Mobile Systems Research Articles

A certificateless designated verifier sanitizable signature in e-health intelligent mobile communication system

With the widespread use of mobile communication and smart devices in the medical field, mobile healthcare has gained significant attention due to its ability to overcome geographical limitations and provide more efficient and high-quality medical services. In mobile healthcare, various instruments and wearable device data are collected, encrypted, and uploaded to the cloud, accessible to medical professionals, researchers, and insurance companies, among others. However, ensuring the security and privacy of healthcare data in the context of mobile networks has been a highly challenging issue. Certificateless signature schemes allow patients to conceal their respective privacy information for different sharing needs. Nevertheless, existing mobile healthcare data protection solutions suffer from costly certificate management and the inability to restrict signature verifiers. This paper proposes a certificateless designated verifier sanitizable signature for mobile healthcare scenarios, aiming to enhance the security and privacy of mobile healthcare data. This scheme enables the sanitization of sensitive data without the need for certificate management and allows for the specification of signature verifiers. This ensures the confidentiality of medical data, protects patient privacy, and prevents unauthorized access to healthcare data. Through security analysis and experimental comparisons, it is demonstrated that the proposed scheme is both efficient and effectively ensures data security and user privacy. Therefore, it is well-suited for privacy protection in mobile healthcare data.

On emergent mobile phone-based social engineering cyberattacks in developing countries: The case of the Zambian ICT sector

The number of registered SIM cards and active mobile phone subscribers in Zambia in 2020 surpassed the population of the country. This development and the integration of mobile phone systems with financial payment systems has not come without a cost. Cyberattackers, using various social engineering techniques have jumped onto the bandwagon to defraud unsuspecting users. Considering the aforesaid, this paper presents a high-order analytical approach towards mobile phone-based social engineering cyberattacks (phishing, SMishing, and vishing) in Zambia which seek to defraud benign victims. This paper presents a baseline study to reiterate the problem at hand. The research used a mixed-methods approach, combining quantitative and qualitative data, and adopted a hybrid descriptive research design. Furthermore, an attack model and an evaluation framework were devised to ascertain the most prevalent types of mobile phone-based cyberattacks. Based on logistic regression analysis, the findings indicate that the most prevalent type of mobile phone-based social engineering cyberattack in Zambia is SMishing. Based on the results and observed insights, recommendations to mitigate these emergent social engineering cyberattacks were suggested. This research serves as a valuable baseline for understanding and addressing the growing challenges posed by mobile phone-based cyberattacks in Zambia's evolving technological landscape.

Using an extended technology acceptance model to understand the behavioral adoption intention of mobile commerce among Chinese consumers

The ubiquitous nature of mobile handsets is enhancing consumers’ shopping experience and thus encouraging extensive m-commerce adoption. The personality traits of the users of m-commerce vary and thus understanding the perspective of people to utilize mobile commerce systems is vital. This study thus examined the factors that drive Chinese consumers to adopt m-commerce. The Technology Acceptance Model (TAM) was utilized as the theoretical foundation while the analysis was completed with AMOS using structural equation model techniques. The results have demonstrated that perceived usefulness and perceived ease of use influence m-commerce adoption intentions. Also, service quality along with security, and privacy, were found to significantly drive mobile commerce adoption intentions. Perceived risk showed a significant but negative impact on adoption intentions of m-commerce. Familiarity showed a significant positive three-dimensional impact on PU, PEOU, and m-commerce adoption intentions. The theoretical and managerial repercussions of these results on m-commerce development and diffusion are discussed.

Modeling Android Security Vulnerabilities: Insights from Statistical Distributions

Android operating system is a mobile operating system that supports multimedia features. Android offers a wide range of applications and integrated features for playing, recording, editing and sharing audio, video, images and other multimedia content. Most Android devices include cameras, speakers, microphones, and other multimedia components. In software security, vulnerabilities are critical concerns that often emerge during software development. Predicting these vulnerabilities post-release is essential for risk assessment and mitigation. While various models have been explored, the Android operating system remains relatively uncharted. This study delves into modeling Android security vulnerabilities using different statistical distributions, comparing their suitability to the widely-used Alhazmi-Malaiya Logistic (AML) model. Data from the National Vulnerability Database (NVD) spanning 2016 to 2018, along with Common Vulnerability Scoring System (CVSS) scores, was analyzed. The study evaluates several distribution models, including Logistic, Weibull, Nakagami, Gamma, and Log-logistic, for monthly vulnerability counts and average monthly impact values. Goodness-of-fit tests and information criteria were applied for model robustness assessment. The findings offer valuable insights for researchers and Android software developers, aiding prediction, risk assessment, resource allocation, and research direction. Logistic and Nakagami distributions emerged as the best-fit models for average monthly impact values and monthly vulnerability counts, respectively. Finally, statistical methods perform better against known artificial intelligence methods for small data sets or more clearly defined data due to their flexible features such as comprehensibility, amount of data, need for calculation, and data independence.

