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Journal articles on the topic 'Mobile Network Traffic'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Dr.R., Udayakumar, Dr Suvarna Yogesh Pansambal, Dr Yogesh Manohar Gajmal, Vimal Dr.V.R., and Sugumar Dr.R. "User Activity Analysis Via Network Traffic Using DNN and Optimized Federated Learning based Privacy Preserving Method in Mobile Wireless Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 2 (2023): 66–81. http://dx.doi.org/10.58346/jowua.2023.i2.006.

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Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis for better performances. These previously suggested

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Katsigiannis, Michail. "Mobile Network Offloading." International Journal of Interdisciplinary Telecommunications and Networking 4, no. 3 (2012): 40–53. http://dx.doi.org/10.4018/jitn.2012070103.

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The mobile data traffic growth and the high fraction of indoor-generated traffic push mobile operators to devise new deployment strategies such as mobile network offloading. The purpose of this paper is to evaluate the energy consumption and the deployment cost, based on the demanded traffic level, for a joint macro-femtocell network which enables mobile network offloading in Helsinki Metropolitan Area by 2015. This deployment is compared to an optimized only macro cellular network. The study tries to resolve under what conditions, in terms of demanded traffic, deployment cost and energy consu

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Ray, Shweta. "Intelligent-Driven Green Resource Allocation for Industrial Internet of Things In 5g Heterogeneous Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 55–67. http://dx.doi.org/10.22214/ijraset.2022.45161.

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Abstract: Mobile networks' energy consumption is rising in tandem with the volume of traffic and the number of people utilising mobile technology. To maintain the long-term survival of the next generation of mobile networks, there must be an emphasis on energy efficiency. By minimising the network's power consumption and proposing an energy-efficient network architecture, this thesis addresses the issue of increasing 5G and beyond network efficiency. The first component of this thesis focuses on base stations (BSs), the most energy-intensive part of mobile networks. Mobile network providers of

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Kim, Hwangnam, Woonghee Lee, Hyunsoon Kim, Hwantae Kim, and Jaehyeuk Michael Yang. "Protecting Download Traffic from Upload Traffic over Asymmetric Wireless Links." Wireless Communications and Mobile Computing 2018 (July 12, 2018): 1–15. http://dx.doi.org/10.1155/2018/1283420.

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Many varied mobile device networks have been developed with the advancement of communication and network technologies. Cellular data networks are currently the most widely used, and the number of cellular network subscriptions has increased steadily. Most recent wireless access technologies employ asymmetric uplinks and downlinks because mobile subscribers usually download contents from the Internet. Therefore, most cellular network service providers allocate more bandwidth to downlinks than uplinks for mobile subscribers. However, this asymmetry can have unexpected influence on network perfor

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Gabilondo, Álvaro, Zaloa Fernández, Roberto Viola, et al. "Traffic Classification for Network Slicing in Mobile Networks." Electronics 11, no. 7 (2022): 1097. http://dx.doi.org/10.3390/electronics11071097.

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Network slicing is a promising technique used in the smart delivery of traffic and can satisfy the requirements of specific applications or systems based on the features of the 5G network. To this end, an appropriate slice needs to be selected for each data flow to efficiently transmit data for different applications and heterogeneous requirements. To apply the slicing paradigm at the radio segment of a cellular network, this paper presents two approaches for dynamically classifying the traffic types of individual flows and transmitting them through a specific slice with an associated 5G quali

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Van Thuc, HOANG, PHAM Van Ngoc, DOAN Thi Thanh Thao, VU Chien Thang, PHAM Thanh Nam, and MAC Thi Phuong. "OVERVIEW STUDY OF MOBILE NETWORK TRAFFIC FOR BTS STATIONS." Vinh University Journal of Science 53, no. 4A (2024): 5–14. https://doi.org/10.56824/vujs.2024a076a.

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In recent years, Machine Learning (ML) has become a crucial and promising tool for forecasting and solving a wide range of complex problems. The rapid development of machine learning is closely linked to technological advancements and has also driven the growth of the AI community and open- source tools (e.g., TensorFlow, Keras, PyTorch, fast.ai). This enables researchers to deploy and apply machine learning algorithms more effectively. This paper provides an overview of mobile network traffic at BTS stations, conducted from a data-driven perspective, focusing on extracting and transforming da

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Hailu, Dawit Hadush. "Performance Evaluation of Ethernet Network for Mobile Fronthual Networks." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (2017): 287. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp287-298.

