Computer Communications最新文献

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GNNetSlice: A GNN-based performance model to support network slicing in B5G networks

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-11 DOI: 10.1016/j.comcom.2025.108044

Miquel Farreras , Jordi Paillissé , Lluís Fàbrega , Pere Vilà

Network slicing is gaining traction in Fifth Generation (5G) deployments and Beyond 5G (B5G) designs. In a nutshell, network slicing virtualizes a single physical network into multiple virtual networks or slices, so that each slice provides a desired network performance to the set of traffic flows (source–destination pairs) mapped to it. The network performance, defined by specific Quality of Service (QoS) parameters (latency, jitter and losses), is tailored to different use cases, such as manufacturing, automotive or smart cities. A network controller determines whether a new slice request can be safely granted without degrading the performance of existing slices, and therefore fast and accurate models are needed to efficiently allocate network resources to slices. Although there is a large body of work of network slicing modeling and resource allocation in the Radio Access Network (RAN), there are few works that deal with the implementation and modeling of network slicing in the core and transport network.

In this paper, we present GNNetSlice, a model that predicts the performance of a given configuration of network slices and traffic requirements in the core and transport network. The model is built leveraging Graph Neural Networks (GNNs), a kind of Neural Network specifically designed to deal with data structured as graphs. We have chosen a data-driven approach instead of classical modeling techniques, such as Queuing Theory or packet-level simulations due to their balance between prediction speed and accuracy. We detail the structure of GNNetSlice, the dataset used for training, and show how our model can accurately predict the delay, jitter and losses of a wide range of scenarios, achieving a Symmetric Mean Average Percentage Error (SMAPE) of 5.22%, 1.95% and 2.04%, respectively.

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引用次数: 0

AI-based malware detection in IoT networks within smart cities: A survey

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-10 DOI: 10.1016/j.comcom.2025.108055

Mustafa J.M. Alhamdi , Jose Manuel Lopez-Guede , Jafar AlQaryouti , Javad Rahebi , Ekaitz Zulueta , Unai Fernandez-Gamiz

The exponential expansion of Internet of Things (IoT) applications in smart cities has significantly pushed smart city development forward. Intelligent applications have the potential to enhance systems' efficiency, service quality, and overall performance. Smart cities, intelligent transportation networks, and other influential infrastructure are the main targets of cyberattacks. These attacks have the potential to undercut the security of important government, commercial, and personal information, placing privacy and confidentiality at risk. Multiple scientific studies indicate that Smart City cyberattacks can result in millions of euros in financial losses due to data compromise and loss. The importance of anomaly detection rests in its ability to identify and analyze illegitimacy within IoT data. Unprotected, infected, or suspicious devices may be unsafe for intrusion attacks, which have the potential to enter several machines within a network. This interferes with the network's provision of customer service in terms of privacy and safety. The objective of this study is to assess procedures for detecting malware in the IoT using artificial intelligence (AI) approaches. To identify and prevent threats and malicious programs, current methodologies use AI algorithms such as support vector machines, decision trees, and deep neural networks. We explore existing studies that propose several methods to address malware in IoT using AI approaches. Finally, the survey highlights current issues in this context, including the accuracy of detection and the cost of security concerns in terms of detection performance and energy consumption.

{"title":"AI-based malware detection in IoT networks within smart cities: A survey","authors":"Mustafa J.M. Alhamdi , Jose Manuel Lopez-Guede , Jafar AlQaryouti , Javad Rahebi , Ekaitz Zulueta , Unai Fernandez-Gamiz","doi":"10.1016/j.comcom.2025.108055","DOIUrl":"10.1016/j.comcom.2025.108055","url":null,"abstract":"<div><div>The exponential expansion of Internet of Things (IoT) applications in smart cities has significantly pushed smart city development forward. Intelligent applications have the potential to enhance systems' efficiency, service quality, and overall performance. Smart cities, intelligent transportation networks, and other influential infrastructure are the main targets of cyberattacks. These attacks have the potential to undercut the security of important government, commercial, and personal information, placing privacy and confidentiality at risk. Multiple scientific studies indicate that Smart City cyberattacks can result in millions of euros in financial losses due to data compromise and loss. The importance of anomaly detection rests in its ability to identify and analyze illegitimacy within IoT data. Unprotected, infected, or suspicious devices may be unsafe for intrusion attacks, which have the potential to enter several machines within a network. This interferes with the network's provision of customer service in terms of privacy and safety. The objective of this study is to assess procedures for detecting malware in the IoT using artificial intelligence (AI) approaches. To identify and prevent threats and malicious programs, current methodologies use AI algorithms such as support vector machines, decision trees, and deep neural networks. We explore existing studies that propose several methods to address malware in IoT using AI approaches. Finally, the survey highlights current issues in this context, including the accuracy of detection and the cost of security concerns in terms of detection performance and energy consumption.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"233 ","pages":"Article 108055"},"PeriodicalIF":4.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143132257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage federated learning method for personalization via selective collaboration

