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GDD-Geo: IPv6 geolocation by graph dual decomposition

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108019

Chong Liu, Ruosi Cheng, Fuxiang Yuan, Shichang Ding, Yan Liu, Xiangyang Luo

IP geolocation is a technique used to infer the location of an IP address through its network measurement features. It is widely used in network security, network management, and location-based services. To improve geolocation accuracy in IPv6 networks, especially when landmarks are sparse, we propose an IPv6 geolocation method based on graph dual decomposition called GDD-Geo. GDD-Geo models an IPv6 address by its network measurement attributes, including paths, delay values, and addresses. The geolocation process involves comparing the similarity of these attributes. GDD-Geo comprises two sub-algorithms, GDD-CGeo and GDD-SGeo, which provide city-level and street-level (oriented) geolocation results, respectively. Particularly, we design two graph decomposition algorithms to transform the paths represented by router interfaces into the paths represented by subgraphs based on the characteristics of IPv6 address distribution and delay distribution. The former decomposition is the support for GDD-CGeo, while the latter decomposition is conducted on the results of the former decomposition and supports GDD-SGeo. Due to the aggregation and reconstruction effects of paths derived from graph decomposition, GDD-Geo can reduce the dependence on landmarks and thus can cope with the landmark-sparse scenarios. Experimental results of city-level geolocation show that GDD-CGeo can accurately geolocate the IPv6 targets at the city level. Street-level (oriented) geolocation results in six cities within different countries show that the median errors of GDD-SGeo are 1.66–5.27 km, and the mean errors are 2.55–5.88 km. Compared with popular algorithms SLG and MLP-Geo, GDD-SGeo performs significantly better on sparse landmark datasets, with at least a 60% decrease in errors.

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引用次数: 0

Combining transformer with a latent variable model for radio tomography based robust device-free localization

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108022

Hongzhuang Wu , Cheng Cheng , Tao Peng , Hongzhi Zhou , Tao Chen

Radio tomographic imaging (RTI) is a promising device-free localization (DFL) method for reconstructing the signal attenuation caused by physical objects in wireless networks. In this paper, we use the received signal strength (RSS) difference between the current and baseline measurements captured by a wireless network to achieve the RTI based DFL in a predefined monitoring area. RTI is formulated as solving a badly conditioned problem under complex noise. And the end-to-end deep learning method based on Transformers and latent variable models (LVMs) is considered to address the RTI problem. The data grouping strategy is designed to divide the RSS data into multiple spatially-correlated groups, and a Transformer-based convolutional neural network (TCNN) model is firstly developed for RTI, in which the Transformer blocks are able to help the model learn the more expressive feature for the environmental image reconstruction task. The RTI system is influenced by both sensor noise and environmental noise simultaneously. In order to improve the performance of the RTI method, a Transformer-based latent variable model (TLVM) is proposed further, where the robustness to interference can be enhanced by controlling the capacity of the latent variables. The comparative numerical experiments are conducted for RTI based DFL, and the efficacy of the proposed TCNN and TLVM based RTI methods is verified by the experimental results.

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引用次数: 0

An efficient sharding consensus protocol for improving blockchain scalability

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108032

Li Lu , Linfu Sun , Yisheng Zou

A consortium blockchain facilitates establishment credit among supply and demand agents on a cloud platform. HotStuff, a Byzantine fault-tolerance consensus protocol, predominates the consortium blockchains and has undergone extensive research and practical applications. However, its scalability remains limited with an increased number of nodes, making it unsuitable for large-scale transactions. Consequently, an improved sharding consensus protocol (IShard) is proposed to consider decentralization, security, and scalability within the consortium blockchain. First, IShard employs the jump consistent hash algorithm for reasonable node allocation within the network, thus reducing data migration resulting from shard modifications. Second, a credit mechanism is devised to reflect credit based on the behavior of nodes, optimizing consensus nodes to enhance performance. Third, a credit-based consensus protocol is introduced to concurrently handle transactions through sharding among multiple shards, distributing transactions to each shard to alleviate the overall burden, thus enhancing the scalability of the blockchain. Fourth, a node removal mechanism is devised to identify and eliminate Byzantine nodes, minimizing view changes and ensuring efficient system operation in an environment susceptible to Byzantine faults. Finally, IShard has demonstrated its ability to ensure security and liveness in shard transactions, subject to particular constraints regarding Byzantine nodes. In addition, transaction processes involving supply and demand agents are designed to enhance data reliability. Experimental results demonstrate that IShard surpasses current leading protocols, achieving a communication complexity of O(n) and superior throughput and scalability.

