter sizes, number of epochs, learning rates, and layers to detect and classify the android malware. The Drebin dataset consisting of 215 features was used for this model evaluation. The model shows an accuracy value of 99.92%. The other statistical values are precision, recall, and F1-score. AMDDLmodel introduces innovative deep learning for Android malware detection, enhancing accuracy and practical user security through inventive feature engineering and comprehensive performance evaluation. The AMDDLmodel shows the highest accuracy values as compared to the existing techniques.

The Influence of Input Method and Chinese Character Complexity on the Elderly Using Smartphones to Input Information – A Study from China

The usage of smartphones holds paramount importance in enhancing the ability of elderly people to live independently. This research aims to explore the various scenarios in which Chinese elderly people employ smartphones for information input, and how input method and Chinese character complexity affect their information input process. To achieve this, we conducted semi-structured interviews to investigate typical situations where Chinese elderly individuals use smartphones to input information. Based on the survey results, we devised an experiment to assess the impact of the input method and Chinese character complexity on the elderly’s objective physical load, mental load, input performance, and subjective experience during the inputting process. The findings revealed that the choice of input method significantly affects the objective physical load. Distinct input method engaged different major muscle groups during the process. As the Chinese character complexity increased, the elderly’s input performance declined, with handwriting input being particularly affected. Thus, we proposed enhancing the current handwriting interface by allowing users to input complex Chinese characters through handwritten pinyin, and simplify the start steps of the handwriting function based on the pinyin input interface. This study contributes to the development of evidence-based guidelines for the aging design of smart mobile devices, thus enhancing the elderly’s overall experience in using such technology.

Optimization of Higher Education Teaching Methodology System Based on Edge Intelligence

Abstract This study provides an in-depth research on the dynamic allocation of resources in higher education teaching and learning, especially in the application of edge intelligence architecture. In the study, the characteristics of edge intelligence and its application in smart mobile devices (SMDs) are first analyzed, highlighting the role of mobile edge computing (MEC) in reducing latency and improving the quality of user experience. Then, the study adopts a data acquisition method based on deep neural network (DNN) model to optimize the edge training model. The experimental results show that the efficiency of edge computing can be significantly improved by optimizing the allocation of computing resources and reducing the data transmission delay. Specifically, the total training delay and energy consumption of the edge server are reduced under different global iteration numbers in the experiment. In addition, the study also explores the integration of 5G networks and AR/VR technology in education. It proposes a teaching optimization model based on edge intelligence, improving interaction quality and learning efficiency in AR/VR safety education classrooms. The study shows that the teaching model performs well in reducing latency and increasing transmission rate, which is especially suitable for dual-teacher classroom scenarios and provides a new perspective for future higher education teaching.