Mobile Devices Research Articles

Task Offloading and Resource Allocation for Augmented Reality Applications in UAV-Based Networks Using a Dual Network Architecture

This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource allocation for AR tasks with dependent relationships. This work specifically focuses on the problem of dependent tasks in AR applications within UAV-based networks. This problem has not been thoroughly addressed in previous research. A dual network architecture-based task offloading (DNA-TO) algorithm is proposed, leveraging the DNA framework to enhance decision-making in reinforcement learning while mitigating noise. In addition, a Karush–Kuhn–Tucker-based resource allocation (KKT-RA) algorithm is proposed to optimize resource allocation. Various simulations using real-world movement data are conducted. The results indicate that our proposed algorithm outperforms existing approaches in terms of latency and energy efficiency.

LiteFer: An Approach Based on MobileViT Expression Recognition.

Facial expression recognition using convolutional neural networks (CNNs) is a prevalent research area, and the network's complexity poses obstacles for deployment on devices with limited computational resources, such as mobile devices. To address these challenges, researchers have developed lightweight networks with the aim of reducing model size and minimizing parameters without compromising accuracy. The LiteFer method introduced in this study incorporates depth-separable convolution and a lightweight attention mechanism, effectively reducing network parameters. Moreover, through comprehensive comparative experiments on the RAFDB and FERPlus datasets, its superior performance over various state-of-the-art lightweight expression-recognition methods is evident.

The Impact of Air Pollution Information on Individuals' Exercise Behavior: Empirical Study Using Wearable and Mobile Devices Data.

Physical exercise and exposure to air pollution have counteracting effects on individuals' health outcomes. Knowledge on individuals' real-time exercise behavior response to different pollution information sources remains inadequate. This study aims to examine the extent to which individuals avoid polluted air during exercise activities in response to different air pollution information sources. We used data on individuals' exercise behaviors captured by wearable and mobile devices in 83 Chinese cities over a 2-year time span. In our data set, 35.99% (5896/16,379) of individuals were female and 64% (10,483/16,379) were male, and their ages predominantly ranged from 18 to 50 years. We further augmented the exercise behavior data with air pollution information that included city-hourly level measures of the Air Quality Index and particulate matter 2.5 concentration (in µg/m3), and weather data that include city-hourly level measures of air temperature (ºC), dew point (ºC), wind speed (m/s), and wind direction (degrees). We used a linear panel fixed effect model to estimate individuals' exercise-aversion behaviors (ie, running exercise distance at individual-hour, city-hour, or city-day levels) and conducted robustness checks using the endogenous treatment effect model and regression discontinuity method. We examined if alternative air pollution information sources could moderate (ie, substitute or complement) the role of mainstream air pollution indicators. Our results show that individuals exhibit a reduction of running exercise behaviors by about 0.50 km (or 7.5%; P<.001) during instances of moderate to severe air pollution, and there is no evidence of reduced distances in instances of light air pollution. Furthermore, individuals' exercise-aversion behaviors in response to mainstream air pollution information are heightened by different alternative information sources, such as social connections and social media user-generated content about air pollution. Our results highlight the complementary role of different alternative information sources of air pollution in inducing individuals' aversion behaviors and the importance of using different information channels to increase public awareness beyond official air pollution alerts.

Exploring the impact of paid over-the-top service and mobile network profiles in watching TV content on mobile devices