Implementation of a Highly Mobile UAVs Direction Finding Complex Using Virtual Magnetic Dipoles

Relevance. In the context of the development of modern broadband communication systems, the task of performing direction finding of signals using highly mobile direction finding systems (UAVs) is becoming especially urgent. They impose restrictions on the size of antenna elements and the distance between them, which leads to an increase in the mathematical error of the bearing. Problem statement. The paper sets the task of considering the possibility of increasing the direction finding accuracy of a highly mobile complex by using virtual magnetic dipole technology. A feature of this method is the minimization of distortions introduced by the carrier body into the characteristics of the measured field. To measure the field characteristics, as well as their partial components, vector antenna elements were used. Goal of the work is to study the characteristics of a radio direction finding complex using virtual magnetic dipole methods in conditions of distortions introduced by the carrier body. As an example, cases of direction finding of differently polarized waves, taking into account the influence of radomes of antenna elements, assessment of the maximum resolution accuracy, as well as noise stability are considered. The finite element method implemented in DS CST Studio Suite 2024 was used in the modeling. Result. During the research, a plane wave with different bearings fell on a UAV with a direction finding complex, which made it possible to carry out the most accurate research. The results of the work also include the creation of a model of a radio direction finding complex, which can be installed on a small UAV, providing high accuracy of wave direction finding in passive mode.The novelty of the method used lies in the application of methods for the formation of virtual magnetic dipoles based on the measured characteristics of the distorted electric field. New to this work are modeling cases that are associated with noise suppression, the influence of antenna element housings, as well as assessment of the maximum resolution accuracy.Practical significance of the work lies in the creation of a model of a radio location system based on vector antenna elements, which are used to measure the characteristics of the electromagnetic field with subsequent direction finding based on the magnetic field.

Intelligent mobile robot for contagious disease treatments in hospitals

This paper provides a novel and applicable work that builds a real system for disinfecting the air and surfaces of the environment in a hospital room, with a non-contact measurement system for supporting contagious disease treatments in hospitals. The system is built on an intelligent mobile robot system that operates autonomously in a simulated real treatment room. The research team uses a new positioning algorithm. It is a combination of data from the Lidar sensor, encoder, and Extended Kalman filter. The program that applies segmentation and image feature extraction algorithms is developed to meet requirements of real-time environment mapping in the room. Control algorithms for moving and avoiding obstacles are also proposed. Next, techniques for collecting health data including patient identification, body temperature, and blood oxygen index via wireless sensor network are also mentioned in the article. Analysis and experimental results show qualified outcomes and promise. The main contribution of the paper can be listed as follows.•Design and build a new CEE-IMR, an intelligent mobile robot that can regconize patients, guide and lead them walking in hospitals, especially keep a safe distance avoiding contagious deseases.•A novel framework for controlling the robot is proposed. The robot can move flexible, avoid obstacles, etc. based on advanced control algorithms. A new control mechanism is also proposed.•Methods of collecting data and processing medical data to support either patients or doctors to improve the effecency in hospitals in contagious disease management.

Using CFD to model the distortion of pollutant concentration signal at exhaust lines

The present study introduces a method for generating the distortion of species concentration signals within exhaust lines, employing Computational Fluid Dynamics (CFD) modelling instead of experimental techniques. Understanding distortion is important when e.g. tailpipe pollutant signals need to be correlated to engine operation patterns and related correction models need to be built. The approach was initially applied on a typical exhaust line geometry of a car, demonstrating the ability of CFD modelling to simulate the CO2 signal distortion from engine out to tailpipe, and was validated against laboratory measurements. Comparisons for variable operating points, between simulations and experiments, presented a good agreement with a deviation of up to 0.04 s in the time required for the signal to reach 50 % of its final output value (t50), and of up to 0.1 s in the rise time (t90-10). The methodology was applied to a vehicular exhaust line comprising tubing and buffer volumes, simulating catalysts or mufflers. This investigation aimed to identify the parameters that influence the distortion of CO2, revealing that inlet exhaust gas velocity can significantly impact CO2 distortion. Inlet temperature variations of ± 50 K produce a negligible deviation in t50 of ± 0.01 s for low inlet velocities and of ± 0.001 s for high inlet velocities. CFD is a useful tool to characterize pollutants signal generation within exhaust line configurations not limited to cars. Such a technique can be used to any mobile or stationary combustion system to study species concentration distortions caused by the individual components.

