Mobility Management In Networks Research Articles

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

A Review on Deploying Blockchain Technology for Network Mobility Management

In wireless mobile networks, Mobility Management (MM) is a critical aspect that involves different tasks such as handover management, location management, offloading, resource allocation, and others. Blockchain platform comprising of an array of linked blocks innately supports the incorruptibility, sustaining the authenticity, and sustain pseudonymous privacy. Traditional MM is centralized, in contrast to blockchain-grounded MM that is partially or fully decentralized in its approach. As the inaugural researchers to review on blockchain-grounded MM, we are proud to cluster the blockchain-grounded MM concept under 7 clusters and closely study each framework with respect to different aspects such as blockchain specific parameters, MM specific parameters, network parameters, and more. We gathered a sample of 92 source references by curating the articles for eligibility criteria examined from electronic archives utilizing a comprehensive and enduring way. Grounded on this inspection, blockchain-grounded MM reinforces the overall security, trust, and privacy of location management and resource allocation, blockchain-grounded secure handover, facilitating registration and authentication, automatic MM utilizing smart contracts, reducing vulnerabilities, repairing low quality data, coordination, fair scheduling, in offloading, and blockchain-grounded resource allocation. Close studying proves that out of blockchain grounded MM frameworks, 37.5% utilize blockchain grounded secure handover, 92.5% utilize simple blockchain architecture, 32.5% utilize PoW consensus, 100% distributed MM, 97.5% host grounded MM, 95% packet reordering tolerant MM, 52.5% QoS aware MM, and 12.5% proposed for 5G networks. Finally, we present the openings and obstructions of the concept of blockchain-grounded MM and then catering surmounting advices.

Highly secured authentication and fast handover scheme for mobility management in 5G vehicular networks

The Fifth Generation (5 G) networks exhibit high flexibility and diversity in their design and deployment strategies. Transitions between base stations (BSs), heterogeneous networks (HetNets) and other cellular networks provide significant vulnerabilities and expose users to substantial risks associated with cybersecurity attacks. This article evaluates current handover authentication methods in the context of 5 G networks while proposing a set of security criteria for handover authentication. This study presents a novel authentication technique called SHK (Secure Handover Key) that utilizes SDNs (Software Defined networks). The proposed scheme integrates recycled lightweight dynamic key cryptography and combines security features such as perfect forward secrecy and robustness to leakages. The significance of the proposed approach is assessed in terms of computations, communications, signals, and energy costs on 5 G mobility applications and Vehicular Communication Networks (VCNs). The scheme employed in this study demonstrates enhanced security measures and improved changeover performance compared to conventional schemes.

Enhancing Network Security through Advanced Authentication and Key Management Mechanisms

This paper introduces the SEAKS-PKMv2 protocol, a robust security mechanism aimed at addressing vulnerabilities within the PKMv2 framework, particularly focusing on mutual authentication, key management, and encryption in mobile WiMAX networks. By integrating RSA-based and EAP-based authentication methods, SEAKS-PKMv2 establishes a secure environment that mitigates risks such as replay, man-in-the-middle and interleaving attacks. The protocol adopts a distributed authentication and localized key management approach, facilitating efficient and secure network access and data transmission. Through simulation, we evaluate the SEAKS-PKMv2 protocol’s performance in terms of packet delivery ratio, overhead, processing time, and resilience against rogue relay station attacks. The findings demonstrate significant improvements in network security and efficiency, confirming the effectiveness of SEAKS-PKMv2 in enhancing the integrity and confidentiality of communications in distributed network settings.

Satellite-Terrestrial Integrated 6G: An Ultra-Dense LEO Networking Management Architecture

With the rapid development of low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated networking has become a promising paradigm to provide wide coverage, high capacity and flexible services for the sixth generation (6G) mobile communication networks. However, the natural heterogeneity, high dynamics, and large scale of ultradense LEO satellite networks pose new challenges to network control and management. To this end, in this article, we propose a medium earth orbit (MEO), low earth orbit (LEO) and satellite Earth stations (SESs) integrated multi-layered management architecture for the ultra-dense LEO satellite network, where global controllers and local controllers are introduced to reduce the network management complexity. With the aid of this hybrid multi-layered management framework, we can efficiently implement the network status control, mobility management, resource management, and service management in the satelliteterrestrial integrated network. To obtain proper global and local controllers with high management efficiency in the hybrid multilayered network management architecture, we further propose a novel and efficient grouping and clustering method for the ultradense LEO satellite network, where each group manager MEO satellite and cluster head (CH) LEO satellites are respectively considered as the global and local controllers of each LEO satellite group. Numerical simulations are carried out to evaluate the superiority and effectiveness of the proposed hybrid multilayered ultra-dense LEO networking management architecture.

