scholarly journals SDMN Architecture in 5G

2020 ◽  
Vol 13 (1) ◽  
pp. 101-104
Author(s):  
Márk Kovács ◽  
Péter András Agg ◽  
Zsolt Csaba Johanyák
Keyword(s):  
Mobile Devices ◽  
Mobile Networks ◽  
Mobile Network ◽  
Design Concept ◽  
The Internet ◽  
Wired Networks ◽  
Fifth Generation ◽  
4G Lte

AbstractDue to the exponentially growing number of mobile devices connected to the Internet, the current 4G LTE-A mobile network will no longer be able to serve the nearly 5 billion mobile devices. With the advent of the fifth generation, however, the number of cybercrimes may increase. This requires building an architecture that can adequately protect against these attacks. For wired networks, the SDN-type architecture has been introduced for some time. As a result, a similar design concept has emerged, which is called Software Defined Mobile Networks (SDMN). This article describes this technology and how it helps prevents DoS, DDoS at-tacks, and IP source spoofing.

scholarly journals Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

2021 ◽  
Author(s):  
Abdelfatteh Haidine ◽  
Fatima Zahra Salmam ◽  
Abdelhak Aqqal ◽  
Aziz Dahbi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Cost Effective ◽  
Mobile Internet ◽  
Network Function ◽  
New Radio ◽  
4G Lte

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

scholarly journals Handover Parameters Optimisation Techniques in 5G Networks

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5202
Author(s):  
Wasan Kadhim Saad ◽  
Ibraheem Shayea ◽  
Bashar J. Hamza ◽  
Hafizal Mohamad ◽  
Yousef Ibrahim Daradkeh ◽  
...  
Keyword(s):  
Mobile Networks ◽  
Mobile Network ◽  
Small Cells ◽  
Fourth Generation ◽  
Acceptable Solution ◽  
Massive Growth ◽  
Fifth Generation ◽  
5G Network ◽  
Simulation Results ◽  
The Impact

The massive growth of mobile users will spread to significant numbers of small cells for the Fifth Generation (5G) mobile network, which will overlap the fourth generation (4G) network. A tremendous increase in handover (HO) scenarios and HO rates will occur. Ensuring stable and reliable connection through the mobility of user equipment (UE) will become a major problem in future mobile networks. This problem will be magnified with the use of suboptimal handover control parameter (HCP) settings, which can be configured manually or automatically. Therefore, the aim of this study is to investigate the impact of different HCP settings on the performance of 5G network. Several system scenarios are proposed and investigated based on different HCP settings and mobile speed scenarios. The different mobile speeds are expected to demonstrate the influence of many proposed system scenarios on 5G network execution. We conducted simulations utilizing MATLAB software and its related tools. Evaluation comparisons were performed in terms of handover probability (HOP), ping-pong handover probability (PPHP) and outage probability (OP). The 5G network framework has been employed to evaluate the proposed system scenarios used. The simulation results reveal that there is a trade-off in the results obtained from various systems. The use of lower HCP settings provides noticeable enhancements compared to higher HCP settings in terms of OP. Simultaneously, the use of lower HCP settings provides noticeable drawbacks compared to higher HCP settings in terms of high PPHP for all scenarios of mobile speed. The simulation results show that medium HCP settings may be the acceptable solution if one of these systems is applied. This study emphasises the application of automatic self-optimisation (ASO) functions as the best solution that considers user experience.

scholarly journals Data Traffic Based on Slicing in a 5G Smart Uplink System

Telecom IT ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 44-54
Author(s):  
A. Grebenshchikova ◽  
Elagin V.
Keyword(s):  
Mobile Networks ◽  
Data Transfer ◽  
Relay Node ◽  
Mobile Network ◽  
Video Stream ◽  
Base Station ◽  
Smart Devices ◽  
Future Research ◽  
Data Traffic ◽  
Fifth Generation

The paper considers the data traffic based on slicing in a 5g mobile network uplink system. Slicing is a promising technology for the fifth generation of networks that provides optimal quality of QOS services for each specific user or group of users. Data traffic that is processed by cellular networks increases every year. Therefore, we should consider all set of traffic from VoIP to M2M devices. For example, smart devices in the healthcare system transmit big data that is sensitive to latency, but also a video stream that requires minimal latency in certain cases. The paper focuses on the successful processing of traffic through a relay node, donor microstates, and a base station. All traffic is divided into three levels of QoS segmentation: sensitive, less sensitive, and low-sensitivity, using the AnyLogic simulation program. For fifth-generation 5G networks, achieving minimum latency and maximum data transfer speed within QoS is an important implementation condition. Therefore, in this paper, using simulation modeling, the main and possible results of each segment in the new generation of mobile networks are obtained. The use of a relay node in conjunction with micro-stations can ensure optimal station load and successful data processing. Also, the solutions outlined in this paper will allow you to identify a number of areas for future research to assess possible ways to design new mobile networks, or improve existing ones.