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<p>Increasing mobile data traffic due to the rise of both smartphones and tablets has led to high-capacity demand of mobile data network. To meet the ever-growing capacity demand and reduce the cost of mobile network components, Cloud Radio Access Network (C-RAN) has emerged as a promising solution. In such network, the mobile operator’s Remote Radio Head (RRH) and Base Band Unit (BBU) are often separated and the connection between them has very tight timing and latency requirements imposed by Common Public Radio Interface (CPRI) and 3rd Generation Partnership Project (3GPP). This fronth

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Jiang, Shi Bao, Chuan Feng Bai, and Cui Feng Du. "Traffic Forecast Based on Empirical Mode Decomposition and RBF Neural Network." Advanced Materials Research 846-847 (November 2013): 1270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1270.

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Traffic analysis and forecast are the classic topics of telecommunication research, as they provide strategic ground to address the issues such as mobile networks traffic jam, network coverage planning design, marketing management etc. On the basis of the empirical mode decomposition theory and methods, this article first implements multi-scale analysis of the time series of traffic, then it goes on to executes RBF neural network based on the different compositions, and finally reach the forecast expectation of voice traffics of selected base stations.

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Dong, Zan Qiang, and Su Bin Shen. "A Node Mobile Handoff Model Based on Resource Allocation in Wireless Mobile Networks." Advanced Materials Research 488-489 (March 2012): 417–23. http://dx.doi.org/10.4028/www.scientific.net/amr.488-489.417.

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The technology on network interconnection handoff with the QoS guarantee is becoming an important research issue in wireless mobile networks. Most of the current researches focus on the cell mobile networks, the schemes of which are comparatively complex in solving the QoS guarantee in mobile node handoff and the utilization rate of resource is lower. In order to reduce these disadvantages to the minimal extent, the model (HDBRA) on the mobile node handoff with service guarantee applied in the wireless mobile networks is presented in the paper based on the dynamic network bandwidth resource al

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Ouyang, Ye, and M. Hosein Fallah. "An Analysis of Traffic and Throughput for UMTS Packet Core Networks." International Journal of Interdisciplinary Telecommunications and Networking 2, no. 2 (2010): 1–26. http://dx.doi.org/10.4018/jitn.2010040101.

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Mobile packet services are penetrating mobile markets rapidly. The mobile industry relies heavily on data services to replace traditional voice services. Designing a reliable packet service network is critical to the mobile operators’ ability to maintain their core competence in a data service market. The current literature provides many practical tools and theoretical methods to design, plan and dimension Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) radio networks but overlooks the algorithms of network plan and dimensioning for the core

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Bai, Huiwen, Guangjie Liu, Weiwei Liu, Yingxue Quan, and Shuhua Huang. "N-Gram, Semantic-Based Neural Network for Mobile Malware Network Traffic Detection." Security and Communication Networks 2021 (April 23, 2021): 1–17. http://dx.doi.org/10.1155/2021/5599556.

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Mobile malware poses a great challenge to mobile devices and mobile communication. With the explosive growth of mobile networks, it is significant to detect mobile malware for mobile security. Since most mobile malware relies on the networks to coordinate operations, steal information, or launch attacks, evading network monitor is difficult for the mobile malware. In this paper, we present an N-gram, semantic-based neural modeling method to detect the network traffic generated by the mobile malware. In the proposed scheme, we segment the network traffic into flows and extract the application l

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Moon, Daekyeong. "Network Traffic Characteristics and Analysis in Recent Mobile Games." Applied Sciences 14, no. 4 (2024): 1397. http://dx.doi.org/10.3390/app14041397.

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The landscape of mobile gaming has evolved significantly over the years, with profound changes in network reliability and traffic patterns. In the early 2010s, mobile games faced challenges due to unreliable networks and primarily featured asynchronous gameplay. However, in the current era, modern mobile games benefit from robust network connectivity, mirroring PC gaming experiences by relying on persistent connections to game servers. This shift prompted us to conduct an in-depth traffic analysis of two mobile games that represent opposite ends of the genre spectrum: a massively multiplayer g

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Wu, Xiaorui, and Chunling Wu. "CLPREM: A real-time traffic prediction method for 5G mobile network." PLOS ONE 19, no. 4 (2024): e0288296. http://dx.doi.org/10.1371/journal.pone.0288296.

