Connectivity Of Mobile Devices Research Articles

A Review on 5G Antenna: Challenges and Parameter Enhancement Techniques

The need for a high-speed mobile network has increased due to the COVID19 pandemic.5G is the newest and most sophisticated technology designed to handle the demands of the internet. The 5G network ensures connection security and simplifies mobile device connectivity to wireless devices. This paper explains every parameter related to 5G technology that has been covered in various papers. It addresses some of the difficulties that 5G technology faces. The development of Vivaldi, conformal, MIMO antennas satisfies the requirements of the 5G mobile network and presents opportunities to overcome obstacles. In the paper, MIMO antenna is discussed along with various techniques for enhancing its parameters, such as appropriate substrate selection, antenna element placement, and mutual coupling reduction. Different techniques like isolation, DGS, slot, metamaterial, neutralizing line, and frequency reconfigurations are explained under mutual coupling reduction techniques. A comparative analysis of various antenna feeding techniques is presented in the paper. The article provides details on how various parameters are affected by parameter enhancement techniques.

MOBILE HEALTH (MHEALTH) INNOVATIONS FOR PUBLIC HEALTH FEEDBACK: A GLOBAL PERSPECTIVE

In recent years, Mobile Health (mHealth) innovations have emerged as a transformative force in the realm of public health, revolutionizing the way healthcare is delivered and monitored worldwide. This review presents a comprehensive overview of the latest advancements in mHealth technologies, focusing particularly on their role in soliciting and utilizing public health feedback on a global scale. The proliferation of smartphones and mobile applications has paved the way for novel approaches to healthcare delivery, monitoring, and data collection. Through leveraging the ubiquity and connectivity of mobile devices, mHealth initiatives have facilitated enhanced communication between healthcare providers and patients, enabling real-time monitoring of health metrics, adherence to treatment regimens, and timely interventions. One of the key aspects of mHealth innovations is their capacity to engage diverse populations and solicit feedback regarding various aspects of public health, ranging from disease outbreaks to healthcare service quality. Through interactive platforms and user-friendly interfaces, individuals can provide valuable insights, report symptoms, and participate in surveys, thereby contributing to the generation of actionable data for public health interventions. Furthermore, mHealth solutions have demonstrated significant promise in overcoming barriers to healthcare access, particularly in underserved and remote communities. By delivering health information, diagnostic tools, and remote consultations via mobile platforms, these innovations have expanded the reach of essential healthcare services, thereby bridging gaps in healthcare delivery and improving health outcomes on a global scale. However, challenges such as privacy concerns, technological literacy, and disparities in digital access persist, underscoring the importance of equitable deployment and user-centered design in mHealth initiatives. Moreover, the integration of mHealth solutions into existing healthcare infrastructure requires careful coordination among stakeholders, including policymakers, healthcare providers, and technology developers. Mobile Health (mHealth) innovations hold immense potential to revolutionize public health feedback mechanisms on a global scale. By harnessing the power of mobile technologies, these initiatives can empower individuals, enhance healthcare delivery, and inform evidence-based public health policies for a healthier future.
 Keywords: Mobile Health, mHealth, Public Health, Healthcare, Innovation, Review.

Effect of the COVID-19 Pandemic on Medical Students at the University of Jeddah, Saudi Arabia.