PurposeThe television (TV) content ecosystem has shifted from traditional broadcasting systems to dedicated content producers and over-the-top (OTT) services. However, less empirical effort has been paid to the actual behaviors of the mobile users who watch TV content when explaining the impact of OTT service and mobile network profiles in watching TV content. This study aims to investigate the impact of gratifications and attitude formed by mobile TV users on actual mobile TV watching behaviors, as well as the moderating impacts of paid OTT service subscriptions and mobile network profiles, based on gratification theory, cognition–affect–behavioral (CAB) framework, sunk cost effect and walled-garden effect.Design/methodology/approachThis study employs the generalized linear model (GLM) with generalized estimating equations (GEE) to test hypothesized relationships. A total of 338 mobile phone users who have been watching TV content using a mobile phone participated in the survey. The moderating variables, 4 types of paid streaming platform subscriptions, were classified based on the walled gardens formed by mobile telecom services.FindingsThe study’s results revealed that obtained gratifications and opportunity constructs substantially influenced a mobile phone user’s attitude and behaviors. Additionally, mobile network profiles and the degree of access to paid platform services played significant moderating roles in the relationship between users’ attitudes and behavior.Originality/valueThis research enriches the existing OTT service literature and is one of the pioneering studies investigating the walled-garden effect’s role in mobile phone users’ actual watching behaviors, offering valuable practical implications for the OTT platform providers.

Signal quality evaluation of an in-ear EEG device in comparison to a conventional cap system.

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness.

5G Network with Hexagonal SDN Control for Highly Secure Multimedia Communication

The 5G communication network is a promising technology that offers user-centric connectivity to customers, enabling quick, high-capacity, and latency-free access to many applications. Software Defined Networking Controller (SDNC) is an emerging technology that is used for increasing the security and maintaining the dynamic nature of mobile devices in latency and energy-assured 5G wireless communication. Different SDN topology open flow controllers are used in the literature for increasing the throughput and security of the network but they possess some drawbacks such as frequent location update (LU) overhead, high latency, and high energy consumption. This paper proposes a hexagonal software defined network (H-SDN) controller to overcome these challenges. The proposed H-SDN topology is used to preserve Intermediate Message Layer (IML) location during multimedia data transmission. A Core Switch (CS) updates an IML's location within clusters of cells, as long as it remains within the cluster. However, in the proposed method, there will be a location update on the server if the IML departs the cluster. Experiment results show that the proposed method can increase security, which in turn increases throughput, while reduces the location update costs, energy consumption, and transmission latency.

Implementasi Sanitasi Data Pada Sistem Operasi Android dengan Metode Gutmann

Data security and privacy are essential for all mobile device users, especially Android. However, deleted data can generally still be recovered using forensic analysis of the storage media hardware with data recovery software. For this reason, you need a data deletion application capable of deleting safely. Many methods are available for safe deletion, such as applying different numbers of passes (overwriting). This research proves that most available methods cannot be deleted entirely and can be recovered, so a data deletion application was created using the most up-to-date method, the Gutmann Method. This method uses 35 number of operands to overwrite the file. Applications are made by analyzing system requirements and implementing them into mobile programming. Then, a comparison test is carried out with other existing applications. The results show that applications created using the Gutmann method are 33% less likely to find file traces than the best comparison application.

Effect of a prescription digital therapeutic for chronic insomnia on post-treatment insomnia severity, depression, and anxiety symptoms: results from the real-world DREAM study.

