Mobile Network Model Research Articles

Efficient Task Offloading for Mobile Edge Computing in Vehicular Networks

In vechcular networks, a promising approach to enhance vehicle task processing capabilities involves using a combination of roadside base stations or vehicles, there are two challenges when integrating the two offloading modeth: 1) the high mobility of vehicles can easily lead to connectivity interruptions between nodes, which in turn affects the processing of the tasks that are being offloaded; and 2) vehicles on the road are not completely trustworthy, and vehicle tasks that contain private information may suffer from result errors or privacy leakage and other problems. This paper investigates the computing offloading problem for minimizing task completion delay in vehicular networks. Specifically, we design a trust model for mobile in-vehicle networks and construct a migration decision problem to minimize the overall delay of task execution for all vehicle users. The simulation results show that the scheme proposed in this paper can effectively reduce the execution delay of the task compared to the baseline scheme.

Radar Imaging Based UAV Digital Twin for Wireless Channel Modeling in Mobile Networks

Radar Imaging Based UAV Digital Twin for Wireless Channel Modeling in Mobile Networks

Analysis of P2P network system based on two‐stage service and failure repairability

SummaryAlong with the rapid development of the information technology revolution, P2P technology has been more and more widely and deeply researched in the field of mobile wireless, and mobile P2P has become an emerging and popular research field. The particularity of the mobile environment has extremely high requirements on the P2P network topology, resource transition paths and network security. In this paper, a two‐stage queueing model with negative customers and asynchronous failure is established based on a hybrid mobile network model combining the semi‐distributed topology and the fully‐distributed topology. Using the matrix‐geometric solution and Gauss‐Seidel iterative method, the probability distribution of the network model in steady‐state is obtained, and expressions for performance indicators such as the average number of peers in the two stages are given. Numerical experiments were conducted using programming software to analyze the effect of parameters on each indicator. The optimal parameters were solved by constructing the average cost unit time function and social benefit function to provide decision foundation for the mobile P2P network model.

Real-Time Monitoring System for School-Enterprise Dual-Host Sports Person Path Optimization Based on Online Data Interest Community Game Algorithm

This paper expounds the framework and innovation of a school's construction of a "two-three-three system" school-enterprise dual-discipline interactive teaching quality monitoring system. First, based on the online data mining algorithm, this paper proposes a data forwarding topology model for mobile social networks. This model combines the game theory algorithm to conduct real-time monitoring and analysis of the autonomous learning and teaching modes of schools and enterprises. Then, the game theory algorithm will eventually converge. To Nash equilibrium, that is, through the overall optimal solution combined with school-enterprise supervision of practical teaching and teaching evaluation to carry out comprehensive teaching quality monitoring, so as to maximize the teaching quality and improve the effect of teaching quality.

Research on the network handoff strategy based on the best access point name decision.

To improve the network switching performance and efficiency of mobile phone terminals and establish an efficient mobile communication network connection, this paper constructs the SDN+MPTCP+CP (Software Defined Network, Multi-Path TCP, and Mobile Terminal) mobile communication network model and designs a network switching algorithm with a preselected available access point name (APN) based on the potential game method. The constructed network model integrates a 5G mobile communication network, satellite communication, the SDN network, and the MPTCP multi-way communication technology. APN access point is preselected by using Kalman filtering theory, and dual problems are resolved with the Lagrange function. To determine the MPTCP sub-flow transmission path, the differential derivative calculation is introduced. The performance of the network switching strategy is evaluated based on the Jacobian matrix. Then, the game coefficient is designed, and the game function is calculated. A potential game balance point is found, and an updating strategy is formulated to determine the best APN access point. The simulation network model is constructed, and the parameters of the performance evaluation are defined to find the performance comprehensively. The experimental results demonstrated the extreme reliability, stability, and compatibility of the proposed algorithm.