Background This study aims to identify the coronavirus disease 2019 (COVID-19) pandemic impact on the academic, financial, psychological, and hygienic aspects of medical students at the University of Jeddah. Methodology Three hundred fifty medical students from the University of Jeddah were sent an online questionnaire using a simple consecutive type of sampling for this cross-sectional study. Students from the preclinical and clinical years were included. The survey consisted of 39 items: four questions were for the demographic domain, 14 items were for the academic domain, another 14 were for hygienic, psychological, and financial aspects, and seven items assessed the effect on elective. A P-value of less than 0.05 was regarded as significant during the statistical analysis, which was conducted using Statistical Package for Social Sciences (SPSS) version 25 (IBM Corp., Armonk, NY, USA). Results There were 333 responses, 174 (52.3%) of them were males. The commonest age group was 21-23 years (n=237, 71.2%). Most of the participants were living in Jeddah (n=307, 92.2%). The majority (54%, n=180) agreed or strongly agreed that "Fluctuations in lecture timing is one of the downsides of online teaching" and "The hands-on experience has suffered greatly" and 42% (n=140) strongly agreed on "Technical issues like poor WiFi connection and lack of computer or mobile devices make online teaching difficult". One hundred five (31.5%) of the participants had an elective during the pandemic, of which 41 (39%) did not have it in training centers. In terms of the mental aspect, 154 (46.2%) students were impacted by the COVID-19 pandemic, and 111 (72.1% of them) developed anxiety or depression. Social media (n=150, 45%) was the most preferable source of information during the pandemic.". Conclusion The COVID-19 pandemic affected the academic advancement of medical students, particularly during the years of their clinical training at the University of Jeddah. Our findings also showed that the COVID-19 pandemic had an impact on the students' financial, hygienic, and mental health, which led to increased depression and concern about visiting hospitals and providing care for patients, which ultimately prevented them from acquiring the necessary clinical skills.

Use of Wireless Technologies in the Industrial Environment With Blockchain

This paper presents the development of an OpenThread network within the framework of a BMBF project, which is intended to transfer production data to a private blockchain in order to store the data in a tamper-proof and transparent manner. Among other things, special radio modules were developed for this purpose which support the OpenThread and Bluetooth Low Energy radio standards and thus additionally enable the connection of mobile devices to the private blockchain. During the conception and development of the radio modules as well as the communication protocols, emphasis was placed on ensuring that the radio network meets the high requirements for its use in an industrial environment, such as commissioning through plug & play,robustness, availability, data transfer rate, latency and, above all, IT security.

5G Hybrid System Design and Energy Efficient Resource Allocation Deployment

5G communication provide a promising platform for new, innovative and diverse enhanced mobile broadband (eMBB) and massive device connectivity applications, such as streaming media, machine vision and Internet of Things (IoT), real-time and dynamic data processing, intensive computation. However, 5G multimedia devices deployment relies on the coverage of base stations, which is inefficient and costly in wide-area coverage and physical penetration. In this paper, a 5G and wide-area Ad Hoc network fusion architecture is proposed to flexibly provide scalable 5G and extensible low-power devices interconnection liberated from geographical restriction, which consists of a low-power wide-area network and an edge processing gateway. Moreover, the intelligent edge gateway near a specific base station can support real-time ultra-high-definition video streams access and achieve traffic optimization by compressing, intelligent identification and preprocessing of the video streams to alleviate traffic congestion. The coverage capacity efficiency of wide-area Ad Hoc networks is restricted by the "funnel effect" in multihop cascading, and adaptive resource allocation strategies will present a promising approach to realize energy-efficient deployment. A non-convex optimization problem is formulated to maximize the energy-efficient deployment of Ad Hoc network. Then, a coordination and optimization strategy of internal resource allocation in deployed multihop nodes based on Lagrange relaxation algorithm was presented to solve the optimization problem. The actual system deployment and real measurement proved that the system function is running normally and stably. The experimental simulation test results show that the proposed 5G wide-area Ad Hoc network can effectively make up for the adaptive streaming needs of 5G coverage blind spots. Compared with static resource allocation, the proposed resource allocation and deployment scheme reduces energy consumption by 42.31%.

CROOD: Estimating crude building occupancy from mobile device connections without ground-truth calibration

The occupancy information in buildings is fundamental for smart buildings (e.g., occupant-centric controls). Opportunistic occupancy detection (OOD) uses connection data of mobile devices. While OOD has been developed and applied, one critical drawback is that it requires the ground truth of occupants to calibrate, which is limited to gather. Here, we introduce CROOD: a capture and recapture (CRc) inspired OOD. In ecology, CRc has been established for the estimation of animal populations, when the manual count is impossible. We adopt this unique approach to estimate the number of mobile devices in a building. Then, using a simple estimate on the total population, CROOD determines the relationship between the numbers of occupants and mobile devices. We evaluate CROOD numerically on the synthetic building populations and demonstrate its application in a university library using WiFi connection data. We find that CROOD can estimate the number of mobile devices and subsequently the number of occupants with 1–2 weeks to converge a reasonable accuracy. A long term experiment shows that CROOD can adapt to varying population characteristics (e.g., occupants bring more mobile devices), outperforming the reference sample mean estimator. The real building implementation demonstrates that while in the first 1–2 weeks, the sample mean estimator is superior, eventually CROOD adapts and provides better estimates without ground-truth calibration. Although CROOD has a limitation of building types and systems, our results envision that CROOD could be a viable addition to other OOD methods to better utilize existing mobile device connection data to estimate occupancy in buildings.