scholarly journals An Epidemic Model of Malware Virus with Quarantine

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Aprillya Lanz ◽  
Daija Rogers ◽  
T. L. Alford
Keyword(s):  
Mobile Devices ◽  
Mobile Phones ◽  
Epidemic Model ◽  
Treatment Effectiveness ◽  
Reproduction Number ◽  
Mobile Network ◽  
The Internet ◽  
Compartmental Modeling ◽  
Threshold Values ◽  
Desktop Computers

In March of 2018, about 500,000 desktop computers were infected with cryptocurrency mining malware in less than 24 hours. In addition to attacking desktop computers, malware also attacks laptops, tablets, mobile phones. That is, any device connected via the Internet, or a network is at risk of being attacked. In recent years, mobile phones have become extremely popular that places them as a big target of malware infections. In this study, the effectiveness of treatment for infected mobile devices is examined using compartmental modeling. Many studies have considered malware infections which also include treatment effectiveness. However, in this study we examine the treatment effectiveness of mobile devices based on the type of malware infections accrued (hostile or malicious malware). This model considers six classes of mobile devices based on their epidemiological status: susceptible, exposed, infected by hostile malware, infected by malicious malware, quarantined, and recovered. The malware reproduction number, RM, was identied to discover the threshold values for the dynamics of malware infections to become both prevalent or absent among mobile devices. Numerical simulations of the model give insights of various strategies that can be implemented to control malware epidemic in a mobile network.

Big Data in Telecommunications

2015 ◽  
pp. 67-81
Author(s):  
Yen Pei Tay ◽  
Vasaki Ponnusamy ◽  
Lam Hong Lee
Keyword(s):  
Big Data ◽  
Mobile Networks ◽  
Traffic Congestion ◽  
Big Data Analytics ◽  
Mobile Network ◽  
Consumer Electronics ◽  
Data Traffic ◽  
Network Discovery ◽  
Crowd Intelligence ◽  
4G Lte

The meteoric rise of smart devices in dominating worldwide consumer electronics market complemented with data-hungry mobile applications and widely accessible heterogeneous networks e.g. 3G, 4G LTE and Wi-Fi, have elevated Mobile Internet from a ‘nice-to-have' to a mandatory feature on every mobile computing device. This has spurred serious data traffic congestion on mobile networks as a consequence. The nature of mobile network traffic today is more like little Data Tsunami, unpredictable in terms of time and location while pounding the access networks with waves of data streams. This chapter explains how Big Data analytics can be applied to understand the Device-Network-Application (DNA) dimensions in annotating mobile connectivity routine and how Simplify, a seamless network discovery solution developed at Nextwave Technology, can be extended to leverage crowd intelligence in predicting and collaboratively shaping mobile data traffic towards achieving real-time network congestion control. The chapter also presents the Big Data architecture hosted on Google Cloud Platform powering the backbone behind Simplify in realizing its intelligent traffic steering solution.

scholarly journals An Efficient Patch Dissemination Strategy for Mobile Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Dawei Zhao ◽  
Haipeng Peng ◽  
Lixiang Li ◽  
Yixian Yang ◽  
Shudong Li
Keyword(s):  
Mobile Devices ◽  
Mobile Networks ◽  
Large Scale ◽  
Lower Cost ◽  
Mobile Network ◽  
Personal Digital Assistants ◽  
Virus Spread ◽  
Dissemination Strategy ◽  
Limited Bandwidth ◽  
Evolving Networks

Mobile phones and personal digital assistants are becoming increasingly important in our daily life since they enable us to access a large variety of ubiquitous services. Mobile networks, formed by the connection of mobile devices following some relationships among mobile users, provide good platforms for mobile virus spread. Quick and efficient security patch dissemination strategy is necessary for the update of antivirus software so that it can detect mobile virus, especially the new virus under the wireless mobile network environment with limited bandwidth which is also large scale, decentralized, dynamically evolving, and of unknown network topology. In this paper, we propose an efficient semi autonomy-oriented computing (SAOC) based patch dissemination strategy to restrain the mobile virus. In this strategy, some entities are deployed in a mobile network to search for mobile devices according to some specific rules and with the assistance of a center. Through experiments involving both real-world networks and dynamically evolving networks, we demonstrate that the proposed strategy can effectively send security patches to as many mobile devices as possible at a considerable speed and lower cost in the mobile network. It is a reasonable, effective, and secure method to reduce the damages mobile viruses may cause.

scholarly journals Artificial intelligence techniques over the fifth generation mobile networks: a review

2021 ◽  
Vol 24 (1) ◽  
pp. 317
Author(s):  
Ashwaq N. Hassan ◽  
Sarab Al-Chlaihawi ◽  
Ahlam R. Khekan
Keyword(s):  
Artificial Intelligence ◽  
Mobile Networks ◽  
Human Error ◽  
Smart Cities ◽  
Mobile Network ◽  
Fourth Generation ◽  
Future Research ◽  
Artificial Intelligence Techniques ◽  
Fifth Generation ◽  
Sixth Generation