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Network traffic prediction is an important network monitoring method, which is widely used in network resource optimization and anomaly detection. However, with the increasing scale of networks and the rapid development of 5-th generation mobile networks (5G), traditional traffic forecasting methods are no longer applicable. To solve this problem, this paper applies Long Short-Term Memory (LSTM) network, data augmentation, clustering algorithm, model compression, and other technologies, and proposes a Cluster-based Lightweight PREdiction Model (CLPREM), a method for real-time traffic predictio

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Ruifeng, Pan, Mengsheng Wang, Jindan Zhang, Brij Gupta, and Nadia Nedjah. "Organizational Impact of Spatiotemporal Graph Convolution Networks for Mobile Communication Traffic Forecasting." International Journal of Data Warehousing and Mining 21, no. 1 (2025): 1–19. https://doi.org/10.4018/ijdwm.368563.

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Communication traffic prediction is of great guiding significance for communication planning management and improvement of communication service quality. However, due to the complex spatiotemporal correlation and uncertainty caused by the spatial topology and dynamic time characteristics of mobile communication networks, traffic prediction is facing enormous challenges. We propose a mobile traffic prediction method using dynamic spatiotemporal synchronous graph convolutional network (DSSGCN). DSSGCN has designed multiple components, which can effectively capture the heterogeneity in the local

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López Rodríguez, Fernando, Ugo Silva Dias, Divanilson Campelo, Robson Oliveira Albuquerque, Se-Jung Lim, and Luis García Villalba. "QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks." Sensors 19, no. 6 (2019): 1335. http://dx.doi.org/10.3390/s19061335.

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The next generation of 5G networks is being developed to provide services with the highest Quality of Service (QoS) attributes, such as ultra-low latency, ultra-reliable communication, high data rates, and high user mobility experience. To this end, several new settings must be implemented in the mobile network architecture such as the incorporation of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), along with the shift of processes to the edge of the network. This work proposes an architecture combining the NFV and SDN concepts to provide the logic for Quality of

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Sultan, Kashif, Hazrat Ali, Adeel Ahmad, and Zhongshan Zhang. "Call Details Record Analysis: A Spatiotemporal Exploration toward Mobile Traffic Classification and Optimization." Information 10, no. 6 (2019): 192. http://dx.doi.org/10.3390/info10060192.

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The information contained within Call Details records (CDRs) of mobile networks can be used to study the operational efficacy of cellular networks and behavioural pattern of mobile subscribers. In this study, we extract actionable insights from the CDR data and show that there exists a strong spatiotemporal predictability in real network traffic patterns. This knowledge can be leveraged by the mobile operators for effective network planning such as resource management and optimization. Motivated by this, we perform the spatiotemporal analysis of CDR data publicly available from Telecom Italia.

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Zhang, Ling, and Cheng Wan. "Dynamic Path Planning Design for Mobile Sink with Burst Traffic in a Region of WSN." Wireless Communications and Mobile Computing 2019 (February 3, 2019): 1–8. http://dx.doi.org/10.1155/2019/2435712.

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In mobile wireless sensor networks, priori-trail planning for the mobile sink is a commonly used solution to data collection from the whole network, for its low protocol overhead. However, these trail-based approaches lack efficient load balance mechanism to handle burst WSN traffic, which needs to be sent to the base station correctly with low delay. This paper proposed a dynamic path planning for mobile sink to balance load and avoid traffic bottleneck. It contains grid partition of the network, priori-trail creation, burst-traffic awareness and estimation, resources collaborative strategy,

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Astrakhantsev, Andrii, Larysa Globa, Andrii Davydiuk, and Oleksandra Sushko. "ADJUSTING THE PARAMETERS OF MACHINE LEARNING ALGORITHMS TO IMPROVE THE SPEED AND ACCURACY OF TRAFFIC CLASSIFICATION." Information and Telecommunication Sciences, no. 2 (December 21, 2023): 26–32. http://dx.doi.org/10.20535/2411-2976.22023.26-32.