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Due to a tremendous increase in mobile traffic, mobile operators have started to restructure their networks to offload their traffic. New research directions will lead to fundamental changes in the design of future Fifth-generation (5G) cellular networks. For the formal reason, the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory. Any number that can be uniquely calculated by a graph is known as a graph invariant. During the last two decades, innumerable numerical graph invariants have been portrayed and used for correlation analysis. In any case, no efficient assessment has been embraced to choose, how much these invariants are connected with a network graph. This paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical networks. Since K-banhatti sombor invariants (KBSO) and Contrharmonic-quadratic invariants (CQIs) are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or networks. As the hexagonal networks are used in mobile base stations in layered, forms called honeycomb. The review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced forms. The deduced outcomes can be utilized for the modeling of mobile cellular networks, multiprocessors interconnections, microchips, chemical compound synthesis and memory interconnection networks. The results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network (MBSN) for the high load of traffic and minimal traffic also.

Novel Group Mobility Model for Software Defined Future Mobile Networks

Nowadays, a massive amount of data leads to cause network traffic and inflexible mobility in future mobile networks. A new Group Mobility Model (GMM) named MoMo is introduced that addresses the issue of the aforementioned problems. Even though, software defined network (SDN) is functional with network-rooted mobility protocols that enhance the network efficiency. Some existing network-rooted mobility administration methods still undergo handover delay, packet loss, and high signaling cost through handover processing. In this research work, SDN-based fast handover for GMM is proposed. Here, the neighbor number of evolving node transition probabilities of the mobile node (MN) and their obtainable resource probabilities are estimated. This makes a mathematical framework to decide the preeminent number of the evolving nodes and then allot these to mobile nodes virtually with all associations finished by the exploit of Open-Flow tables. The performance examination demonstrates that the proposed SDN rooted GMM technique has the enhanced performance than the conventional handover process and further technique by handover latency, signaling cost, network throughput, and packet loss.

Target Recognition Technology of Multimedia Platform Based on a Convolutional Neural Network

With the rapid development of the Internet, network media, as a new form of information dissemination, has penetrated into people’s daily life. In recent years, with the rapid transformation of Chinese social structure and the rise of self-media platforms, various social contradictions have been highlighted in the form of online public opinion. Especially on online multimedia platforms, the spread of online public opinion is more rapid, which can easily lead to social hotspots. In order to effectively supervise the public opinion information on the Internet, it is necessary to identify the target of the information on the multimedia platform and effectively screen the information, so as to control the network public opinion in the development stage. Aiming at the above problems, we propose a multitarget retrieval method based on a convolutional neural network, which uses multitarget detection algorithm to locate multitarget regions and extract regional features and uses cosine distance as a similarity measure for multitarget recognition. In view of the slow feature extraction speed of VGG model, a lightweight mobile network model is proposed to replace the original VGG model on the mobile phone to reduce the retrieval time and realize the recognition of specific targets on the multimedia platform, and it is applied to the verification of image recognition on the multimedia platform. The results show that the algorithm proposed in this paper has great advantages in multitarget recognition tasks.

A node trust factor linked privacy preservation model in 5G networks with a multi-stage authentication model

There has been a dramatic expansion in the number and frequency of relationships between entities in mobile telecommunications networks over the last several years. For theseinteractions to be profitable, the parties involved must be able to count on one another. Since mobile networks require trust and reputation models to create feasible communications in 5 G and beyond networks, a collection of entities can create chains of operations between cross-operators with privacy and trustworthiness through mobile telecommunication network models. A major impediment to widespread communication beyond 5 G networks is the absence of automated, efficient, and scalable models for creating security and trust. Multitenancy and active infrastructure sharing will be major facilitators of new business models as 5 G networks expand. New security concerns are emerging due to these new opportunities, which is one of the primary obstacles for the mass implementation of 5 G networks. There are several ways in which attackers might use 5G-enabled situations to undertake lateral moves and disrupt 5 G services and infrastructure resources. Existing trust and security models cannot deal with the dynamic nature of the 5 G infrastructural threats or the multi-tenant security issues. Hence, we propose a Mobile Node Trust Factor Linked Privacy Preservation model for 5 G multi-stage authentication models in this work. A threat model for multi-stage authentication scenarios in an underlying 5 G network infrastructure is used to demonstrate how this model can be used. For secure end-to-end communication, authorization and strong authentication are necessary. A combination of anonymous authentication and authorization is needed to preserve the privacy of mobile devices which share personal information. The proposed model is compared with the traditional models and the proposed model results exhibit better performance.

Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

The polymerase chain reaction (PCR) test is not only time-intensive but also a contact method that puts healthcare personnel at risk. Thus, contactless and fast detection tests are more valuable. Cough sound is an important indicator of COVID-19, and in this paper, a novel explainable scheme is developed for cough sound-based COVID-19 detection. In the presented work, the cough sound is initially segmented into overlapping parts, and each segment is labeled as the input audio, which may contain other sounds. The deep Yet Another Mobile Network (YAMNet) model is considered in this work. After labeling, the segments labeled as cough are cropped and concatenated to reconstruct the pure cough sounds. Then, four fractal dimensions (FD) calculation methods are employed to acquire the FD coefficients on the cough sound with an overlapped sliding window that forms a matrix. The constructed matrixes are then used to form the fractal dimension images. Finally, a pretrained vision transformer (ViT) model is used to classify the constructed images into COVID-19, healthy and symptomatic classes. In this work, we demonstrate the performance of the ViT on cough sound-based COVID-19, and a visual explainability of the inner workings of the ViT model is shown. Three publically available cough sound datasets, namely COUGHVID, VIRUFY, and COSWARA, are used in this study. We have obtained 98.45%, 98.15%, and 97.59% accuracy for COUGHVID, VIRUFY, and COSWARA datasets, respectively. Our developed model obtained the highest performance compared to the state-of-the-art methods and is ready to be tested in real-world applications.

Cloud secured mobile e-learning system solutions using machine learning approach

Specifically designed learning models namely the mobile learning systems are conveniently available at a mobile device. The following are the difficulties that mobile learning systems must overcome: connection speed, processing power, adaptability, and the difficulty of attaining security. In this paper, we design and construct a cloud-based secure mobile e-learning management system (CSMELMS) for educational purposes. The system is made up of three primary modules, which are the client, mobile network, and cloud model. The client model makes the users to access the data via mobile application, which is connected to a mobile network using the client model. The authentication server is responsible for ensuring that each user attempting to access the system is who they claim to be. The CSMELMS system was developed using the Java programming language with the database being provided by MySQL. When the model is tested using a machine learning algorithm, it was found to be effective at enabling it for available to students when and where they need them. The utilisation of machine learning concepts enables better and secured operations for educational purposes. The results of simulation shows that this model is better at providing a better e-learning portal for students and teachers.

A DQN-Based Cache Strategy for Mobile Edge Networks

The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse, thus the network transmission delay and system throughput can be improved significantly. Since the duplicate content transmissions between edge network and remote cloud can be reduced, the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great extent. This paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning algorithm. The deep neural network (DNN) and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network (DQN), in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning solution. The parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method, so the DQN algorithm could converge to the optimal solution quickly, and the network performance of the content caching policy can be optimized. The simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.

Mobile Network Coverage Prediction Based on Supervised Machine Learning Algorithms

The need for wider coverage and high-performance quality of mobile networks is critical due to the maturity of Internet penetration in today&#x2019;s society. One of the primary drivers of this demand is the dramatic shift toward digitalization due to the Covid-19 pandemic impact. Meanwhile, the emergence of the 5G wireless standard and the increasingly complex actual operating environment of mobile networks make the traditional prediction model less reliable. With the recent advancements and promising capabilities of machine learning (ML), it is seen as an alternative to the traditional approaches for ground to ground (G2G) mobile communication coverage prediction. In this study, various ML models have been tested and evaluated to develop an ML-based received signal strength prediction model for mobile networks. However, the challenge is to identify a practical ML model that can fulfill the computing speed criteria while still meeting the prediction accuracy. A total of six categories of ML models, namely Linear Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Regression Trees (RT), Ensembles of Trees (ET), and Gaussian Process Regression (GPR) that consists of more than 20 types of established algorithms/kernels have been tested and evaluated in this paper to identify the best contender among them, in terms of speed and accuracy. Findings from the evaluation showed that the GPR model is the most accurate model for Reference Signal Received Power (RSRP) prediction in terms of <inline-formula> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>, followed by ET, RT, SVM, ANN and LR. Nevertheless, prediction speed and model training times are also important factors in determining the most practical model for RSRP prediction for several real-world mobile network planning applications. Finally, the ET model with Random Forest (RF) algorithm has been selected and highly recommended as the most practically employed ML model for developing rigorous RSRP predictions model in multi-frequency bands and multi-environment. The developed prediction model is capable of being utilized for the network analysis and optimization.