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引用次数: 0

Novel low-latency data gathering scheduling for multi-radio wireless multi-hop networks

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108020

Chi Zhang , Honglie Li , Mi Yan , Peng Guo , Yiyi Zhang , Zhe Tian

Due to the complicated terrain, wireless multi-hop networks (WMNs) are often needed in large industrial environment. As the distribution of WMNs’ nodes in practical industrial environment are usually quite uneven, WMNs are prone to suffer from serious congestion during the data gathering. Employing multiple radios can help to mitigate the congestion. This is due to their flexible capability to concurrently schedule each node’s transmission and reception. Most existing works for multi-radio WMNs study the scheduling of gathering traffic flow at each node, while little work studies the scheduling of gathering a fixed amount of data at the nodes. The latter is quite typical in practice, quite complicated as the transmission load of each node in this scenario is dynamic and related to the descendant nodes’ scheduling. In this paper, we propose a novel low-latency scheduling for gathering a fixed amount of data spread in multi-radio WMNs. The proposed scheduling dynamically assigns each node’s radios for transmission, reception and their targets, according to the current amount of local data and the assignment of neighboring nodes’ radios. Extensive simulations are conducted and the results show the remarkable performance of the proposed scheduling, when compared to related scheduling methods.

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引用次数: 0

Deadline-constrained routing based on power-law and exponentially distributed contacts in DTNs

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108038

Tuan Le

During a large-scale disaster, there is a severe destruction to physical infrastructures such as telecommunication and power lines, which result in the disruption of communication, making timely emergency response challenging. Since Delay Tolerant Networks (DTNs) are infrastructure-less, they tolerate physical destruction and thus can serve as an emergency response network during a disaster scenario. To be effective, DTNs need a routing protocol that maximizes the number of messages delivered within deadline. One obvious approach is to broadcast messages everywhere. However, this approach is impractical as DTNs are resource-constrained. In this work, we propose a cost-effective routing protocol based on the expected delivery delay that optimizes the number of messages delivered within deadline with a significantly low network overhead. Simulations using real-life mobility traces show that with our scheme, up to 95% of messages are delivered within deadline, while requiring on average less than three message copies.

引用次数: 0

Attribute-based policies through microservices in a smart home scenario

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108039

Alessandra Rizzardi, Sabrina Sicari, Alberto Coen-Porisini

Application containerization allows for efficient resource utilization and improved performance when compared to traditional virtualization techniques. However, managing multiple containers and providing services such as load balancing, fault tolerance and security represent challenging tasks in the emerging microservices architectures. In this context, Kubernetes platform allows to build resilient distributed containers. Besides its efficiency in terms of configuration and architectural resiliency, it must also guarantee the access control to the managed resources. In fact, information must be protected throughout the different microservices which compose an application. To cope with such an issue, this paper proposes the definition of attribute-based policies able to regulate data disclosure within a Kubernetes-based microservices network. Simulations are carried out in a local Minikube environment, considering a smart residence scenario. The investigated metrics include response time, required memory, CPU load, and disk usage.

引用次数: 0

Blockchain-based VANET edge computing-assisted cross-vehicle enterprise authentication scheme

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

Computer Communications

Pub Date : 2025-02-01 DOI: 10.1016/j.comcom.2024.108040

Jiaming Lai , Xiaohong Zhang , Shuling Liu , Shaojiang Zhong , Ata Jahangir Moshayedi

Vehicular Ad hoc Network (VANET) is considered one of the feasible solutions to improve the efficiency and safety of modern transportation systems, and it provides new opportunities for creating a safe and efficient traffic environment. In recent years, this technology has attracted extensive attention from the academic community. However, VANET is an open network with frequent information interaction, and users are vulnerable to security and privacy threats. The existing schemes mainly consider the identity authentication of vehicles in vehicle enterprises (VEs). Due to concerns about the leakage of core data, VEs lack the motivation to establish a cross-vehicle enterprise identity authentication framework. Based on the above analysis, we propose a cross-vehicle enterprise authentication architecture by designing a two-stage certificate generation mechanism where certificate authority (CA) and VEs cooperate to generate identity credentials for vehicles. To address the concerns of VEs, we establish distributed trust and enable information sharing across VEs by introducing a consortium blockchain composed of car companies, CA, and pseudonym certificate authority (PCA). Considering the need for vehicles to access road traffic information, we use a public blockchain to store public information, and the practical byzantine fault tolerant (PBFT) algorithm is used to reach consensus. Instead of using computationally complex bilinear pairing and mapping-to-point hashing operations, the proposed scheme uses an elliptic curve cryptosystem (ECC), considering the limited hardware resources of the vehicle and RSU. In addition, our scheme integrates edge computing to solve complex computing tasks that cannot be performed locally and further reduces system latency. Security analysis and performance analysis show that our scheme has better performance than existing schemes in terms of security, computational overhead, and communication overhead.

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引用次数: 0

Leveraging decentralized communication for privacy-preserving federated learning in 6G Networks

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

Computer Communications

Pub Date : 2025-01-28 DOI: 10.1016/j.comcom.2025.108072

Rafael Teixeira , Gabriele Baldoni , Mário Antunes , Diogo Gomes , Rui L. Aguiar

Artificial intelligence (AI) is a fundamental pillar in developing next-generation networks. Federated learning (FL) emerges as a promising solution to address data privacy concerns during AI model training within the network. However, training AI models on user equipment raises challenges regarding battery consumption, unreliable connections, and communication overhead. This paper proposes Zenoh, a data-centric communication middleware, as an alternative to the traditional Message Passing Interface (MPI) for FL applications. Zenoh’s decentralized nature and low communication overhead make it suitable for resource-constrained devices and unreliable network connections. The paper compares Zenoh and MPI in a realistic FL scenario, demonstrating Zenoh’s potential to outperform MPI in terms of flexibility, communication efficiency, and system complexity.