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Background. Telecommunications developments lead to new mobile network technologies and especially 5G, which has only recently been launched, sixth generation of which is already under active development. The development of new technologies influence on both types of mobile traffic (V2V, IoT) and leads to the significant increase in the volume of existing traffic types. Currently, existing methods of traffic processing are not adapted to such changes, which may lead to a deterioration in the quality of service. Objective. The purpose of the paper is to analyze the effectiveness of machin

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Kim, Dae-Young, and Seokhoon Kim. "Network-Aided Intelligent Traffic Steering in 5G Mobile Networks." Computers, Materials & Continua 65, no. 1 (2020): 243–61. http://dx.doi.org/10.32604/cmc.2020.011253.

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Kochetkova, Irina, Anna Kushchazli, Sofia Burtseva, and Andrey Gorshenin. "Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models." Future Internet 15, no. 9 (2023): 290. http://dx.doi.org/10.3390/fi15090290.

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Fifth-generation (5G) networks require efficient radio resource management (RRM) which should dynamically adapt to the current network load and user needs. Monitoring and forecasting network performance requirements and metrics helps with this task. One of the parameters that highly influences radio resource management is the profile of user traffic generated by various 5G applications. Forecasting such mobile network profiles helps with numerous RRM tasks such as network slicing and load balancing. In this paper, we analyze a dataset from a mobile network operator in Portugal that contains in

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Hailu, Dawit Hadush. "QoS Performance of Integrated Hybrid Optical Network in Mobile Fronthual Networks." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 1 (2017): 189. http://dx.doi.org/10.11591/ijeecs.v7.i1.pp189-204.

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<p>Cloud Radio Access Network (C-RAN) has emerged as a promising solution to meet the ever-growing capacity demand and reduce the cost of mobile network components. In such network, the mobile operator’s Remote Radio Head (RRH) and Base Band Unit (BBU) are often separated and the connection between them has very tight timing and latency requirements. To employ packet-based network for C-RAN fronthaul, the carried fronthaul traffic are needed to achieve the requirements of fronthaul streams. For this reason, the aim of this paper is focused on investigating and evaluating the feasibility

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Zeng, Jun, Hao Wang, and Wei Luo. "Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks." Sensors 22, no. 19 (2022): 7112. http://dx.doi.org/10.3390/s22197112.

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Driven by growing mobile traffic, millimeter wave (mmWave) communications have recently been developed to enhance wireless network capacity. Due to insufficient coverage and the lack of support for mobility, mmWave is often deployed in the ultra-dense small cells of the 5G heterogeneous network. In this article, we first summarize the characteristics of the 5G heterogeneous network from the viewpoints of devices, spectra, and networks. We then propose a triple-band network structure which incorporates licensed bands, sub-6GHz unlicensed bands, and mmWave bands to support various types of mobil

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Lyandres, V. "Mobile network synthesis strategy." Information and Control Systems, no. 1 (February 19, 2019): 98–101. http://dx.doi.org/10.31799/1684-8853-2019-1-98-101.

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Introduction:Effective synthesis of а mobile communication network includes joint optimisation of two processes: placement of base stations and frequency assignment. In real environments, the well-known cellular concept fails due to some reasons, such as not homogeneous traffic and non-isotropic wave propagation in the service area.Purpose:Looking for the universal method of finding a network structure close to the optimal.Results:The proposed approach is based on the idea of adaptive vector quantization of the network service area. As a result, it is reduced to a 2D discrete map split into zo

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Hu, Liang, Jiejun Hu, Tao Fu, Jiyang Bao, and Feng Wang. "Asymmetric network information cache based on mobile traffic in software-defined network." Advances in Mechanical Engineering 11, no. 1 (2019): 168781401881989. http://dx.doi.org/10.1177/1687814018819894.

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Software-defined network is an encouraging research area that realizes updates throughout an entire network, and a wireless network is ubiquitous in an up-to-date world. The combination of these techniques, which is referred to as a wireless software-defined network, has been a significant development in numerous testbeds. The extensive use of distributed controllers that achieved elastic extension and are fault tolerant in large-scale wireless networks is hopeful. Despite their profits, they generate significant overhead in synchronizing network information. Thus, power-hungry wireless softwa

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Soelistijanto, Bambang, and Vittalis Ayu. "Improving Traffic Load Distribution Fairness in Mobile Social Networks." Algorithms 15, no. 7 (2022): 222. http://dx.doi.org/10.3390/a15070222.

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Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most “social” nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load

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Wang, Jian Qun, Xu Dong Li, and Ya Fei Xiong. "The Influence of Future Vehicle Safety Network Impact on Traffic Flow." Applied Mechanics and Materials 505-506 (January 2014): 985–89. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.985.