A Secure Ticket-Based Authentication Mechanism for Proxy Mobile IPv6 Networks in Volunteer Computing

Technology advances—such as improving processing power, battery life, and communication functionalities—contribute to making mobile devices an attractive research area. In 2008, in order to manage mobility, the Internet Engineering Task Force (IETF) developed Proxy Mobile IPv6, which is a network-based mobility management protocol to support seamless connectivity of mobile devices. This protocol can play a key role in volunteer computing paradigms as a user can seamlessly access computing resources. The procedure of user authentication is not defined in this standard; thus, many studies have been carried out to propose suitable authentication schemes. However, in the current authentication methods, with reduced latency and packet loss, some security and privacy considerations are neglected. In this study, we propose a secure and anonymous ticket-based authentication (SATA) method to protect mobile nodes against existing security and privacy issues. The proposed method reduces the overhead of handover authentication procedures using the ticket-based concept. We evaluated security and privacy strengths of the proposed method using security theorems and BAN logic.

An efficient mobility prediction model for resource allocation in mobile cloud computing

Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.

Saving bandwidth and energy of mobile and IoT devices with link predictions

Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised. Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android. We evaluated our approach with real sensor data and showed how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.

Renegotiating family: Social media and forced migration

Abstract The increasing, although uneven, trends of global connectivity and uptake of mobile devices have growing implications for local and transnational family relationships and activities. For refugees and their families separated by physical geography and conflict, social media platforms can effectively bridge, and at times collapse, the distance between ‘here’ and ‘there’. Offering a range of audio-, video-, and text-based interaction, these platforms represent a portal to engage with transnational family. Drawing upon a digital ethnography of resettled refugees over the course of a year using online methods, this article examines how the general ubiquity of connective media in New Zealand has created opportunities for co-presence with overseas family and how this impacts close proximal relationships. This article explores these tensions—the possibilities for connection and co-presence alongside its associated challenges, burdens, and affective paradoxes—to unpack the implications, positive and negative, as to how resettled refugees living in New Zealand use social media to practise family.

IPAS: A deep Monte Carlo Tree Search-based intelligent pilot-power allocation scheme for massive MIMO system

Massive Multiple-Input-Multiple-Output (MIMO) is a promising technology to meet the demand for the connection of massive devices and high data capacity for mobile networks in the next generation communication system. However, due to the massive connectivity of mobile devices, the pilot contamination problem will severely degrade the communication quality and spectrum efficiency of the massive MIMO system. We propose a deep Monte Carlo Tree Search (MCTS)-based intelligent Pilot-power Allocation Scheme (iPAS) to address this issue. The core of iPAS is a multi-task deep reinforcement learning algorithm that can automatically learn the radio environment and make decisions on the pilot sequence and power allocation to maximize the spectrum efficiency with self-play training. To accelerate the searching convergence, we introduce a Deep Neural Network (DNN) to predict the pilot sequence and power allocation actions. The DNN is trained in a self-supervised learning manner, where the training data is generated from the searching process of the MCTS algorithm. Numerical results show that our proposed iPAS achieves a better Cumulative Distribution Function (CDF) of the ergodic spectral efficiency compared with the previous suboptimal algorithms.

A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices

Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA.