Chronic insomnia is a substantial public health burden that often presents with co-occurring depression and anxiety. Randomized clinical trials and preliminary real-world evidence have shown that digitally delivered cognitive-behavioral therapy for insomnia (dCBT-I) is associated with improvements in insomnia, but real-world evidence is needed to determine the true impact of digital CBT-I. This pragmatic study aimed to evaluate the benefits of treating chronic insomnia with a tailored prescription digital therapeutic in a real-world population. This prospective, single-arm clinical study involved adults aged 22-75 with chronic insomnia living in the US who had access to a mobile device. Participants accessed the FDA-cleared prescription digital therapeutic (PDT; Somryst®) over a 9-week intervention period. The PDT delivers cognitive-behavioral therapy for insomnia via six interactive treatment cores and daily sleep diaries used for tailoring treatment. Participants completed validated patient-reported instruments at baseline, before completing treatment cores, immediately post-intervention, and at 6-month and 1-year follow-ups. The Insomnia Severity Index [ISI], the 8-item Patient Health Questionnaire [PHQ-8], and the Generalized Anxiety Disorder-7 scale [GAD-7] were used to determine the effect of the PDT on insomnia, depression, and anxiety. After screening, 1565 adults accessed the PDT. 58% of those who began the program completed Core 4, established as exposure to all mechanisms of action in the digital therapeutic. For those who completed assessments for all 6 cores (48.4%), the ISI was lowered from 18.8 to a mean of 9.9 (P <.001). These scores continued to be lower than baseline at immediate post (11.0), 6-month (11.6), and 1-year follow-ups (12.2) (P <.001). The results of the PHQ-8 and GAD-7 also show significant decreases at all measured timepoints from baseline (P <.001). Of the patients that began the program, 908 (58.0%) were considered adherent and 733 (46.8%) completed all 6 cores. Data from the DREAM study contributes to the growing body of clinical evidence of how patients are utilizing a PDT in the real world, outside of controlled settings, offering insights for clinicians who use these therapeutics in practice. ClinicalTrials.gov, identifier NCT04325464.

Understanding mobile health literacy among the working-age population: A cross-sectional study in Thailand

Mobile health (mHealth) literacy refers to the ability of individuals to seek, find, understand, and evaluate health information accessed via mobile devices. In Thailand, an assessment of mobile health literacy among the working-age population (25-59 years old) across the country is still needed to gain a better understanding of the current landscape of this concept as well as to create strategies for improvement. As a result, this study aimed 1) to assess mHealth literacy among the Thai working-age population, 2) to investigate differences in mHealth literacy among the Thai working-age population classified by demographic variables, and 3) to identify the predictive factors affecting mHealth literacy among the Thai working-age population. Cross-sectional survey research was conducted in this study with samples consisting of 600 working-age individuals derived from six regions across the country. Respondents were randomly selected using a multi-stage sampling method. Data were collected by a self-administered questionnaire. Mean, SD, t-test, One-Way ANOVA, and multiple regression analysis [MRA] were used for data analysis at a .05 level of significance. The results found that 1) the samples showed a high level of mHealth literacy (Mean = 4.32, SD = 0.58) 2), differences in mHealth literacy level were classified by gender, age, income, education, and region, and 3) perceived behavioral control, attitudes, and subjective norms were factors significantly affecting mHealth literacy. These findings can be used to plan and develop strategies for improving mHealth literacy among the Thai working-age population more effectively.

Efficiency Boosts in Human Mobility Data Privacy Risk Assessment: Advancements within the PRUDEnce Framework

With the exponential growth of mobility data generated by IoT, social networks, and mobile devices, there is a pressing need to address privacy concerns. Our work proposes methods to reduce the computation of privacy risk evaluation on mobility datasets, focusing on reducing background knowledge configurations and matching functions, and enhancing code performance. Leveraging the unique characteristics of trajectory data, we aim to minimize the size of combination sets and directly evaluate risk for trajectories with distinct values. Additionally, we optimize efficiency by storing essential information in memory to eliminate unnecessary computations. These approaches offer a more efficient and effective means of identifying and addressing privacy risks associated with diverse mobility datasets.

Intermediate-grained kernel elements pruning with structured sparsity

Neural network pruning provides a promising prospect for the deployment of neural networks on embedded or mobile devices with limited resources. Although current structured strategies are unconstrained by specific hardware architecture in the phase of forward inference, the decline in classification accuracy of structured methods is beyond the tolerance at the level of general pruning rate. This inspires us to develop a technique that satisfies high pruning rate with a small decline in accuracy and has the general nature of structured pruning. In this paper, we propose a new pruning method, namely KEP (Kernel Elements Pruning), to compress deep convolutional neural networks by exploring the significance of elements in each kernel plane and removing unimportant elements. In this method, we apply a controllable regularization penalty to constrain unimportant elements by adding a prior knowledge mask and obtain a compact model. In the calculation procedure of forward inference, we introduce a sparse convolution operation which is different from the sliding window to eliminate invalid zero calculations and verify the effectiveness of the operation for further deployment on FPGA. A massive variety of experiments demonstrate the effectiveness of KEP on two datasets: CIFAR-10 and ImageNet. Specially, with few indexes of non-zero weights introduced, KEP has a significant improvement over the latest structured methods in terms of parameter and float-point operation (FLOPs) reduction, and performs well on large datasets.