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With the rapid development of high-speed mobile networks, the mobile applications related to vehicle safety, navigation systems are increasingly present in our lives, it is more and more easy for the driver to understand the situation on the road ahead, and this kind of change will greatly affect future traffic conditions. This article uses cellular automaton to simulate basic road sections, considering two modes of vehicle network safety applications may affect the future traffic flow, through the simulation, analysis the basic traffic flow data, conclude how the future vehicle network safety

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Kannan, Shathya Pranav Sujithra Rajesh. "PDE based Analysis for Propagation of Disturbance by Users Mobility in Mobile Network System." Spectrum of Emerging Sciences 2, no. 2 (2023): 1–5. http://dx.doi.org/10.55878/ses2022-2-2-1.

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Analyzing the nature of mobile user mobility in a given network system is one of the basic metric for the traffic performance analysis in land-mobile cellular communications. This paper focuses on the partial differential equation (PDE) based analysis on the mobile user mobility; and proposes a new concept of user flow density wave in the mobile network system. The theoretical formulation for measuring the characteristics of network traffic performance deals with the propagation of disturbance produced by the velocity and density characteristics of the mobile users in a given network system; w

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Soos, Gabor, Daniel Ficzere, and Pal Varga. "Towards Traffic Identification and Modeling for 5G Application Use-Cases." Electronics 9, no. 4 (2020): 640. http://dx.doi.org/10.3390/electronics9040640.

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To analyze next-generation mobile networks properly, there is a need to define key performance indicators (KPIs). Testing signaling only or just partial domains of the network have been replaced with end-to-end testing methodologies. With the appearing of machine-to-machine (M2M) applications, this question became even harder, since there is no direct user feedback. Quality of experience cannot be measured accurately in M2M applications, even if the network operates correctly and without failures. There are dozens of new—but theoretical—use-cases for 5G; however, these are not tested on a live

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Shen, Dawei, Wei Yan, Yuhuai Peng, Yanhua Fu, and Qingxu Deng. "Congestion Control and Traffic Scheduling for Collaborative Crowdsourcing in SDN Enabled Mobile Wireless Networks." Wireless Communications and Mobile Computing 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/9821946.

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Currently, a number of crowdsourcing-based mobile applications have been implemented in mobile networks and Internet of Things (IoT), targeted at real-time services and recommendation. The frequent information exchanges and data transmissions in collaborative crowdsourcing are heavily injected into the current communication networks, which poses great challenges for Mobile Wireless Networks (MWN). This paper focuses on the traffic scheduling and load balancing problem in software-defined MWN and designs a hybrid routing forwarding scheme as well as a congestion control algorithm to achieve the

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Islam, Imdadu, Jugal Krishna Das, and Siddique Hossain. "Modeling of mixed traffic for mobile cellular network." Journal of Telecommunications and Information Technology, no. 1 (June 25, 2023): 83–89. http://dx.doi.org/10.26636/jtit.2007.1.801.

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The most convenient way of presenting one- or two-dimensional offered traffic in a network by Markovian chain to evaluate quality of service (QoS). Pictorial presentation of chain becomes very complicated for three dimensional traffic case, required for voice data integrated service/mixed traffic of mobile cellular network; hence application of cut or node equations become a cumbersome job. This paper proposes a model of three-dimensional traffic in a network for both unlimited and limited user case. Here direct analytical method is introduced instead of Markovian chain to achieve traffic para

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Gui, Xiaolin, Yuanlong Cao, Ilsun You, Lejun Ji, Yong Luo, and Zhenzhen Luo. "A Survey of techniques for fine-grained web traffic identification and classification." Mathematical Biosciences and Engineering 19, no. 3 (2022): 2996–3021. http://dx.doi.org/10.3934/mbe.2022138.

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<abstract><p>After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then,

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Xu, Guoliang, Ming Xu, Yunzhi Chen, and Jiaqi Zhao. "A Mobile Application-Classifying Method Based on a Graph Attention Network from Encrypted Network Traffic." Electronics 12, no. 10 (2023): 2313. http://dx.doi.org/10.3390/electronics12102313.