Optical Technologies Supporting 5G/6G Mobile Networks

Intensively developed mobile systems and networks 5G [...]

A Mobile Surveillance System with Face Detection and Recognition Abilities

This paper proposes a mobile surveillance system similar to an electronic dog which has the ability to move around a certain location all while detecting and recognizing the individuals and notifying the respective authorities about the detected person. The system uses an Arduino UNO microcontroller along with IR receiver and remote to function as a remote-controlled vehicle while also providing an option for automatic movement of the vehicle. The system also has an integrated camera that constantly feeds live video to a computer system. Haar-cascades take in the frames of videos and detect appearance of human face. Then, pretrained FaceNet Convolutional Neural Network (CNN) compares the detected face to the authorized faces in an attempt to recognize the face. The system also boasts the feature of email notification for which it employs the SMTP protocol to send an email with the detected face to the authorized personnel. The methods and techniques used to implement these features are presented in depth in this paper.

A modular and flexible open source cell incubator system for mobile and stationary use

Culturing living cells in vitro requires the maintenance of physiological conditions for extended periods of time. Here, we introduce a versatile and affordable incubation system, addressing the limitations of traditional incubation systems. Conventionally, stationary cell incubators maintain constant temperature and gas levels for in vitro cell culturing. Combining such incubators with additional lab equipment proves challenging. The presented platform offers modularity and adaptability, enabling customization to diverse experimental needs. The system includes a main unit with a user-friendly interface as well as an interchangeable incubation chamber. We present two incubation chambers targeting two completely different use cases. The first chamber, named “inkugo” facilitates the transportation of cells for up to two hours without external power and for more than a day without an external CO2 source. The second chamber termed “inkubox” was designed to enable continuous electrophysiological recordings. Recordings from up to four neural cultures growing on high-density microelectrode arrays can be performed in parallel. The system’s unique feature lies in its separability of control and incubation components, allowing one control unit to manage various custom chambers. The design’s simplicity and the use of widely accessible components make the here proposed incubation system replicable for any laboratory. This platform fosters collaboration and experimentation in both decentralized and traditional laboratory settings, making it an invaluable addition to any cell culturing pipeline.

Enhancement of the impacts 2050 model to enable a whole system sustainability assessment of rideshare

Emerging concepts, such as Mobility as a Service (MaaS), could evolve to provide sustainable mobility, especially in densely populated urban areas. However, recent studies highlight the challenge of evaluating how the complex interactions of user demographics, mode choice, vehicle automation, governance, and efficiency will impact the sustainability of future mobility. Given this challenge, this research identifies a whole system (STEEP - social, technical, economic, environmental, and political) framework as essential to assess the overall sustainability of emergent urban mobility systems such as rideshare. The need is a single tool that can rapidly explore the long-range sustainability impact of such alternative future mobility scenarios for a given city region. This paper documents enhancements made to Impacts 2050, a strategic-level model of urban mobility, to address this need, including updates to the statistical travel behavior model and the addition of rideshare including trip occupancy. Results obtained with the enhanced Impacts 2050 showed that, while rideshare use increased significantly for some scenarios, its overall mode share remained limited. In addition, though rideshare enabled users to shed car ownership, the overall percentage increase of “no car ownership” was low. An urban mobility sustainability scorecard based on STEEP and generated by output from the enhanced Impacts 2050 is presented.

Therapists’ perspectives on experience and treatment of mobility-related anxiety disorders

In this paper, we explore how psychology professionals understand and treat the mobility issues of people diagnosed with anxiety disorder, based on 13 in-depth interviews. All interviewees regularly treat clients facing challenges using various modes of transportation due to anxiety disorders. Their clients exhibit a range of mobility issues, with some struggling with driving or being a passenger in a car, while others encounter difficulties when using public transport, cycling, or being a pedestrian. The mobility problems their clients experience affect their daily lives. Next to applying individual Cognitive Behavioral Therapy (CBT) adopted by most interviewees, they also suggest that adjustments in the mobility system could assist individuals with anxiety disorders to participate more fully in society. Hence, we advocate for more research on the mobility system that integrates psychological perspectives on mobility-related problems caused by anxiety disorder to support a more inclusive mobility planning, design, and organization of the system. Inclusive planning implies (1) designing mainstream infrastructures and services enabling people with anxiety disorders to move around independently while reducing anxiety-triggering situations without them becoming disclosed; (2) enhancing walkability and cyclability through both transport and land use planning, since having destinations within a short distance from home seems to be especially beneficial for people with anxiety disorders.