引用次数: 0

UAV-assisted mobile edge computing model for cognitive radio-based IoT networks

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

Computer Communications

Pub Date : 2025-01-27 DOI: 10.1016/j.comcom.2025.108071

Hisham M. Almasaeid

The explosive growth in Internet of Things (IoT) in terms of number of applications and deployed devices has created many challenges over the past decade. Among the most critical of which are the increasing demand on spectrum resources, the growing computation and data processing cost, and the limited energy resources. In this paper, we present a model for IoT networks that incorporates the technologies of cognitive radio (CR), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and radio-frequency energy harvesting to address the aforementioned challenges. In this model, UAVs provide computation and energy recharging services to IoT devices. These services can be requested/delivered through multiple spectrum bands by exploiting the CR technology. Specifically, aim at scheduling the task offloading and energy transmission/harvesting activities over time and frequency so that the maximum energy consumption rate among IoT devices is minimized. A mixed integer linear program was formulated to find such schedule. A greedy sub-optimal algorithm was also proposed, where our results show that it is within

\approx 11 %

of the optimal solution.

We also investigate the maximum energy consumption rate among IoT devices under several settings regarding number of UAV MEC servers, task size, task offloading cost, and task local computation cost.

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引用次数: 0

RP-DFC: Responsive Probes and Dynamic Flow Classification based load balancing in datacenter networks

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

Computer Communications

Pub Date : 2025-01-27 DOI: 10.1016/j.comcom.2025.108069

Bo Li, Qiang Li, Bo Peng, Ji Zhao, Shunhua Tan

Datacenter networks achieve high bandwidth by establishing multiple accessible paths between hosts. This necessitates a load-balancing approach to effectively select optimal paths for data flows, minimizing transmission delays and path congestion. The current congestion awareness and load-balancing methods that rely on active detection encounter issues with substantial bandwidth overhead from probes and overlook the distribution characteristics of network traffic. This paper introduces RP-DFC as a distributed load-balancing approach that utilizes responsive probes and flow classification within the data plane. RP-DFC employs in-band network telemetry and active detection to create a responsive probe congestion awareness mechanism. This mechanism can adaptively adjust the detection frequency according to the network congestion status, significantly decreasing the bandwidth overhead of active detection. RP-DFC further enhances network performance in high-load scenarios by employing advanced large-flow and small-flow classification techniques tailored to data centers’ unique traffic distribution characteristics. This strategic implementation at the network edge optimizes traffic management, significantly outperforming existing methods. RP-DFC exhibits a substantial 30% performance improvement over HULA under high-load conditions, concurrently reducing probe overhead by an impressive 98%. Moreover, when benchmarked against alternative methods like W-ECMP, RP-DFC achieves a notable 29% enhancement in performance, highlighting its effectiveness in optimizing data center network operations.

{"title":"RP-DFC: Responsive Probes and Dynamic Flow Classification based load balancing in datacenter networks","authors":"Bo Li, Qiang Li, Bo Peng, Ji Zhao, Shunhua Tan","doi":"10.1016/j.comcom.2025.108069","DOIUrl":"10.1016/j.comcom.2025.108069","url":null,"abstract":"<div><div>Datacenter networks achieve high bandwidth by establishing multiple accessible paths between hosts. This necessitates a load-balancing approach to effectively select optimal paths for data flows, minimizing transmission delays and path congestion. The current congestion awareness and load-balancing methods that rely on active detection encounter issues with substantial bandwidth overhead from probes and overlook the distribution characteristics of network traffic. This paper introduces RP-DFC as a distributed load-balancing approach that utilizes responsive probes and flow classification within the data plane. RP-DFC employs in-band network telemetry and active detection to create a responsive probe congestion awareness mechanism. This mechanism can adaptively adjust the detection frequency according to the network congestion status, significantly decreasing the bandwidth overhead of active detection. RP-DFC further enhances network performance in high-load scenarios by employing advanced large-flow and small-flow classification techniques tailored to data centers’ unique traffic distribution characteristics. This strategic implementation at the network edge optimizes traffic management, significantly outperforming existing methods. RP-DFC exhibits a substantial 30% performance improvement over HULA under high-load conditions, concurrently reducing probe overhead by an impressive 98%. Moreover, when benchmarked against alternative methods like W-ECMP, RP-DFC achieves a notable 29% enhancement in performance, highlighting its effectiveness in optimizing data center network operations.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"234 ","pages":"Article 108069"},"PeriodicalIF":4.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143341153","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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