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Classifying mobile applications from encrypted network traffic is a common and basic requirement in network security and network management. Existing works classify mobile applications from flows, based on which application fingerprints and classifiers are created. However, mobile applications often generate concurrent flows with varying degrees of ties, such as low discriminative flows across applications and application-specific flows. So flow-based methods suffer from low accuracy. In this paper, a novel mobile application-classifying method is proposed, capturing relationships between flow

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Ouyang, Ye, and M. Hosein Fallah. "A Dimensioning Study for UMTS Core Networks." International Journal of Business Data Communications and Networking 6, no. 2 (2010): 19–45. http://dx.doi.org/10.4018/jbdcn.2010040102.

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Current literature provides many practical tools and theoretical methods to design, plan, and dimension Global System for Mobile Communications (GSM) and Universal Mobile Telecommunications System (UMTS) radio networks but overlooks the algorithms of network planning and dimensioning for core networks of GSM, UMTS and IP Multimedia Subsystem (IMS). This paper introduces an algorithm for traffic, bandwidth and throughput dimensioning of the network entities in the UMTS core network, based on the traffic and throughput generated or absorbed in the interfaces of the network entities. A case study

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Liu, Zhen, Nathalie Japkowicz, Ruoyu Wang, and Deyu Tang. "Adaptive learning on mobile network traffic data." Connection Science 31, no. 2 (2018): 185–214. http://dx.doi.org/10.1080/09540091.2018.1512557.

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Ramadhani, Afifah Dwi, Alon Jala Tirta Segara, and Aditya Wijayanto. "Optimasi Protokol LEACH Menggunakan PSO Pada Mobile Ad-Hoc Network." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (2022): 513. http://dx.doi.org/10.30865/mib.v6i1.3455.

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Mobile ad-hoc network is a wireless network with variable topology and communication routes. In a MANET network, nodes have the role of finding and handling routes to each node in the network. There are several challenges such as bandwidth, power, and direction of movement of nodes. A routing protocol is needed to determine the route and ensure that the packets sent successfully arrive at their destination to solve this problem,. In large-scale networks, the routing structure requires redundant route information, resulting in frequent overhead. We investigated the use of the LEACH protocol as

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Adaramola, O. J., and J. R. Olasina. "Machine Learning-Driven Congestion Prediction in Mobile Ad-Hoc Networks Through Modelling Approaches." International Journal of Scientific Research in Computer Science and Engineering 13, no. 2 (2025): 30–38. https://doi.org/10.26438/ijsrcse.v13i2.666.

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Mobile Ad Hoc Networks (MANETs) operate autonomously through decentralized configurations for military as well as emergency and academic applications. The adaptable network structure and unstable nature of MANETs result in major traffic jam occurrences when network activity is at the peak. This research studies the congestion issue of MANETs by implementing network simulation with Machine Learning analytics to identify and control traffic congestion effectively. The investigation employed OPNET 14.5v to simulate office scenarios that contained five, ten and fifteen mobile nodes to study conges

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Pham, Vinh, Erlend Larsen, Øivind Kure, and Paal Engelstad. "Routing of Internal MANET Traffic over External Networks." Mobile Information Systems 5, no. 3 (2009): 291–311. http://dx.doi.org/10.1155/2009/682865.

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Many have proposed to connect Mobile Ad Hoc Networks (MANETs) to a wired backbone Internet access network. This paper demonstrates that a wired backbone network can be utilized for more than just providing access to the global Internet. Traffic between mobile nodes in the ad hoc network may also be routed via this backbone network to achieve higher throughput, and to reduce the load in the ad hoc network. This is referred to as transit routing. This paper proposes a cost metric algorithm that facilitates transit routing for some of the traffic flows between nodes in the MANET. The algorithm ai

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Yıldız, Önem, and Radosveta Ivanova Sokullu. "Deep Q-Learning based resource allocation and load balancing in a mobile edge system serving different types of user requests." Journal of Electrical Engineering 74, no. 1 (2023): 48–56. http://dx.doi.org/10.2478/jee-2023-0006.

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Abstract With the expansion of the communicative and perceptual capabilities of mobile devices in recent years, the number of complex and high computational applications has also increased rendering traditional methods of traffic management and resource allocation quite insufficient. Recently, mobile edge computing (MEC) has emerged as a new viable solution to these problems. It can provide additional computing features at the edge of the network and allow alleviation of the resource limit of mobile devices while increasing the performance for critical applications especially in terms of laten

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Liu, Rong Duo, Wei Wu, Jia Dong Du, Wei Xin, and Da Cheng Yang. "A Tree-Structured Traffic Model of Mobile POS Service Applied in Cellular Network." Advanced Materials Research 671-674 (March 2013): 3155–60. http://dx.doi.org/10.4028/www.scientific.net/amr.671-674.3155.