Design of a Tri-band Minkowski Fractal MIMO Antenna for FR2 5G Applications

The growing importance of fifth-generation (5G) communication technologies for the Internet-of-Things have made it necessary to design multi-element antennas that operate within the millimetre-wave spectrum. However, simultaneously achieving multiple operating frequencies, high gain and good isolation between the elements of such small-sized antennas is a challenging task. In this research, a composite right/left hand millimetre-wave fractal antenna was designed for 5G mobile terminals, in order to simultaneously satisfy these key design goals. This aim was accomplished by designing a 2 x 2 multiple-input, multiple output (MIMO) antenna based on the second-order Minkowski fractal using an electromagnetics simulation software, Computer Simulation Technology (CST) Studio Suite ®. Two composite-right/left hand structures were included on the reverse side of the antenna, to reduce the coupling between the MIMO antenna elements. Simulation results show that the designed antenna operates at three frequency bands within the millimetre-wave spectrum, namely 25.52 GHz – 28.23 GHz, 36.5 GHz – 37.5 GHz, and 47.7 GHz – 49.7 GHz. The maximum antenna gains at these three bands are 6.9 dBi, 7.5 dBi, and 6.0 dBi, respectively. Also, good isolation is realized between the antenna elements at the operating frequencies, with a minimum value of 10.8 dB. With a spacing of 0.03λ between the MIMO antenna elements, the overall size of the designed antenna was 30 mm x 12 mm x 0.508 mm, which compares positively with other related fractal antenna designs. The designed antenna shows good prospects for integration into various mobile terminal devices to aid 5G-based millimetre-wave communications.

Diagnosing Dyslexia in Early School-Aged Children Using the LSTM Network and Eye Tracking Technology

Dyslexia, often referred to as a specific reading disability, affects many students around the world. It is a neurological disorder that affects the ability to recognise words, and it causes difficulties in writing and reading comprehension. Previous computer-based methods for the automatic detection of dyslexia in children have had low efficiency due to the complexity of the test administration process and the low measurement reliability of the attention measures used. This paper proposes the use of a student’s mobile device to record the spatio-temporal trajectory of attention, which is then analysed by deep neural network long short-term memory (LSTM). The study involved 145 participants (66 girls and 79 boys), all of whom were children aged 9 years. The input signal for the neural network consisted of recorded observation sessions, which were packets containing the child’s spatio-temporal attention trajectories generated during task performance. The training set was developed using stimuli from Benton tests and an expert opinion from a specialist in early childhood psychology. The coefficients of determination of R2∼0.992 were obtained for the proposed model, giving an accuracy of 97.7% for the test set. The ease of implementation of this approach in school settings and its non-stressful nature make it suitable for use with children of different ages and developmental stages, including those who have not yet learned to read. This enables early intervention, which is essential for effective educational and emotional support for children with dyslexia.

