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A secure and dynamic fusion addressing scheme for Internet of Vehicles scenarios

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-11 DOI: 10.1016/j.comnet.2025.111112

Chao Liu, Fulong Chen, Taochun Wang, Chuanxin Zhao, Dong Xie, Peng Hu

The extensive deployment of Internet of Things (IoT) nodes has led to the emergence of various novel application scenarios, one of which is the Internet of Vehicles (IoV). Prior to efficacious data communication, IoT nodes must initially be equipped with a globally distinctive IPv6 address. Nevertheless, resource constraints and security vulnerabilities impose significant challenges to the addressing process. Existing addressing paradigms have been incapable of simultaneously meeting the requirements for security, scalability of the routing communication network, and address privacy protection of the terminal node. Consequently, they are not appropriate for utilization in IoV scenarios. To address these issues, this paper formulates a network model combining a static backbone network and a dynamic mobile network based on the characteristics of the IoV scenarios and designs a secure and dynamic fusion addressing scheme (SD-FAC). In SD-FAC, the backbone nodes utilize a lightweight security-enhanced neighbor discovery protocol based on location information to effectuate address registration, resisting message forgery, modification, and replay attacks, enhancing the security of the routing communication network, while supporting network scalability and automatic route establishment. The mobile nodes employ the congruence class approach to pre-construct independent address spaces and promptly complete association addressing upon entering the backbone network area. We analyze the addressing performance of the overall network model from eight indicators. Experimental simulation results demonstrate that the addressing latency, resource overhead, and energy consumption of the mobile network are all superior to those of the relevant addressing schemes.

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引用次数: 0

Age of collection with non-orthogonal multiple access: A theoretical-plus-experimental study

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-11 DOI: 10.1016/j.comnet.2025.111102

Yurong Lai , Xinhui Han , Xueer Wang , Tse-Tin Chan , Haoyuan Pan

This paper examines the concept of information freshness in a collaborative surveillance scenario utilizing non-orthogonal multiple access (NOMA). In this scenario, each monitoring device observes a portion of a common target and reports the latest status to a common access point (AP), which then recovers the complete observations. We use the age of collection (AoC) as a metric for information freshness. Unlike the conventional age of information (AoI) metric, the instantaneous AoC decreases only when the AP receives all partial updates from different devices (i.e., successfully receives a “joint” update). Consequently, existing AoI-oriented analyses on NOMA cannot be easily generalized to AoC due to the different update rules between AoC and AoI. Conventional NOMA schemes typically employ multiuser decoding (MUD) techniques to decode update messages from different devices, referred to here as the MUD-only NOMA system. However, MUD techniques are less effective when the signal-to-noise ratios (SNRs) of different NOMA users are nearly balanced. Therefore, we consider network-coded multiple access (NCMA), an advanced NOMA scheme that integrates MUD with physical-layer network coding (PNC). PNC is a technique that transforms wireless mutual interference into useful network-coded information, working well even when the SNRs of different users are similar. Experimental results using software-defined radios indicate that NCMA is a practical solution for achieving low average AoC under varying channel conditions. The combination of MUD and PNC enables the AP to receive joint update messages much faster, significantly reducing the average AoC of the system.

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引用次数: 0

Range-free localization algorithm based on modified distance and improved black-winged kite algorithm

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-10 DOI: 10.1016/j.comnet.2025.111091

Haibin Sun, Shuai Yang

Node localization is an important issue in wireless sensor networks and has received increasing attention in the field of IoT in recent years. The Distance Vector-Hop (DV-Hop) algorithm has been widely used due to its low cost and ease of application. However, it suffers from a large error due to the hop counting method and the network structures. In this paper, we optimize the distance estimation process in DV-Hop by proposing an error factor and designing an improved black-winged kite optimization algorithm (IBKA) to solve the problem of location calculation. In simulation tests, the convergence speed and accuracy of the IBKA are verified using different benchmark functions. In addition, the DV-Hop algorithm based on the IBKA is applied to the node localization process and compared with the four recent algorithms. The experimental results show that the algorithm obtains the best localization results on different network environments and C, H, and X-shaped network structures.