A Blockchain-Based Approach for USIM Management in Mobile Networks

A Blockchain-Based Approach for USIM Management in Mobile Networks

Mobility Management in TSCH-based Industrial Wireless Networks

Mobility Management in TSCH-based Industrial Wireless Networks

Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core

Federated Learning-Empowered Mobile Network Management for 5G and Beyond Networks: From Access to Core

A Vulnerability Assessment of Open-Source Implementations of Fifth-Generation Core Network Functions

The paper presents an experimental security assessment within two widely used open-source 5G projects, namely Open5GS and OAI (Open-Air Interface). The examination concentrates on two network functions (NFs) that are externally exposed within the core network architecture, i.e., the Access and Mobility Management Function (AMF) and the Network Repository Function/Network Exposure Function (NRF/NEF) of the Service-Based Architecture (SBA). Focusing on the Service-Based Interface (SBI) of these exposed NFs, the analysis not only identifies potential security gaps but also underscores the crucial role of Mobile Network Operators (MNOs) in implementing robust security measures. Furthermore, given the shift towards Network Function Virtualization (NFV), this paper emphasizes the importance of secure development practices to enhance the integrity of 5G network functions. In essence, this paper underscores the significance of scrutinizing security vulnerabilities in open-source 5G projects, particularly within the core network’s SBI and externally exposed NFs. The research outcomes provide valuable insights for MNOs, enabling them to establish effective security measures and promote secure development practices to safeguard the integrity of 5G network functions. Additionally, the empirical investigation aids in identifying potential vulnerabilities in open-source 5G projects, paving the way for future enhancements and standard releases.

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

AbstractMillimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.

Enhancing heterogeneous mobile network management based on a well-founded reference ontology

The fulfillment of the Always Best Connected & Served concept in future mobile wireless networks depends on their ability to maintain a seamless association between the user’s equipment and the network. As a result, many researchers and developers have proposed several decision-support mechanisms to cope with this challenge. One of the promising mechanisms to aid mobile wireless networks in achieving ubiquitous coverage with a seamless connection is semantic reasoning. However, once it relies on ontologies, proper representation of Link and Connection – entities that bind the communication nodes – is paramount. Unfortunately, although several network-related ontologies present these concepts, they are unclear on which entities they bind, nor do they present them simultaneously. Also, the same ambiguity can be perceived in vocabulary recommendations of several telecommunication standard bodies. Our main contribution is the ontological analysis that clarifies the definition of Link and Connection in telecommunication domain. Besides, we analyze their dependencies while refining other concepts such as Medium, Server, and Neighbor. Thus, we provide the foundations for a new network-related ontology to support mobility management in wireless networks.

Selective Layered Blockchain Framework for Privacy-preserving Data Management in Low-latency Mobile Networks

<p>With the gradual development of Fourth Industrial Revolution technologies, such as artificial intelligence, the Internet of Things, and big data, and the considerable amount of data in mobile networks, low-latency communication and security management are becoming crucial. Blockchain is a data-distributed processing technology that tracks data records to support secure electronic money transactions and data security management in a peer-to-peer environment without the need of a central trusted authority. The data uploaded to the blockchain-shared ledger are immutable, making tracking integrity preservation facile. However, blockchain technology is limited because it is challenging to utilize in the industry owing to its inability to correct data, even when inaccurate data are uploaded. Accordingly, research on blockchain mechanisms that consider privacy-preserving data management is required to commercialize blockchain technology. Previously, off-chain, blacklist, and hard-fork methods have been proposed; however, their application is challenging or impractical. Therefore, to protect privacy, we propose a layered blockchain mechanism that can correct data by adding a buffer blockchain. We evaluated the latency, security, and space complexity of layered blockchains. The security and security-to-latency ratio for data management of the selective layered blockchain is 2.2 and 11.3 times higher than the conventional blockchains, respectively. The proposed selective layered blockchain is expected to promote the commercialization of blockchain technologies in various industries by protecting user privacy.</p> <p> </p>

Improvement of the Location Update Delay and Packet Transmission of MH Movement on the Mobile IP Network Mobility Management Server

Improvement of the Location Update Delay and Packet Transmission of MH Movement on the Mobile IP Network Mobility Management Server

Forecasting Framework for Mobile Networks Based on Automatic Feature Selection

Traditionally, mobile network management has been based on reactive systems, where corrective actions are taken when a failure or a suboptimal network performance is detected. However, the requirements of low latency and high throughput associated with 5G new services, make these reactive systems insufficient to manage the new needs of users. Thus, the focus of mobile network management has changed to a proactive approach, where preventive actions are taken to avoid failures and suboptimal network performance. Moreover, in 5G, management algorithms are expected to make use of large amounts of information sources. The use of this large amount of information and the need to use historical data to generate forecasting models, may cause a loss of accuracy. To address these challenges, a forecasting framework is proposed in this work. The framework is based on the use of automatic feature selection techniques in both spatial and temporal dimensions as a pre-prediction stage. In this way, the proposed framework can improve the accuracy of other prediction schemes when using large amounts of data as input. The proposed scheme has been tested using different forecasting techniques. In addition, the impact of the proposed framework on predictions of different future lags has been analyzed.