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-10 DOI: 10.1016/j.comcom.2025.108053

Jiuyun Xu , Liang Zhou , Yingzhi Zhao , Xiaowen Li , Kongshang Zhu , Xiangrui Xu , Qiang Duan , RuRu Zhang

As an emerging distributed learning method, Federated learning has received much attention recently. Traditional federated learning aims to train a global model on a decentralized dataset, but in the case of uneven data distribution, a single global model may not be well adapted to each client, and even the local training performance of some clients may be superior to the global model. Under this background, clustering resemblance clients into the same group is a common approach. However, there is still some heterogeneity of clients within the same group, and general clustering methods usually assume that clients belong to a specific class only, but in real-world scenarios, it is difficult to accurately categorize clients into one class due to the complexity of data distribution. To solve these problems, we propose a two-stage federated learning method for personalization via selective collaboration (FedSC). Different from previous clustering methods, we focus on how to independently exclude other clients with significant distributional differences for each client and break the restriction that clients can only belong to one category. We tend to select collaborators for each client who are more conducive to achieving local mission goals and build a collaborative group for them independently, and every client engages in a federated learning process only with group members to avoid negative knowledge transfer. Furthermore, FedSC performs finer-grained processing within each group, using an adaptive hierarchical fusion strategy of group and local models instead of the traditional approach’s scheme of directly overriding local models. Extensive experiments show that our proposed method considerably increases model performance under different heterogeneity scenarios.

{"title":"A two-stage federated learning method for personalization via selective collaboration","authors":"Jiuyun Xu , Liang Zhou , Yingzhi Zhao , Xiaowen Li , Kongshang Zhu , Xiangrui Xu , Qiang Duan , RuRu Zhang","doi":"10.1016/j.comcom.2025.108053","DOIUrl":"10.1016/j.comcom.2025.108053","url":null,"abstract":"<div><div>As an emerging distributed learning method, Federated learning has received much attention recently. Traditional federated learning aims to train a global model on a decentralized dataset, but in the case of uneven data distribution, a single global model may not be well adapted to each client, and even the local training performance of some clients may be superior to the global model. Under this background, clustering resemblance clients into the same group is a common approach. However, there is still some heterogeneity of clients within the same group, and general clustering methods usually assume that clients belong to a specific class only, but in real-world scenarios, it is difficult to accurately categorize clients into one class due to the complexity of data distribution. To solve these problems, we propose a two-stage <strong>fed</strong>erated learning method for personalization via <strong>s</strong>elective <strong>c</strong>ollaboration (FedSC). Different from previous clustering methods, we focus on how to independently exclude other clients with significant distributional differences for each client and break the restriction that clients can only belong to one category. We tend to select collaborators for each client who are more conducive to achieving local mission goals and build a collaborative group for them independently, and every client engages in a federated learning process only with group members to avoid negative knowledge transfer. Furthermore, FedSC performs finer-grained processing within each group, using an adaptive hierarchical fusion strategy of group and local models instead of the traditional approach’s scheme of directly overriding local models. Extensive experiments show that our proposed method considerably increases model performance under different heterogeneity scenarios.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"232 ","pages":"Article 108053"},"PeriodicalIF":4.5,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource allocation and UAV deployment for a UAV-assisted URLLC system

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-08 DOI: 10.1016/j.comcom.2025.108049

Xinyue Gu, Hong Jiang, Hao Yang

The unmanned aerial vehicle (UAV)-assisted transmission in ultra-reliable low-latency communication (URLLC) can achieve precise control in environments where communication infrastructures are unavailable, with enormous benefits in military and commercial applications. This paper investigates a three-hop decode-and-forward UAV-assisted system to guarantee the stringent quality-and-service requirements in long-distance URLLC. First, the block error rate (BLER) is derived for air-to-ground and air-to-air channels. Then, the transmit power, blocklength, and UAV deployment in three-dimensional space are optimized together to jointly minimize the overall BLER and UAV communication energy consumption. The formulated non-convex problem is divided into subproblems and an iterative algorithm is proposed to tackle it by utilizing the block coordinate descent. Different search techniques and the block successive convex approximation approach are used to conquer the subproblems. Finally, simulations are conducted to demonstrate the system performance and the effectiveness of the proposed algorithm.