Bangla Optical Character Recognition for Mobile Platforms: A Comprehensive Cross-Platform Approach

The development of Optical Character Recognition (OCR) systems for Bangla script has been an area of active research since the 1980s. This study presents a comprehensive analysis and development of a cross-platform mobile application for Bangla OCR, leveraging the Tesseract OCR engine. The primary objective is to enhance the recognition accuracy of Bangla characters, achieving rates between 90% and 99%. The application is designed to facilitate the automatic extraction of text from images selected from the device&amp;apos;s photo library, promoting the preservation and accessibility of Bangla language materials. This paper discusses the methodology, including the preparation of training datasets, preprocessing steps, and the integration of the Tesseract OCR engine within a Dart programming environment for cross-platform functionality. This integration provides that the application could be introduced on mobile platforms without substantial alterations. The results demonstrate significant improvements in recognition accuracy, making this application a valuable tool for various practical applications such as data entry for printed Bengali documents, automatic recognition of Bangla number plates, and the digital archiving of vintage Bangla books. These improvements are crucial to further enhance the usability and reliability of Bangla OCR on mobile devices. Our cross-platform method for Bangla OCR on mobile devices provides a strong solution with exceptional identification accuracy, which helps in preserving and making Bangla language information accessible in digital format. This study has significant implications for future research and advancement in the field of optical character recognition (OCR) for intricate writing systems, especially in mobile settings.

Teaching the Teachers: Technological Proficiency and Professional Growth in Rural Education

Preparing teachers in small rural schools to effectively integrate technology into their teaching practices is vital for fostering 21st-century skills in students, despite the unique challenges these settings present. To explore the specific challenges and opportunities related to the professional development of teachers in small rural schools, in the context of increasing digitalisation of education. This study employs a systematic review as its research method, aiming to identify, critically evaluate, and synthesize relevant studies that shed light on this context. Teacher education programmes that adeptly incorporate technology, such as mobile devices, into their curricula offer a critical support system not just for urban educators, but importantly for rural teacher candidates who may have limited access to higher education resources. Smartphones and tablets, for instance, can serve as valuable tools for these teachers, allowing them constant and remote access to professional development materials and effective learning resources. The technology also presents an opportunity for iterative learning, where immediate feedback can guide improvements in teaching strategies. Examples include the use of virtual avatars as teaching aids instead of in-person instruction. As the push for digitalisation continues, it is incumbent upon educational institutions to articulate the benefits and returns of such investments, especially in the context of small rural schools where resources may be limited and the challenges distinct. This articulation must be framed in terms that resonate with educators, students, communities, and potential educational funders in these rural contexts.

A convenient archaeological ruins identification method through elevation information extraction from CORONA stereo pairs

Three-dimensional (3-D) stereo images can be generated via computer-based image processing of CORONA stereo pairs. To a certain extent, important terrain and surface feature data extracted from these stereo images can improve the survey of archaeological sites and the identification and mapping of major landscapes. In this study, we focused on the identification of the archaeological ruins of Liangzhu City. An optical stereo model (red/blue stereo image) of the Liangzhu site was created through computer-based mosaicking and processing of CORONA remote-sensing stereo pairs taken in the 1960s and 1970s. By importing the optical stereo model into mobile phones, tablet computers, and other mobile devices, the research team undertook real-time locating of ruins via human observation, on-site investigation, and image overlay during a field survey and identified several Liangzhu-period dams, some of which have been confirmed via archaeological field investigations. The research team later applied the same method to the identification of tombs at the site of the mausoleums of the six emperors of the Southern Song dynasty. The results further prove that this method is feasible and reliable and can be widely promoted and used for the identification of archaeological ruins.

Advance in Applied Cryptography Theory: Survey and New Results. Part 1. Key Cryptography