引用次数: 0

Exploring coverage and security challenges in wireless sensor networks: A survey

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-08 DOI: 10.1016/j.comnet.2025.111096

Mari M. Moslehi

This survey paper addresses critical issues related to area coverage and security in wireless sensor networks (WSNs). It highlights the essential challenges and corresponding solutions necessary for achieving effective and secure network deployments. The paper underscores the significance of integrating both coverage and security considerations in practical applications, such as environmental monitoring, precision agriculture, and surveillance. Various strategic methods for sensor deployment are discussed, including two-dimensional and three-dimensional approaches, as well as deterministic and stochastic methods. The impact of these strategies on coverage, connectivity, energy efficiency, and scalability is thoroughly analyzed. Additionally, the deployment of both homogeneous and heterogeneous sensors are explored, investigating how differing design criteria influence network deployment, operation, and security.

Key security challenges examined include encryption, authentication mechanisms, intrusion detection, resource depletion, and countermeasures for node compromise attacks. The insights gained from this analysis contribute to enhancing the reliability, energy efficiency, and overall resilience of WSNs, thereby establishing a foundation for future research on secure and efficient network deployment.

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引用次数: 0

Machine learning approaches for predicting link failures in production networks

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-08 DOI: 10.1016/j.comnet.2025.111098

Bruck W. Wubete, Babak Esfandiari, Thomas Kunz

Resolving network failures after they occur through human investigation is a costly and time-consuming process. Predicting upcoming failures could mitigate this to a large extent. In this work, we collect data from a large intercontinental network and study the problem of flapping links, which are indicative of link failures. Such flapping links have their routing metric increased to divert traffic away; this is followed by corrective actions, and eventually their routing metric is lowered again to carry traffic. Using the collected data, primarily metrics reported from Internet Protocol (IP) and optical layers of the network, we develop ML models to predict upcoming link failures. Exploring a sequence of increasingly complex models, we study the relevance of optical metrics, the underlying temporal relations, and the topological relations in improving the predictive model performance. We discovered that optical features such as optical maximum and minimum power or unavailable and errored seconds increased the model’s performance (measured in average precision) by about 9 percentage points while temporal and spatial features improved it further by 8 and 7 percentage points respectively for a total improvement of 24 percentage points.

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引用次数: 0

Enhancing Extended Reality Assisted Surgery through a Field-of-View Video Delivery Optimization

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-06 DOI: 10.1016/j.comnet.2025.111093

Daria Alekseeva , Anzhelika Mezina , Radim Burget , Otso Arponen , Elena Simona Lohan , Aleksandr Ometov

Emerging Extended Reality (XR) applications bring new opportunities for digital healthcare systems, i.e., eHealth. XR-assisted surgery is one of the most outstanding examples of future technology that has a high social impact on the healthcare and medical educational system. The current work presents the intelligent design for remote XR-assisted surgery. The study presents the Field-of-View (FoV)-based viewport model empowered with behavioral data. It applies the viewport prediction model based on the behavioral data by applying Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM). In the final analysis, LSTM showed lower errors and a higher coefficient of determination, but ANN performed much faster. Finally, the study defines the dynamic system’s states for adaptive and fast video delivery concerning Quality of Experience (QoE). The presented approach aims to mitigate the delay to ensure smooth playback and display high-quality images.

引用次数: 0

KTWIN: A serverless Kubernetes-based Digital Twin platform

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-06 DOI: 10.1016/j.comnet.2025.111095

Alexandre Gustavo Wermann, Juliano Araujo Wickboldt

Digital Twins (DTs) systems are virtual representations of physical assets allowing organizations to gain insights and improve existing processes. In practice, DTs require proper modeling, coherent development and seamless deployment along cloud and edge landscapes relying on established patterns to reduce operational costs. In this work, we propose KTWIN a Kubernetes-based Serverless Platform for Digital Twins. KTWIN was developed using the state-of-the-art open-source Cloud Native tools, allowing DT operators to easily define models through open standards and configure details of the underlying services and infrastructure. The experiments carried out with the developed prototype show that KTWIN can provide a higher level of abstraction to model and deploy a Digital Twin use case without compromising the solution scalability. The tests performed also show cost savings ranging between 60% and 80% compared to overprovisioned scenarios.