引用次数: 0

Graph convolutional networks and deep reinforcement learning for intelligent edge routing in IoT environment

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-08 DOI: 10.1016/j.comcom.2025.108050

Zhi Wang , Bo Yi , Saru Kumari , Chien Ming Chen , Mohammed J.F. Alenazi

The rapid growth of the Internet of Things (IoT) has increased the demand for Quality of Service (QoS) in various applications. Intelligent routing algorithms have emerged to meet these high QoS requirements. However, existing algorithms face challenges such as long training time, limited generalization capabilities, and difficulties in handling high-dimensional continuous action spaces, which hinder their ability to achieve optimal routing solutions. To address these challenges, this paper proposes a novel intelligent edge routing optimization (RO) algorithm that integrates node classification (NC) using a graph convolutional network (GCN) with path selection (PS) based on deep reinforcement learning (DRL). This approach aims to intelligently select optimal paths while meeting high QoS requirements in complex, dynamically changing IoT Edge Network Environments (IENEs). The NC module reduces the computational complexity and enhances the generalization capability of the RO algorithm by transforming network topology and link state information into node features, effectively filtering out low-performing nodes. To cope with high-dimensional continuous action spaces and meet QoS requirements, the PS module utilizes the refined network topology and state information from NC to determine optimal routing paths. Simulation results show that the proposed algorithm outperforms state-of-the-art methods in key performance metrics such as average network delay, packet loss rate, and throughput. In addition, it shows significant improvements in convergence speed and generalization ability.

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引用次数: 0

DBVA: Double-layered blockchain architecture for enhanced security in VANET vehicular authentication

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-07 DOI: 10.1016/j.comcom.2025.108048

Samuel Akwasi Frimpong , Mu Han , Usman Ahmad , Otu Larbi-Siaw , Joseph Kwame Adjei

Vehicular ad-hoc networks (VANET) are crucial for improving road safety and traffic management in Intelligent Transportation Systems (ITS). However, these networks face significant security and privacy challenges due to their dynamic and decentralized nature. Traditional authentication methods, such as Public Key Infrastructure (PKI) and centralized systems, struggle with scalability, single points of failure, and privacy issues. To address these issues, this paper introduces DBVA, a Double-Layered Blockchain Architecture that integrates private and consortium blockchains to create a robust and scalable authentication framework for VANET. The DBVA framework segregates public transactions, such as traffic data, from private transactions, such as identity and location information, into separate blockchain layers, preserving privacy and enhancing security. Additionally, DBVA introduces strict access control smart contracts for the decentralized revocation of unauthorized vehicle privileges, minimizing communication risks and enhancing system resilience. A dynamic pseudonym identity generation mechanism with periodic updates further strengthens privacy by segregating real and pseudonymous identities into separate blockchain layers. Comprehensive performance evaluations reveal that DBVA significantly enhances computational efficiency, reducing the computational cost to 18.34 ms, lowering communication overhead to 992 bits per message, and minimizing storage requirements to just 50 units, making it competitive among contemporary schemes. Extensive security analysis and formal proof confirm that DBVA effectively meets all essential privacy and security requirements, making it a robust, reliable, and scalable solution for enhancing the security, privacy, and resilience of VANET.

{"title":"DBVA: Double-layered blockchain architecture for enhanced security in VANET vehicular authentication","authors":"Samuel Akwasi Frimpong , Mu Han , Usman Ahmad , Otu Larbi-Siaw , Joseph Kwame Adjei","doi":"10.1016/j.comcom.2025.108048","DOIUrl":"10.1016/j.comcom.2025.108048","url":null,"abstract":"<div><div>Vehicular ad-hoc networks (VANET) are crucial for improving road safety and traffic management in Intelligent Transportation Systems (ITS). However, these networks face significant security and privacy challenges due to their dynamic and decentralized nature. Traditional authentication methods, such as Public Key Infrastructure (PKI) and centralized systems, struggle with scalability, single points of failure, and privacy issues. To address these issues, this paper introduces DBVA, a Double-Layered Blockchain Architecture that integrates private and consortium blockchains to create a robust and scalable authentication framework for VANET. The DBVA framework segregates public transactions, such as traffic data, from private transactions, such as identity and location information, into separate blockchain layers, preserving privacy and enhancing security. Additionally, DBVA introduces strict access control smart contracts for the decentralized revocation of unauthorized vehicle privileges, minimizing communication risks and enhancing system resilience. A dynamic pseudonym identity generation mechanism with periodic updates further strengthens privacy by segregating real and pseudonymous identities into separate blockchain layers. Comprehensive performance evaluations reveal that DBVA significantly enhances computational efficiency, reducing the computational cost to 18.34 ms, lowering communication overhead to 992 bits per message, and minimizing storage requirements to just 50 units, making it competitive among contemporary schemes. Extensive security analysis and formal proof confirm that DBVA effectively meets all essential privacy and security requirements, making it a robust, reliable, and scalable solution for enhancing the security, privacy, and resilience of VANET.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"232 ","pages":"Article 108048"},"PeriodicalIF":4.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Next-generation cloudlet federation for Internet of Things in healthcare: Enhancing response time and energy efficiency