Comparison of the Mobile and Fixed Nano/Micro-Scale Systems by Using Monte Carlo Simulation for Different Viscosity Values

Nano networks that are defined as a communication of nano-sized devices (Nano Machines) are a new nano/micro-scale system subject. In this study, on the contrary to the literature, a mobile nano network model has been used to analyze the proposed system in a different viscosity environment by using some Physics law. Because it is known that besides the molecules, which transport information between transmitter and receiver, the transmitter and receiver parts of the biological cells can be mobile in the blood or any other fluid media. In addition, the effect of viscosity which is an important part of the environment of the nano-device systems and distance between transmitter and receiver are analyzed detailed in Matlab with analytical and simulation results by comparing the fixed and mobile nano scale systems. It is concluded that when the receiver and transmitter are mobile, distance between them changes and finally this affects the probability of the received molecules at the receiver. As is expected, the fraction of received molecules is obtained the highest when the viscosity of the environment and distance are the lowest for both fixed and mobile system models. Also positions of receiver and transmitter show that when the distance of transmitter and receiver increases from the origin, fraction of received molecules decreases.

Stochastic modelling and analysis of mobility models for intelligent software defined internet of vehicles

Data traffic forwarding and network optimization is essential to effective congestion management in software-defined vehicular networks, and it is necessary for software-defined vehicle networks (SDVN). SDVN is needed to optimize connection performance and network controls in dense and sparse networks to govern data flow between nodes as effectively as possible. Intelligent software-defined internet of vehicles (iSDIoVs) has recently emerged as a potential technology for future vehicular networks. It manages the vehicular ad hoc networks systematically. The link connection of moving vehicles from the central SDN controller may fail. It impacts the efficiency and communication performance because of the lack of connection between vehicles and infrastructure (V2I). The researchers have analyzed the network performance and mobility models in a dense and sparse network to maximize network performance by iSDIoVs. By integrating heterogeneous systems such as IEEE 802.11p and cellular networks into vehicular ad-hoc networks, it is possible to reduce buffer occupancy in iSDIoV and control the mobility and delay bound analysis in V2V communication. The SDN will provide flexibility and reliability to the vehicular networks. An SDN controller manages the data flow in the vehicular network and controls the flow matching rules in the control plane. The iSDIoV and queuing models improve the response time and resource utilization and enhance the network complexity analysis for traffic management services.

Practical mobile network planning and optimization for Thai smart cities: Towards a more inclusive globalization

Globalization has facilitated productivity gains and economic growth and for decades. It has helped integrate emerging economies, spread technology, knowledge, culture, and created an interconnected global community. Smart cities depend on a robust and flexible mobile network foundation, and yet, Thailand is leading the way in the ASEAN. Using Thailand as a case study, this paper presents a practical mobile network planning and optimization framework (PMNPO) that’s workable for the fourth-generation up to sixth-generation networks in smart cities, especially in an industrial estate. While theoretical frameworks play a vital role in optimizing network performance, such frameworks may not be practical enough. This study applies a mixed quantitative research method on actual applications (case studies) and qualitative research. The PMNPO is a management function of pseudo-codes. A workable cost-sharing model for mobile networks consists of CAPEX, OPEX, feasibility study, and site value management (mobile network operators). The fair factor (0.2), the payback (5 years), the NPV ($0.5 million), and the IRR (20%) reveal a promising financial prospect. The PMNPO is practical for Thailand, with a mean acceptance rate of 4.83. Therefore, the findings support stakeholders, policymakers, and decision-makers, in setting telecommunication policies. The results can apply to other developing countries or catalyze a similar research type.