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The user number of mobile point of sale (POS) service is growing rapidly. The market potential is attractive to the mobile network operators, while the traffic challenges caused by heavy mobile POS service is becoming a critical issue to the operators. In order to evaluate the impact on the network and make best use of the existing network, it is important to model the traffic of mobile POS service. A tree-structured modeling method is presented in the paper, which can be used to model the traffic of mobile POS service as well as other interactive M2M services, such as remote medical services.

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TORSHIZ, MASOOD NIAZI, and ALI MOVAGHAR. "DESIGN AND PERFORMANCE EVALUATION OF A FUZZY-BASED TRAFFIC CONDITIONER FOR MOBILE AD HOC NETWORKS." Journal of Circuits, Systems and Computers 17, no. 06 (2008): 995–1014. http://dx.doi.org/10.1142/s0218126608004848.

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A mobile ad hoc network is a collection of mobile hosts forming a temporary network on the fly, without using any fixed infrastructure. Characteristics of mobile ad hoc networks such as lack of central coordination, mobility of hosts, dynamically varying network topology, and limited availability of resources make QoS provisioning very challenging in such networks. In this paper, we introduce a fuzzy QoS traffic conditioner for mobile ad hoc networks. The proposed traffic conditioner consists of fuzzy admission control (FAC), fuzzy traffic rate controller (FTRC), and fuzzy scheduler (FS). The

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Trinh, Hoang Duy, Engin Zeydan, Lorenza Giupponi, and Paolo Dini. "Detecting Mobile Traffic Anomalies Through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach." IEEE Access 7 (October 1, 2019): 152187–201. https://doi.org/10.1109/ACCESS.2019.2947742.

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Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensive framework for detecting network anomalies using mobile traffic data: collecting data from the LTE Physical Downlink Control Channel (PDCCH) of different eNodeBs, we implement deep learning algorithms in a semi-supervised way to detect potential traffic anomalies that are generated, for example, b

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Wei, Songjie, Zedong Zhang, Shasha Li, and Pengfei Jiang. "Calibrating Network Traffic with One-Dimensional Convolutional Neural Network with Autoencoder and Independent Recurrent Neural Network for Mobile Malware Detection." Security and Communication Networks 2021 (February 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/6695858.

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In response to the surging challenge in the number and types of mobile malware targeting smart devices and their sophistication in malicious behavior camouflage, we propose to compose a traffic behavior modeling method based on one-dimensional convolutional neural network with autoencoder and independent recurrent neural network (1DCAE-IndRNN) for mobile malware detection. The design solves the problem that most existing approaches for mobile malware traffic detection struggle with capturing the network traffic dynamics and the sequential characteristics of anomalies in the traffic. We reconst

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Omumbo, Nicholas J., Titus M. Muhambe, and Cyprian M. Ratemo. "Evaluation of Routing Performance using OSPF and Multi-Controller Based Network Architecture." International Journal of Computer Network and Information Security 13, no. 4 (2021): 45–61. http://dx.doi.org/10.5815/ijcnis.2021.04.05.

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Newer mobile applications are increasingly being defined using Internet Protocol, resulting in increased use of Internet Protocol and subsequent upsurge of smartphones. However, many communication service provider core networks continue to use classical routing protocols and single controller-based networks if deployed. Controller-based networks built on the foundation of software-defined networks include centralization and separation of control plane and data plane, which can address the challenges experienced with the classical routing protocols. When single controllers are used, they tend t

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Mwashita, Weston, and Marcel Ohanga Odhiambo. "Base Station Energy Efficiency Improvement for Next Generation Mobile Networks." International Journal of Electronics and Telecommunications 63, no. 2 (2017): 187–94. http://dx.doi.org/10.1515/eletel-2017-0025.