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-07 DOI: 10.1016/j.comcom.2025.108046

Rahima Tanveer , Muhammad Ziad Nayyer , Muhammad Hasan Jamal , Imran Raza , Claude Fachkha

The integration of the Internet of Things (IoT) with edge computing has revolutionized various sectors, including healthcare, by enabling timely patient monitoring and service availability. These services encompass emergency responses, regular treatment, and physician assistance. Healthcare systems leverage mobile edge computing, fog computing, and cloudlet computing to offload data and computations and minimize delays and energy consumption. However, edge computing solutions face limitations such as restricted area coverage, diverse service requirements, and insufficient computational power, making them unsuitable for large-scale data processing and computation. To address the growing need for seamless patient information sharing among doctors and specialists across different hospitals, a resource-sharing solution, such as the cloudlet federation, is essential. Nonetheless, integrating IoT systems with cloudlet federations in healthcare presents challenges, primarily because of the necessity for mobile nodes such as ambulances, in addition to the fixed-edge nodes and brokers typical of traditional cloudlet federations. This study proposes a modified cloudlet federation model designed to overcome these challenges by effectively managing data from heterogeneous nodes. Our approach enhanced response times, reduced energy consumption, and ensured the availability of comprehensive patient information. The experimental results demonstrate a 49% improvement in response time and a 51% reduction in energy consumption compared with existing cloud-based solutions.

{"title":"Next-generation cloudlet federation for Internet of Things in healthcare: Enhancing response time and energy efficiency","authors":"Rahima Tanveer , Muhammad Ziad Nayyer , Muhammad Hasan Jamal , Imran Raza , Claude Fachkha","doi":"10.1016/j.comcom.2025.108046","DOIUrl":"10.1016/j.comcom.2025.108046","url":null,"abstract":"<div><div>The integration of the Internet of Things (IoT) with edge computing has revolutionized various sectors, including healthcare, by enabling timely patient monitoring and service availability. These services encompass emergency responses, regular treatment, and physician assistance. Healthcare systems leverage mobile edge computing, fog computing, and cloudlet computing to offload data and computations and minimize delays and energy consumption. However, edge computing solutions face limitations such as restricted area coverage, diverse service requirements, and insufficient computational power, making them unsuitable for large-scale data processing and computation. To address the growing need for seamless patient information sharing among doctors and specialists across different hospitals, a resource-sharing solution, such as the cloudlet federation, is essential. Nonetheless, integrating IoT systems with cloudlet federations in healthcare presents challenges, primarily because of the necessity for mobile nodes such as ambulances, in addition to the fixed-edge nodes and brokers typical of traditional cloudlet federations. This study proposes a modified cloudlet federation model designed to overcome these challenges by effectively managing data from heterogeneous nodes. Our approach enhanced response times, reduced energy consumption, and ensured the availability of comprehensive patient information. The experimental results demonstrate a 49% improvement in response time and a 51% reduction in energy consumption compared with existing cloud-based solutions.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"232 ","pages":"Article 108046"},"PeriodicalIF":4.5,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DCDIM: Diversified influence maximization on dynamic social networks

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-06 DOI: 10.1016/j.comcom.2025.108045

Sunil Kumar Meena , Shashank Sheshar Singh , Kuldeep Singh

The problem of influence maximization (IM) identifies the top

k

nodes that can maximize the expected influence in social networks. IM has many applications, such as viral marketing, business strategy, and profit maximization. However, most of the existing studies focus on maximizing the influenced node in the static social network, and overlook the diversity of the influenced nodes. To address this issue, this work proposes a framework to diversify the influenced node in dynamic social networks. Utilizing the framework, our DCDIM algorithm identifies the communities dynamically and maximizes the communities of influential nodes using a proposed objective function. We prove that the proposed objective function is Monotonic, Submodular, and NP-Hard. The experiments have been conducted on four datasets, and the experimental results show that the proposed approach achieves the maximum number of communities and gives competitive influenced node with the benchmark algorithms.