A Trust-Based Predictive Model for Mobile Ad Hoc Networks

The Internet of things (IoT) is a heterogeneous network of different types of wireless networks such as wireless sensor networks (WSNs), ZigBee, Wi-Fi, mobile ad hoc networks (MANETs), and RFID. To make IoT a reality for smart environment, more attractive to end users, and economically successful, it must be compatible with WSNs and MANETs. In light of this, the present paper discusses a novel quantitative trust model for an IoT-MANET. The proposed trust model combines both direct and indirect trust opinion in order to calculate the final trust value for a node. Further, a routing protocol has been designed to ensure the secure and reliable end-to-end delivery of packets by only considering trustworthy nodes in the path. Simulation results show that our proposed trust model outperforms similar existing trust models.

Design and analysis 5G mobile network model to enhancement high-density subscribers

To obtain a high data rate that is commensurate with the growing demand for internet services, the fifth generation (5G) cellular networks will use the bandwidth beyond 6 GHz, called millimeters waves (mm-waves), to obtain a higher. The first phase (phase I) of the 5G network design for high user density, where the optimized microcells are deployed at carrier frequency 700 MHz with 20 MHz bandwidth. The second phase (phase II) of the design consists of the deployment of microcells which are operating at 3.6 GHz with 100 MHz bandwidth; this phase is planned to cover 200000 users within the province. The third phase (phase III) of the design is represented by the deployment of picocells, which are planned to operate at 26 GHz frequency and bandwidth 500 MHz; this phase is planned to cover 3,500,000 users within the province. Two types of modulation are adopted for the network (orthogonal frequency division multiplexing (OFDM) and 256 quadrature amplitude modulation (QAM)); the overall performance of the network is studied with regards to the percentage of coverage, power overlapping ratio, frequency interference, and quality of service (QoS).

Evaluation of Flying Ad Hoc Network Topologies, Mobility Models, and IEEE Standards for Different Video Applications

Nowadays, drones became very popular with the enhancement of the technological progress of moving devices with a connection to each other, known as Flying Ad Hoc Network (FANET). It is used in most worldwide necessary life scenarios such as video recording, search and rescue, military missions, moving items between different areas, and many more. This leads to the necessity to evaluate different network strategies between these flying drones, which are essential to improve their quality of performance in the field. Several challenges must be addressed to effectively use FANET, to provide stable and reliable transmission for different types of data during vast changing topologies, such as different video sizes, different types of mobility models, recent Wireless Fidelity standards, types of routing protocols used, security problems, and many more. In this paper, a fully comprehensive analysis of FANET will be done to evaluate and enhance these challenges that concern different video types, mobility models, and IEEE 802.11n standards for best performance, by measuring throughput, retransmission attempt, and delay metrics. The result shows that Gauss–Markov mobility model gives the highest result using Ad Hoc On-Demand Vector and lowest delay, whereas for retransmission attempts, 2.4 GHz frequency has the lowest as it can reach more coverage area than 5 GHz.

Mobility models for next generation wireless mesh network

&lt;span&gt;Wireless mesh network (WMN) is a new trend in wireless communication promising greater flexibility, reliability, and performance over traditional wireless local area network (WLAN). Test bed analysis and emulation plays an essential role in valuation of software defined wireless network and node mobility is the prominent feature of next generation software defined wireless network. In this study, the mobility models employed for moving mobile stations in software defined wireless network are explored. Moreover, the importance of mobility model within software defined wireless mesh network for enhancing the performance through handover-based load balancing is analyzed. The mobility models for the next generation software defined wireless network are explored. Furthermore, we have presented the mobility models in the mininet-Wi-Fi test bed, and evaluated the performance of Gauss Marko’s mobility model.&lt;/span&gt;