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Abstract As more and more Base Stations (BSs) are being deployed by mobile operators to meet the ever increasing data traffic, solutions have to be found to try and reduce BS energy consumption to make the BSs more energy efficient and to reduce the mobile networks’ operational expenditure (OPEX) and carbon dioxide emissions. In this paper, a BS sleeping technology deployable in heterogeneous networks (HetNets) is proposed. The proposed scheme is validated by using extensive OMNeT++/SimuLTE simulations. From the simulations, it is shown that some lightly loaded micro BSs can be put to sleep in

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K., Thamizhmaran. "Adaptive Location Update in Wireless Mobile Ad Hoc Networks." Recent Trends in Analog Design and Digital Devices 5, no. 2 (2022): 1–5. https://doi.org/10.5281/zenodo.7208928.

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<em>Some of young and beginning researchers boldly focused in the field of mobile ad hoc networks its collection of separate nodes in the particular network through wireless links. </em><em>It is also difficult to</em><em> pre-set protocol parameters correctly to fit in different environments. We have settled two self-adaptive on-demand topographical routing schemes. The local topology is updated in a timely manner according to network dynamics and traffic demands. Our route optimization scheme adjusts the routing path permitting to both topology changes and real data traffic requirements. Eac

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Shi, Jianrun, Leiyang Cui, Bo Gu, Bin Lyu, and Shimin Gong. "State Transition Graph-Based Spatial–Temporal Attention Network for Cell-Level Mobile Traffic Prediction." Sensors 23, no. 23 (2023): 9308. http://dx.doi.org/10.3390/s23239308.

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Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial–temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the underlying spatial–temporal dynamic information hidden in the historical mobile traffic data. Specifically, we first identify the semantic context information over different segments of the historical data by constructing the state transition graphs, which may reveal different patterns of random fluctuation. T

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P, Karthikala, Dr C.Arunachala Perumal, and Ms M.Ramya. "Network Efficiency Amendment Utilizing Cloud Radio Access Network In Mobile Communications." International Journal of Engineering & Technology 7, no. 4.6 (2018): 350. http://dx.doi.org/10.14419/ijet.v7i4.6.28439.

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Mobile data traffic is finding exponential growth currently in telecommunications industry. It has become important to concentrate on both spectral and energy efficiencies in utilizing cellular networks under green communication standpoint. Thus, for 5G the utmost priority is that to increase data traffic and reduce the total network energy ingesting by half. The proposed work is to design the Cloud Radio Access Network (C-RAN) with energy efficient, flexible and capacity-enhanced features by effectively bundling and establishing relation between BBU and RRU utilizing Catechistic technique. Ma

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Alekseeva, D., A. Marochkina, and A. Paramonov. "Traffic optimization applying machine learning methods." Telecom IT 9, no. 1 (2021): 1–12. http://dx.doi.org/10.31854/2307-1303-2021-9-1-1-12.

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Future networks bring higher communication requirements in latency, computations, data quality, etc. The attention to various challenges in the network field through the advances of Artificial Intelligence (AI), Machine Learning (ML) and Big Data analysis is growing. The subject of research in this paper is 4G mobile traffic collected during one year. The amount of data retrieved from devices and network management are motivating the trend toward learning-based approaches. The research method is to compare various ML methods for traffic prediction. In terms of ML, to find a solution for a regr

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Biernacki, Arkadiusz. "Throughput Prediction of 5G Network Based on Trace Similarity for Adaptive Video." Applied Sciences 14, no. 5 (2024): 1962. http://dx.doi.org/10.3390/app14051962.

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Predicting throughput is essential to reduce latency in time-critical services like video streaming, which constitutes a significant portion of mobile network traffic. The video player continuously monitors network throughput during playback and adjusts the video quality according to the network conditions. This means that the quality of the video depends on the player’s ability to predict network throughput accurately, which can be challenging in the unpredictable environment of mobile networks. To improve the prediction accuracy, we grouped the throughput trace into clusters taking into acco

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Ajib, Setyo Arifin, and Idham Habibie Muhammad. "The prediction of mobile data traffic based on the ARIMA model and disruptive formula in industry 4.0: A case study in Jakarta, Indonesia." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 2 (2020): 907–18. https://doi.org/10.12928/TELKOMNIKA.v18i2.12989.

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Disruptive technologies, which are caused by the cellular evolution including the Internet of Things (IoT), have significantly contributed data traffic to the mobile telecommunication network in the era of Industry 4.0. These technologies cause erroneous predictions prompting mobile operators to upgrade their network, which leads to revenue loss. Besides, the inaccuracy of network prediction also creates a bottleneck problem that affects the performance of the telecommunication network, especially on the mobile backhaul. We propose a new technique to predict more accurate data traffic. This re

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