In the current paper, consisting from two parts, are presented both results already published before (but hard for access) and new once. Actuality of this work is firstly in a fact that recently has been obtained a number of new results in area of applied cryptography that are needed both in a clarification and be put into practice. This is namely the main goal of the current paper. The setting problem in the first paper part concerns to a complexity of symmetric cipher breaking while in the second part of the paper is discussed, so called, keyless cryptography, namely: wiretap channel concept, execution of communication channels which allow to provide information security without of key exchange procedure between legal correspondences. In the part widely used methods of applied mathematics, namely: algebra, number, probability and information theories. Computer simulation also used there. A novelty of the first part of work consists in the following: first of all it is clarified the sense of a key lifetime limitation for different symmetric cipher modes, secondly, it is explained an approach of cipher breaking by the use of quantum computers, finely, the key authentication for the Diffie ‒ Hellman protocol based on the mobile device pairing technology is investigated in detail. In the second part of the current paper has been presented a vulnerability of Dean ‒ Goldsmith cryptosystem under some extension of attacks. The main results of this paper are: estimation of the key lifetime of single key for symmetric cipher in CBC mode, clarifying of Grover’s algorithm breaking of symmetric ciphers by brute force attack, development of a method for authentication of Diffie ‒ Hellman values based on pre-distributed sequences, selection of ciphers which allow to execute with Shamir’s protocol without any key sharing in advance, breaking of Dean ‒ Goldsmith protocol under some conclusions, proof the fact regarding of a possible breakability of the key sharing protocol over noiseless communication channels. Practical application of paper results consists in the fact of stimulation the correct choice of ciphers and their parameters in order to provide their resistance to different attacks and more attention to algorithms of keyless cryptography.

A FORENSICS ACTIVITY LOGGER TO EXTRACT USER ACTIVITY FROM MOBILE DEVICES

Mobile devices have become one of the most often used tools in everyday life, mostly because of the importance of its apps. In this case, mobile devices become personal trackers for daily activities that provide important information about the user by recording extra data in addition to the user's personal information. As a consequence of this information gathering, several tools are now accessible for use on mobile devices, however each tool is only able to provide discrete details about a certain application or activity. Consequently, the present research proposes a technology that allows investigators to get a detailed report and time line of all operations performed on the device. This report combines data from several sources to generate a unique collection of facts. Furthermore, an example is provided to illustrate how the solution works, highlighting the practicality of the instrument as well as the way in which investigators need to use it.

Efficient microservices offloading for cost optimization in diverse MEC cloud networks

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

Routing Task in Dynamic Fog Computing Network

Relevance. In the context of traffic growth, transition to IMT-2030 networks and Telepresence services, the tasks of efficient management of network and computing resources occupy a special place. Fog computing as the next stage of decomposition of the architecture of multi access edge cloud computing is designed to radically change the models and methods of distributing computing tasks, influencing, among other things, the user-operator interaction models. At the moment, there is a whole layer of scientific problems for revealing the possibilities of fog computing. They can be divided into a number of areas, such as: study of models and methods for implementing services of ultra-reliable and ultra-low latency communications, defined in IMT-2020 networks; study of models and methods for ensuring quality of service, including quality of experience; study of methods for live migration of microservices, as well as groups of typical microservices; study of models and methods for distributing resources of dynamic fog computing while ensuring the stability of fog computing forms (clusters, nebulae); one of the potentially effective areas is research in the field of combining federated learning with dynamic fog computing. This paper solves a routing problem that can be attributed to the direction of infrastructure research in dynamic fog computing.Problem statement: research and develop the effective methods for routes determination in a dynamic fog computing network, including tasks of migrating microservices of telepresence services. Goal of the work: research and development of an effective method for ways determination to migrate microservices in communication networks using fog computing technologies, which could take into account not only the characteristics of connections (edges of the network graph), but also the computing capabilities and limitations of fog computing devices, as well as their features - the dynamics of computing devices. Methods: in order to test the proposed method, the program model was developed in the NS-3 modeling environment. Result. Analysis of the results showed the effectiveness of the proposed method within the framework of the task and various application scenarios. Novelty. A microservice migration method has been developed as a new routing protocol in a dynamic fog computing environment, which differs from the known ones in that this method ensures the interaction of fog computing devices for migrating microservices, while achieving a reduction in energy consumption by fog computing devices by 41% and reducing the share of lost packages on average up to 34%. Practical significance: The developed method can be used to implement fog computing in conditions of mobility of end devices in order to achieve the requirements of promising services of IMT-2030 networks.