引用次数: 0

UAVs deployment optimization in cell-free aerial communication networks

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-06 DOI: 10.1016/j.comcom.2024.108041

Aya Ahmed , Cirine Chaieb , Wessam Ajib , Halima Elbiaze , Roch Glitho

This paper tackles the joint problem of user association, channel assignment, UAV placement, and transmit power allocation in cell-free wireless networks. Each user can either be directly connected to a ground base station (GBS) or through UAVs acting as relays. To address this, we formulate the problem mathematically as a mixed-integer non-convex program, to minimize the number of deployed UAVs under data rate requirements and coverage constraints. Since the problem is

NP

-hard, we propose a model-free algorithm utilizing the deep deterministic policy gradient method to handle UAV deployment and positioning within a continuous space domain. We also propose efficient heuristic and meta-heuristic algorithms for comparison purposes. Simulation results demonstrate the benefits of the cell-free concept in satisfying users and improving the performance of UAV-assisted wireless networks. They also validate the effectiveness of the proposed algorithms in minimizing the required number of deployed UAVs to meet stringent user requirements.

引用次数: 0

5G for connected and automated mobility - Network level evaluation on real neighboring 5G networks: The Greece - Turkey cross border corridor

IF 4.5 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications

Pub Date : 2025-01-04 DOI: 10.1016/j.comcom.2025.108047

Konstantinos Trichias , Serhat Col , Ioannis Masmanidis , Afrim Berisha , Foteini Setaki , Panagiotis Demestichas , Symeon Papavassiliou , Nikolaos Mitrou

The automotive industry has been one of the vertical sectors eagerly waiting for the extended availability of 5G connectivity in order to deliver Connected and Automated Mobility (CAM) services. These services require extremely fast and reliable, uninterrupted communication to guarantee the safety of the drivers and other road users. Even though extended analysis and evaluation of the expected performance of 5G for CAM services has taken place in the past years via simulation studies and local trials based on 5G experimental testbeds, performance evaluation based on real 5G networks has been extremely limited, due to their unavailability until recently. Even more so in rural/highway conditions, as the 5G deployments so far have been focused on urban environments with greater population coverage. This article is among the first to present evaluation data and the corresponding analysis of the 5G Non-Stand Alone (NSA) network performance for CAM services based on neighboring 5G (overlay) networks in the cross-border corridor between Greece and Turkey, by one of the leading global 5G vendors and two of the top national operators. The performance evaluation focuses on the effect of inter-PLMN (Public Land Mobile Network) Handovers on the throughput, latency and interruption time experienced by a mobile user, and the network metrics achievable under various network configurations.

{"title":"5G for connected and automated mobility - Network level evaluation on real neighboring 5G networks: The Greece - Turkey cross border corridor","authors":"Konstantinos Trichias , Serhat Col , Ioannis Masmanidis , Afrim Berisha , Foteini Setaki , Panagiotis Demestichas , Symeon Papavassiliou , Nikolaos Mitrou","doi":"10.1016/j.comcom.2025.108047","DOIUrl":"10.1016/j.comcom.2025.108047","url":null,"abstract":"<div><div>The automotive industry has been one of the vertical sectors eagerly waiting for the extended availability of 5G connectivity in order to deliver Connected and Automated Mobility (CAM) services. These services require extremely fast and reliable, uninterrupted communication to guarantee the safety of the drivers and other road users. Even though extended analysis and evaluation of the expected performance of 5G for CAM services has taken place in the past years via simulation studies and local trials based on 5G experimental testbeds, performance evaluation based on real 5G networks has been extremely limited, due to their unavailability until recently. Even more so in rural/highway conditions, as the 5G deployments so far have been focused on urban environments with greater population coverage. This article is among the first to present evaluation data and the corresponding analysis of the 5G Non-Stand Alone (NSA) network performance for CAM services based on neighboring 5G (overlay) networks in the cross-border corridor between Greece and Turkey, by one of the leading global 5G vendors and two of the top national operators. The performance evaluation focuses on the effect of inter-PLMN (Public Land Mobile Network) Handovers on the throughput, latency and interruption time experienced by a mobile user, and the network metrics achievable under various network configurations.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"232 ","pages":"Article 108047"},"PeriodicalIF":4.5,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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