Emulsions that store oxygen for fast ORR kinetics and multifunctional robotic and mobility systems

Emulsions that store oxygen for fast ORR kinetics and multifunctional robotic and mobility systems

Securing high-value electronic equipment: an internet of things driven approach for camera security

<p>This article addresses pressing challenge of securing high-value electronic equipment, notably cameras, which face dual threats of damage in high humidity conditions and theft due to their significant market value. To confront these issues, the study introduces innovative internet of things (IoT)-driven approach aimed at strengthening conventional storage box security. Central to this approach is integration of IoT technologies, such as Arduino and ESP32, to develop advanced safety storage box. This enhanced system features essential hardware components, including buzzer, password-protected keypad, radio frequency identification reader, and DHT11 sensor for humidity monitoring. Additionally, mobile alarm system is incorporated to promptly alert owners of any detected movement vibrations, thereby augmenting security measures. By leveraging these components, proposed methodology seeks to mitigate risks associated with camera theft and fungal contamination, thereby advancing electronic device security. The expected outcome is marked enhancement in protection of high-value electronic equipment, particularly cameras, through continuous real-time monitoring and proactive security measures. This research underscore’s critical role of IoT technologies in fortifying security measures for valuable electronic assets, contributing significantly to ongoing discourse on innovative strategies in field. Through its comprehensive approach, this study aims to offer practical solutions to mitigate security risks and safeguard electronic equipment against potential threats, thereby addressing critical need in realm of electronic device security.</p>

A New Method for Temperature Dependent COM Parameters Extraction and Its Application to Simulation of SAW Devices

Abstract Surface Acoustic Wave (SAW) filters are widely used in mobile communication systems due to their superior performance. Yet, application scenarios for these SAW devices can be limited by temperature stability. The coupling of modes (COM) model is proved to be an efficient modeling approach to design SAW devices at present. However, temperature dependent COM parameter extraction is still absent. This work proposed a new method combining COM model with quasi-3D periodic finite element method (FEM) model coupled with thermal field, which can accurately and rapidly simulate and optimize the temperature stability of SAW devices. The COM parameters extraction method used in this study mainly depends on harmonic admittance. For a given temperature (T), the thermal effect can be computed by incorporating the linear thermal expansion coefficients and temperature coefficients of elasticity into the model. For validation, the admittance responses of a finite-length SAW resonator and the S-parameter of a double-mode SAW (DMS) for 42º YX-LiTaO3 piezoelectric substrate are calculated by using P-Matrix with the extracted COM parameters. For validation, the admittance responses of a SAW resonator on 42º YX-LiTaO3 under different temperature conditions is calculated. Furthermore, the temperature behaviors of a DMS filter on 42º YX-LiTaO3 are also analyzed based on the proposed method. Accurate predictions of the temperature behaviors for both resonator and filter were achieved and discussed. This investigation provides guidelines for design of high-performance SAW devices with good temperature stability.

Learning efficiency maximization in UAV-and-RIS-aided mobile edge learning system

With the ever-increasing number of Internet of Things devices (IoTDs) and the rapid development of artificial intelligence (AI) technologies, mobile edge learning (MEL) has emerged as a new communication network paradigm that can deploy machine learning on mobile edge computing (MEC) platforms with abundant computational resources. Then how to fully exploit the potential of MEL and enhance its performance is an important issue. To address this issue, the paper proposes an unmanned aerial vehicle (UAV)-and-reconfigurable intelligent surface (RIS)-aided MEL system. In the system, the UAV equipped with a MEL server can fly close to the IoTDs to collect their data for training a deep learning model. And the RIS mounted on the building can improve the wireless channel environment between the UAV and IoTDs to assist the UAV in collecting data. In order to maximize the MEL learning performance while minimizing the system energy consumption, this paper also proposes a new optimization metric called learning efficiency. Then, a learning efficiency maximization problem based on the proposed system is formulated by jointly optimizing the minority class sample size, the transmit resource of the IoTDs, the phase shift of the RIS, and the trajectory of the UAV. Considering the intractability of the problem, we solve it using the alternating optimization (AO) algorithms based on the two types of UAV trajectory design, i.e., a time-division-multiple-access (TDMA) design with higher performance and a Flight-Hover design with lower complexity. The simulation results demonstrate that the proposed optimization metric and algorithms are effective and perform excellently compared to other baselines.