引用次数: 0

Mx-TORU: Location-aware multi-hop task offloading and resource optimization protocol for connected vehicle networks

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-06 DOI: 10.1016/j.comnet.2025.111094

Oğuzhan Akyıldız , Feyza Yıldırım Okay , İbrahim Kök , Suat Özdemir

Connected Vehicle Networks (CVNs), as a part of Internet of Vehicles (IoV), represent an innovative solution for enhancing communication between vehicles and Internet of Things (IoT) devices within transportation infrastructures. However, task offloading in CVNs presents significant challenges due to high computational demands and the dynamic nature of network conditions. While traditional static fog networks support CVNs, they often suffer from inefficiencies in resource allocation, leading to underutilization or over-utilization, as well as elevated maintenance costs. To address these limitations, mobile fog computing emerges as a more adaptable solution, enabling efficient task processing by leveraging the resources of nearby vehicles. In this paper, we introduce a novel mobility-driven protocol, Mx-TORU, which combines multi-hop task offloading with resource optimization to enhance task processing efficiency in CVNs. This protocol builds upon our previously proposed MobTORU framework, aiming to maximize resource utilization through dynamic multi-hop strategies. Extensive experiments using real-world vehicular mobility data demonstrate that Mx-TORU improves resource utilization by up to 17.8% compared to one-hop methods. Additionally, our Mx-TORU protocol and the employed RELiOff algorithm show a consistent improvement of at least 5% in task offloading efficiency across various test scenarios including intelligent transformation systems.

{"title":"Mx-TORU: Location-aware multi-hop task offloading and resource optimization protocol for connected vehicle networks","authors":"Oğuzhan Akyıldız , Feyza Yıldırım Okay , İbrahim Kök , Suat Özdemir","doi":"10.1016/j.comnet.2025.111094","DOIUrl":"10.1016/j.comnet.2025.111094","url":null,"abstract":"<div><div>Connected Vehicle Networks (CVNs), as a part of Internet of Vehicles (IoV), represent an innovative solution for enhancing communication between vehicles and Internet of Things (IoT) devices within transportation infrastructures. However, task offloading in CVNs presents significant challenges due to high computational demands and the dynamic nature of network conditions. While traditional static fog networks support CVNs, they often suffer from inefficiencies in resource allocation, leading to underutilization or over-utilization, as well as elevated maintenance costs. To address these limitations, mobile fog computing emerges as a more adaptable solution, enabling efficient task processing by leveraging the resources of nearby vehicles. In this paper, we introduce a novel mobility-driven protocol, Mx-TORU, which combines multi-hop task offloading with resource optimization to enhance task processing efficiency in CVNs. This protocol builds upon our previously proposed MobTORU framework, aiming to maximize resource utilization through dynamic multi-hop strategies. Extensive experiments using real-world vehicular mobility data demonstrate that Mx-TORU improves resource utilization by up to 17.8% compared to one-hop methods. Additionally, our Mx-TORU protocol and the employed RELiOff algorithm show a consistent improvement of at least 5% in task offloading efficiency across various test scenarios including intelligent transformation systems.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"259 ","pages":"Article 111094"},"PeriodicalIF":4.4,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143372267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 cost-efficient traffic engineering framework with various pricing schemes in clouds

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-06 DOI: 10.1016/j.comnet.2025.111090