A STUDY ON CUSTOMER SATISFACTION TOWARDS SMARTPHONE USERS

Smart phones are getting smarter each day. In view of the rapid development, multi-functionality, ubiquity and connectivity of mobile devices,it offers a new and potentially powerful market for the Smartphone users. The objective of the study is to identify the brand of Smartphone preferred by the respondents, to analyse the satisfaction of the consumers towards Smartphone usage and to compare the satisfaction score among selected demographic variables. The sample size is 200 respondents.The statistical tools used in this study are Simple percentage,Descriptive analysis, Rank analysis,ANOVA,T-test and Regression analysis.The concept of customer satisfaction has attached much attention in recent years. Organizations that try to analyze this concept should begin with an understanding of various customer satisfaction models.They are living in world which is totally networked with the communication.With the advent of fast technology,the world has become a global village.With the click of small buttons on a computer one can easily get any information according to user needs and choice. Recent developments in mobile technologies have produced a new kind of device, a programmable mobile phone, the smart phone. Generally, smart phone users can program any application which is customized for needs. Furthermore,they can share these applications in online market.Therefore, smart phone and its application are now most popular keywords in mobile technology. Hence, it is vital to analyse the customer satisfaction among Smartphone users and make necessary changes in the technology in order to with stand in the competitive market. The findings state that the consumers are more satisfied with user friendliness, picture and sound quality of smartphones but not satisfied with after sales service pirce of current brand of Smartphone has an influence over satisfaction.Samsung brand is mostly preferred by the respondents.

Joint optimization for broadcast service rendering and call connectivity for dual stack mobile devices

INTRODUCTION: Multimedia Broadcast Multicast Service (MBMS) primarily targets broadcasting mobile television contents and video streaming services. Consumers worldwide are rapidly using Dual SIM Dual Standby (DSDS) device that utilizes a single common Radio Frequency Integrated Circuit (RFIC) for su

Improving students’ learning with a mobile augmented reality approach – the EduPARK game

PurposeThe purpose of this paper is to present a survey study that analyzes mobile learning through students’ opinion regarding the use of mobile devices for learning, including their advantages and difficulties, as well as the educational value of a specific mobile learning strategy, reified in the EduPARK game, after an experience of exploring it in formal and non-formal educational contexts.Design/methodology/approachThis paper comprises a survey study where mobile learning is analyzed through the eyes of 244 students attending the second or third cycles of basic education. The acknowledged advantages and difficulties of the use of mobile devices for learning, as well as the educational value and usability of a specific mobile learning strategy, using the EduPARK app and game, were analyzed.FindingsResults revealed that most students owned a mobile device and were able to use them to learn. They had a positive perspective regarding mobile learning and valued the advantages of being easy to find up-to-date information, motivating for learning and easy to carry along. Difficulties are related to the use of mobile devices, such as requiring an internet connection, its slowness and prohibition of mobile devices in schools. The EduPARK game achieved an average educational value scale of 83.8 and an average system usability scale of 80.2, indicating its high educational value and usability for students.Originality/valueThis paper presents empirical evidence regarding the effectiveness of the integration of mobile game-based augmented reality approaches in basic education to promote students’ learning. It also includes an example of excellent cross-subjects educational materials comprising a very useful tool for teachers and students to explore scientific knowledge by accessing appealing information on biological and historical references of a local urban park.

Pedagogical Practices of Mobile Learning in K-12 and Higher Education Settings

The advancement of mobile technology has influenced teaching and learning at various educational levels. The mobility, ubiquity, and connectivity of mobile devices help to foster meaningful learning experience across contexts and in different subject areas. Positive results from mobile learning have been reported in the current literature. To help to further the discussion and provide references for practitioners at various educational levels, this paper categorizes main pedagogical practices of mobile learning in K-12 and higher education settings into nine types: situated learning, communication and collaborative learning, game-based learning, inquiry-based learning, informal learning, personalized learning, behaviorist learning activities, learning content access and review, student engagement and motivation. Mobile learning in distance education is also discussed.

Efficient Mobile Clouds: Forecasting the Future Connectivity of Mobile and IoT Devices to Save Energy and Bandwidth

Use cases in the Internet of Things (IoT) and in mobile clouds often require the interaction of one or more mobile devices with their infrastructure to provide users with services. Ideally, this interaction is based on a reliable connection between the communicating devices, which is often not the case. Since most use cases do not adequately address this issue, service quality is often compromised.Aimed to address this issue, this paper proposes a novel approach to forecast the connectivity and bandwidth of mobile devices by applying machine learning to the context data recorded by the various sensors of the mobile device. This concept, designed as a microservice, has been implemented in the mobile middleware CloudAware, a system software infrastructure for mobile cloud computing that integrates easily with mobile operating systems, such as Android.We evaluate our approach with real sensor data and show how to enable mobile devices in the IoT to make assumptions about their future connectivity, allowing for intelligent and distributed decision making on the mobile edge of the network.