Deep Mobile Path Prediction With Shift-and-Join and Carry-Ahead

Importance of user mobility has rapidly increased in 5G due to reduced cell sizes, management of Multi-access Edge Computing (MEC), and ultra-low latency services. Reactive nature of existing management systems is a bottleneck, and it can be solved by building proactive systems that exploit temporal characteristics of time-series mobility data to predict long-term user movement (i.e.path). However, user mobile path prediction with useable accuracy is a challenging task, particularly for lengthy target trajectories. This paper adopts general approaches to propose two models for predicting mobile path with high accuracy. Step Forward Iteration (SFI) model is based on recursive approach, whereas Encoder-Decoder (ED) model follows multi-output approach, and both the models use Long-Short Term Memory (LSTM) as the learning unit. Training and testing of these models is done on mobility datasets from the wireless network of Pangyo ICT Research Center, Korea and one of the Korean mobile operators. The experiment results show viability of the proposed models for leveraging mobile network management, as they outperform state-of-the-art GRU with attention (GRU-ATTN) and Transformer Network (TN) models. The highest prediction accuracies achieved for 3, 5, and 7 steps of target sequences (i.e.predicted mobile path) in the campus dataset are 96%, 90%, and 87%, respectively.

An Enhanced Virtual Cord Protocol Based Multi-Casting Strategy for the Effective and Efficient Management of Mobile Ad Hoc Networks

To solve problems with limited resources such as power, storage, bandwidth, and connectivity, efficient and effective data management solutions are needed. It is believed that the most successful algorithms for circumventing these constraints are those that self-organise and collaborate. To make the best use of available bandwidth, mobile ad hoc networks (MANETs) employ the strategy of multi-casting. The communication cost of any network can be significantly reduced by multi-casting, and the network can save resources by transmitting only one set of data to numerous receivers at a time. In this study, we implemented multi-casting in the virtual cord protocol (VCP), which uses virtual coordinates (VC) to improve effective routing and control wireless data transmission. We have improved the classic VCP protocol by making it so that intermediate nodes can also forward or re-transmit the dataset to interested nodes. This improves data transmission from the sender to multiple receivers. Simulation results proved efficacy of our proposed enhanced virtual cord protocol-based multi-casting strategy over traditional VCP protocol and helped in reduction of number of MAC transmissions, minimization of end-to-end delay, and maximization of packet delivery ratio.

Temporal dynamics clustering for analyzing cell behavior in mobile networks

In recent years, management and resource allocation of mobile networks have become crucial because of a dramatic growth of mobile traffic. For Internet service providers, resource allocation is a challenging problem due to the inhomogeneous distributed nature of user demand, which increases the difficulty of configuring base stations in complicated urban environments. To manage mobile networks in an efficient way, understanding the typical usage behavior is very valuable for network operators. This paper aims at providing tools to analyze the behavior of mobile network cells in an urban environment. To achieve this goal, we propose a new time series clustering algorithm denoted as temporal dynamics clustering. Unlike conventional clustering approaches, this algorithm generates a new representation of time series by summarizing the distribution of the sequence of differences; in this way, temporal dynamics clustering creates clusters based on the variability of time series. We apply the proposed algorithm on a real-world mobile network dataset, showing its superior performance and much faster running time with respect to conventional methods such as hierarchical clustering and K-medoids. We also train neural networks on the clusters generated by temporal dynamics clustering, and the results show that the forecasting difficulty of time series is closely related to its temporal dynamics. Using this approach, we have analyzed the cell behavior with two weeks observation collected in Turin, and obtained interpretable results, in that the behavior of each cell is strongly related to the characteristics of the area and the related human activity. We also study the cell behavior in different time slots by considering time-dependent characteristic of human activity. Through experiments, we show the effectiveness of the proposed algorithm at providing a deeper understanding of traffic usage patterns in intricate urban environments.

Pattern Based Mobility Management in 5G Networks With a Game Theoretic-Jump Markov Linear System Approach

Pattern Based Mobility Management in 5G Networks With a Game Theoretic-Jump Markov Linear System Approach

Real-time load balancing and dynamic profile management in mobile data networks

As mobile data grows rapidly, carriers must intelligently manage network traffic. Due to economic realities and the physical limitations of the available spectrum, operators cannot add more network capacity. To satisfy investors, operators must maximise their service returns. The more services a subscriber receives, which costs more, the higher their expectations of acceptable network availability and quality. So it becomes very challenging for operators to provide QoS to their high-value subscribers and manage network traffic dynamically without impacting the user experience of their potential and key users. In this paper, all literature surveys are carried out on different schemes and solutions to manage mobile data networks' profiles. This paper aims to identify an effective way of managing real-time data traffic by adjusting QoS/profile parameters dynamically so that high-value subscribers get better QoS during congestion than regular users.