COVID-19 vaccination campaigns in fragile and conflict-affected settings, Somalia.

By 31 December 2021, only 5.5% (861 879/15 670 530) of the Somali population had been fully vaccinated against coronavirus disease 2019 (COVID-19). To rapidly increase COVID-19 vaccine coverage in 2022, the health ministry and its partners (World Health Organization and United Nations Children's Fund) adopted a more responsive strategy. This strategy included careful microplanning, better targeting of populations and providing people-centred vaccination services close to their homes. These services were combined with childhood vaccination and basic health-care provision using the existing polio network and community health workers. Additionally, a digital tool for recording COVID-19 vaccination data and a mobile phone-based electronic registration system were introduced. Somalia, a fragile and conflict-affected state, faced challenges when implementing COVID-19 vaccination, including inexperience in managing mass adult vaccination, inadequate infrastructure and health workforce. Furthermore, insecurity in some areas and severe drought resulted in large-scale displacement of people. The implementation of a more context-specific strategy helped Somalia reach substantially more people with COVID-19 vaccination and 42.1% coverage by 31 December 2022. Additionally, 84 600 zero-dose children received their first childhood vaccine during the integrated campaigns. The increased coverage has led to public health benefits that outweigh the investment in the COVID-19 vaccination campaigns. Successful roll-out of adult vaccination is achievable even in a fragile and conflict-affected setting through implementation of a tailored contextualized approach. Key factors include good microplanning, use of digital tools, better population-targeting, bundling vaccines together and delivering vaccination services close to people's homes.

Consumer Adoption of Mobile Payment Systems: A Lens Into Smartphone‐Enabled Tourism

ABSTRACTThis research examines the factors influencing the success or failure of mobile payment systems (MPSs) in the travel sector. The research makes use of components from innovation resistance theory (IRT), coping theory (CP), and the unified theory of acceptance and use of technology (UTAUT 2). The data were gathered from 503 travelers in Malaysia, and Statistical Package for Social Sciences (SPSS) and SmartPLS were used to analyze the data. Usage, risk, and tradition barriers from IRT significantly influenced tourists' intention to use MPS. Furthermore, all direct hypotheses for CP components were supported, except for the mediating role of satisfaction. Additionally, effort expectancy, hedonic motivation, and habit (UTAUT 2) were identified as critical factors shaping tourists' intention to use MPS. These findings are crucial for tourist operators, banks, and governmental agencies to facilitate smooth and trackable financial transactions in the travel and tourism sector.

AI-Powered Obstacle Detection for Safer Human-Machine Collaboration

Abstract This article deals with ensuring and increasing the safety of mobile robotic systems in human-machine collaboration. The goal of the research was to design and implement an artificial intelligence application that recognizes obstacles, including humans, and increases safety. The resulting mobile Android application uses a MiDaS model to generate a depth map of the environment from the drone’s camera to approximate the distance from all obstacles to avoid the drone’s collision. Besides, this work introduced us to DJI Mobile SDK and neural network optimizations for their use on smartphones.

A comprehensive strategy of lipidomics and pharmacokinetics based on ultra-high-performance liquid chromatography-mass spectrometry of Shaoyao Gancao Decoction.

Shaoyao Gancao Decoction (SGD), a traditional Chinese medicine, has been proven to have a good liver protection effect, but the mechanism and pharmacodynamic substances of SGD in the treatment of acute liver injury are still unclear. In this study, an ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) method was established to characterize 107 chemical components of SGD and 12 compounds absorbed in rat plasma samples after oral administration of SGD. Network pharmacology was applied to construct a component-target-pathway network to screen the possible effective components of SGD in acute liver injury. Using lipidomics based on UHPLC-Q-TOF-MS coupled with a variety of statistical analyses, 20 lipid biomarkers were screened and identified, suggesting that the improvement of acute liver injury by SGD was involved in cholesterol metabolism, glycerol-phospholipid metabolism, sphingolipid signaling pathways and fatty acid biosynthesis. In addition, the UHPLC-tandem MS method was established for pharmacokinetics analysis, and 10 potential active components were determined simultaneously within 12 min through the optimization of 0.1% formic acid water and acetonitrile as a mobile phase system. A Pharmacokinetics study showed that paeoniflorin, albiflorin, oxypaeoniflorin, liquiritigenin, isoliquiritigenin, liquiritin, ononin, formononetin, glycyrrhizic acid, and glycyrrhetinic acid as the potential active compounds of SGD curing acute liver injury.