Google timeline accuracy assessment and error prediction

Google Location Timeline, once activated, allows to track devices and save their locations. This feature might be useful in the future as available data for evidence in investigations. For that, the court would be interested in the reliability of these data. The position is presented in the form of a pair of coordinates and a radius, hence the estimated area for tracked device is enclosed by a circle. This research focuses on the assessment of the accuracy of the locations given by Google Location History Timeline, which variables affect this accuracy and the initial steps to develop a linear multivariate model that can potentially predict the actual error with respect to the true location considering environmental variables. The determination of the potential influential variables (configuration of mobile device connectivity, speed of movement and environment) was set through a series of experiments in which the true position of the device was recorded with a reference Global Positioning System (GPS) device with a superior order of accuracy. The accuracy was assessed measuring the distance between the Google provided position and the de facto one, later referred to as Google error. If this Google error distance is less than the radius provided, we define it as a hit. The configuration that has the largest hit rate is when the mobile device has GPS available, with a 52% success. Then the use of 3G and 2G connection go with 38% and 33% respectively. The Wi-Fi connection only has a hit rate of 7%. Regarding the means of transport, when the connection is 2G or 3G, the worst results are in Still with a hit rate of 9% and the best in Car with 57%. Regarding the prediction model, the distances and angles from the position of the device to the three nearest cell towers, and the categorical (non-numerical) variables of Environment and means of transport were taking as input variables in this initial study. To evaluate the usability of a model, a Model hit is defined when the actual observation is within the 95% confidence interval provided by the model. Out of the models developed, the one that shows the best results was the one that predicted the accuracy when the used network is 2G, with 76% of Model hits. The second model with best performance had only a 23% success (with the mobile network set to 3G).

Massive CSI Acquisition for Dense Cloud-RANs With Spatial-Temporal Dynamics

Dense cloud radio access networks (cloud-RANs) provide a promising way to enable scalable connectivity and handle diversified service requirements for massive mobile devices. To fully exploit the performance gains of dense cloud-RANs, channel state information of both the signal link and interference links is required. However, with limited radio resources for training, the channel estimation problem in dense cloud-RANs becomes a high-dimensional estimation problem, i.e., the number of measurements will be typically smaller than the dimension of the channel. In this paper, we shall develop a generic high-dimensional structured channel estimation framework for dense cloud-RANs, which is based on a convex structured regularizing formulation. Observing that the wireless channel possesses ample exploitable statistical characteristics, we propose to convert the available spatial and temporal prior information into appropriate convex regularizers. Simulation results demonstrate that exploiting the spatial and temporal dynamics can achieve good estimation performance even with limited training resources. The alternating direction method of multipliers algorithm is further adopted to solve the resultant large-scale high-dimensional channel estimation problems. The proposed framework thus enjoys modeling flexibility, low training overhead, and computation cost scalability.

A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

In recent years, the smart city concept, which involves multiple disciplines, for example, smart healthcare, smart transportation, and smart community, has become popular because of its ability to improve urban citizens' quality of life. However, most services in these areas of smart cities have become data-driven, thus generating big data that require seamless real-time access, sharing, storing, processing, and analysis anywhere at any time for intelligent decision making to improve living standards. In this scenario, MCC can play a vital role by allowing a mobile device to access and offload big-data-related tasks to powerful cloudlet servers attached to many wireless APs, thus ensuring that the QoS demands of end users are met. However, the connectivity of mobile devices with a given AP is not continuous, but rather sporadic with varying signal strengths. Furthermore, the heterogeneity of the cloudlet resources and the big data application requests place additional challenges in making optimal code execution decision. To cope with this problem, this article proposes a mobility- aware optimal resource allocation architecture, namely Mobi-Het, for remote big data task execution in MCC that offers higher efficiency in timeliness and reliability. The system architecture and key components of the proposed resource allocation service are presented and evaluated. The results of experiments and simulations have demonstrated the effectiveness and efficiency of the proposed Mobi-Het architecture for mobile big data applications.