<span>A well Fifth generation (5G) mobile networks have been a common phrase in recent years. We have all heard this phrase and know its importance. By 2025, the number of devices based on the fifth generation of mobile networks will reach about 100 billion devices. By then, about 2.5 billion users are expected to consume more than a gigabyte of streamed data per month. 5G will play important roles in a variety of new areas, from smart homes and cars to smart cities, virtual reality and mobile augmented reality, and 4K video streaming. Bandwidth much higher than the fourth generation, more reliability and less latency are some of the features that distinguish this generation of mobile networks from previous generations.  Clearly, at first glance, these features may seem very impressive and useful to a mobile network, but these features will pose serious challenges for operators and communications companies. All of these features will lead to considerable complexity. Managing this network, preventing errors, and minimizing latency are some of the challenges that the 5th generation of mobile networks will bring. Therefore, the use of artificial intelligence and machine learning is a good way to solve these challenges. in other say, in such a situation, proper management of the 5G network must be done using powerful tools such as artificial intelligence. Various researches in this field are currently being carried out. Research that enables automated management and servicing and reduces human error as much as possible. In this paper, we will review the artificial intelligence techniques used in communications networks. Creating a robust and efficient communications network using artificial intelligence techniques is a great incentive for future research.</span><span> The importance of this issue is such that the sixth generation (6G) of cellular communications; There is a lot of emphasis on the use of artificial intelligence.</span>

scholarly journals Mobility Management Techniques in 5G Wireless Networks

2019 ◽  
Vol 8 (10) ◽  
pp. 4543-4547
Keyword(s):  
Wireless Networks ◽  
Mobile Devices ◽  
Wireless Network ◽  
Mobile Networks ◽  
Mobility Management ◽  
The Internet ◽  
Distributed Mobility Management ◽  
Detailed Survey ◽  
Management Techniques ◽  
Mobile And Wireless Networks

Current innovation in the field of Mobile and Wireless network will increase the use of mobile devices which procreated in an outburst of traffic passing through the internet. Due to the explosion of traffic mobility management has become a challenge in future mobile and wireless networks. To deal with such an explosion, mobile networks are becoming flatter as compared to previous hierarchical mobile networks. This paper presents a detailed survey of solutions for currently mobility management such as Centralized mobility management techniques for mobile and wireless networks, described the limitation of Centralized Mobility Management which is hierarchical and centralized in nature and discussed an approach which removes the limitation of Centralized mobility management called as Distributed mobility management. This paper also discussed two different approaches of Distributed mobility management such as Client based Distributed mobility management and Network based Distributed mobility management.

A Power Control Strategy for IoT Sensors Developed for 5G Networks

2020 ◽  
Vol 1 (1) ◽  
pp. 22-41
Author(s):  
Weston Mwashita ◽  
Marcel Ohanga Odhiambo
Keyword(s):  
Power Control ◽  
Mobile Networks ◽  
Control Mechanism ◽  
Research Work ◽  
Mobile Network ◽  
Base Station ◽  
Open Loop ◽  
5G Networks ◽  
Fifth Generation

This research work presents a power control mechanism developed for ProSe-enabled sensors so that the sensors can be smoothly integrated into the fifth generation (5G) of mobile networks. It is strongly anticipated that 5G networks will provide an enabling environment for the 21st century innovations like the internet of things (IoT). Sensors are pivotal in IoT. The proposed power control mechanism involves an open loop power control (OLPC) mechanism that a ProSe-enabled sensor has to use to establish communication with a base station (BS) and a closed loop power control (CLPC) the BS then has use to establish transmit power levels for devices to be involved in a device to device (D2D) communication depending on the prevailing channel conditions. The results obtained demonstrate that the developed scheme does not adversely affect the quality of service (QoS) of a 5G mobile network.

Mobile Network Forensics

2018 ◽  
pp. 287-308
Keyword(s):  
Mobile Networks ◽  
Network Architecture ◽  
Mobile Network ◽  
Next Generation ◽  
Network Forensics ◽  
Implementation Challenges ◽  
Mobile Forensics ◽  
Fifth Generation ◽  
Highly Effective ◽  
Effective Discipline

Mobile networks are evolving towards the fifth generation, with radical changes in the delivery of user services. To take advantage of the new investigative opportunities, mobile network forensics need to address several technical, legal, and implementation challenges. The future mobile forensics need to adapt to the novelties in the network architecture, establish capabilities for investigation of transnational crimes, and combat clever anti-forensics methods. At the same time, legislation needs to create an investigative environment where strong privacy safeguards exist for all subjects of investigation. These are rather complex challenges, which, if addressed adequately, will ensure investigative continuity and keep the reputation of mobile network forensics as a highly effective discipline. In this context, this chapter elaborates the next-generation of mobile network forensics.

Sign in / Sign up

Export Citation Format

Share Document