Jingzhou Wang , Gongming Zhao , Hongli Xu , Chunming Qiao , He Huang

The rapid growth of cloud services has brought a significant increase in inter-datacenter traffic. To transfer data among geographically distributed datacenters, cloud providers need to purchase bandwidth from ISPs. The data transferring cost has become one of the major expenses for cloud providers. Therefore, it is essential for a cloud provider to carefully allocate inter-datacenter traffic among the ISPs’ links to minimize the costs. Exiting solutions mainly focus on the situations where all links adopt the same pricing scheme. However, in practice, ISPs usually provide multiple pricing schemes for their links due to market competition, which makes the existing solutions non-optimal. Thus, a new traffic engineering approach that considers various pricing schemes is needed. This paper presents COIN, a new framework for cost-efficient traffic engineering with various pricing schemes. We propose a partition rounding traffic engineering algorithm based on linear independence analysis. The approximation factors and time complexity are formally analyzed. We further conduct large-scale simulations with real-world topologies and datasets. Extensive simulation results show that COIN can save the data transferring cost by up to 63.4% compared with the state-of-the-art solutions.

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引用次数: 0

Federated dual correction intrusion detection system: Efficient aggregation for heterogeneous data

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks

Pub Date : 2025-02-05 DOI: 10.1016/j.comnet.2025.111116

Zhigang Jin , Yu Ding , Xiaodong Wu , Xuyang Chen , Zepei Liu , Gen Li

Federated learning-based intrusion detection system (FL-IDS) can effectively ensure global security without the concerns of data privacy, becoming the primary defense method for distributed networks. However, the inherent challenge of heterogeneous data in FL brings client drift to IDS. Besides, the dynamic learning of FL further aggravates model bias caused by the sparsity of malicious traffic. Therefore, we propose a federated dual correction intrusion detection system called FIST-G2 to optimize global aggregation. In the first correction, a momentum-like update mechanism with gradient memory is proposed to solve the client drift. Specifically, with the gradient memory buffer, we leverage the current updated gradient change, historical information, and global information to fix the momentum factor used in global model update. We propose gradient memory buffering strategy in this mechanism to dynamically maintain the information of each client, particularly the records of stragglers. In the second correction, a fine-tuning mechanism with GAN boundary samples is proposed to alleviate the model bias. A generator, deployed on the server, which extracts local models’ knowledge by data-free knowledge distillation, is used to supplement rare traffic. By forming an adversarial training pattern instead of direct data balancing, a GAN boundary samples mining scheme is introduced to keep the ambiguity of samples to improve global model constantly. Extensive experiments on UNSW-NB15 dataset and CICIDS2018 dataset show that the proposed method is robust for heterogeneous data and completely compatible with many client-side optimization algorithms, having excellent scalability and portability.

{"title":"Federated dual correction intrusion detection system: Efficient aggregation for heterogeneous data","authors":"Zhigang Jin , Yu Ding , Xiaodong Wu , Xuyang Chen , Zepei Liu , Gen Li","doi":"10.1016/j.comnet.2025.111116","DOIUrl":"10.1016/j.comnet.2025.111116","url":null,"abstract":"<div><div>Federated learning-based intrusion detection system (FL-IDS) can effectively ensure global security without the concerns of data privacy, becoming the primary defense method for distributed networks. However, the inherent challenge of heterogeneous data in FL brings client drift to IDS. Besides, the dynamic learning of FL further aggravates model bias caused by the sparsity of malicious traffic. Therefore, we propose a federated dual correction intrusion detection system called FIST-G2 to optimize global aggregation. In the first correction, a momentum-like update mechanism with gradient memory is proposed to solve the client drift. Specifically, with the gradient memory buffer, we leverage the current updated gradient change, historical information, and global information to fix the momentum factor used in global model update. We propose gradient memory buffering strategy in this mechanism to dynamically maintain the information of each client, particularly the records of stragglers. In the second correction, a fine-tuning mechanism with GAN boundary samples is proposed to alleviate the model bias. A generator, deployed on the server, which extracts local models’ knowledge by data-free knowledge distillation, is used to supplement rare traffic. By forming an adversarial training pattern instead of direct data balancing, a GAN boundary samples mining scheme is introduced to keep the ambiguity of samples to improve global model constantly. Extensive experiments on UNSW-NB15 dataset and CICIDS2018 dataset show that the proposed method is robust for heterogeneous data and completely compatible with many client-side optimization algorithms, having excellent scalability and portability.</div></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":"259 ","pages":"Article 111116"},"PeriodicalIF":4.4,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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