Computer Communications最新文献

{"title":"A privacy-enhancing and lightweight framework for device-free localization-based AIoT system","authors":"Haoda Wang ,&nbsp;Chen Zhang ,&nbsp;Lingjun Zhao ,&nbsp;Huakun Huang ,&nbsp;Chunhua Su","doi":"10.1016/j.comcom.2025.108200","DOIUrl":"10.1016/j.comcom.2025.108200","url":null,"abstract":"<div><div>With the growing demand for location-based services in smart cities, Artificial Intelligence of Things (AIoT)-enabled device-free methods have gained attention for their ability to address privacy and usability challenges. WiFi-based target localization, leveraging channel state information, offers advantages such as ease of deployment and obstacle penetration but faces privacy and computational challenges in centralized training. To address these issues, we propose a privacy-enhancing and lightweight federated device-free localization framework (PLDFL). The PLDFL integrates local differential privacy in federated learning to safeguard user data, uses the Fisher Information Matrix for model pruning to reduce model complexity, and employs three-dimensional convolutional neural network (3DCNN) for efficient feature extraction. Experimental results on real-world data validate its effectiveness in achieving accurate, private, and lightweight device-free localization.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"240 ","pages":"Article 108200"},"PeriodicalIF":4.5,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941656","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}

{"title":"Standardizing the evaluation framework for ECG-based authentication in IoT devices","authors":"Bonan Zhang ,&nbsp;Lin Li ,&nbsp;Chao Chen ,&nbsp;Ickjai Lee ,&nbsp;Kyungmi Lee ,&nbsp;Kok-Leong Ong","doi":"10.1016/j.comcom.2025.108201","DOIUrl":"10.1016/j.comcom.2025.108201","url":null,"abstract":"<div><div>Devices on the Internet of Things (IoT) often have constrained resources and operate in diverse environments, making them vulnerable to unauthorized access and cyber threats. Electrocardiogram (ECG) signals have emerged as a promising biometric for authenticating users in such settings. However, current ECG-based authentication studies lack a standardized evaluation framework tailored to resource-limited IoT contexts and long-term usage, making it difficult to assess their practical reliability. In this paper, we introduce a new evaluation framework for ECG-based authentication on IoT devices and construct a standardized dataset to facilitate rigorous testing. We categorize performance metrics into four key dimensions: scalability, adaptability, efficiency, and cancelability. Using this framework, we evaluate four representative ECG authentication algorithms for IoT devices. The results show that these algorithms struggle to maintain consistent performance under cross-session authentication scenarios. These findings highlight the critical importance of addressing the temporal variability of ECG signals and the current gap in robust ECG-based authentication for IoT devices. We believe the proposed framework will guide future research toward more resilient and secure ECG authentication systems for the IoT.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"240 ","pages":"Article 108201"},"PeriodicalIF":4.5,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143948818","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}

{"title":"LEBFL: Lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems with AIoT","authors":"Peng Liu ,&nbsp;Qian He ,&nbsp;Sen Li ,&nbsp;Yiting Chen ,&nbsp;Anfeng Liu","doi":"10.1016/j.comcom.2025.108196","DOIUrl":"10.1016/j.comcom.2025.108196","url":null,"abstract":"<div><div>Artificial Intelligence Internet of Things (AIoT) technology is gradually overcoming challenges related to traffic data transmission and processing in vehicle-road cooperative systems. However, the dynamism and openness of the vehicle-road cooperative networks make it susceptible to potential attacks, where attackers might intercept or tamper with transmitted local model parameters, thereby compromising the integrity of the models and leaking user privacy. Although existing solutions such as differential privacy and encryption can address these issues, they may reduce data availability or increase computational complexity. To tackle these challenges, we propose a lightweight authentication and efficient consensus for blockchained federated learning in vehicle–road cooperation systems(LEBFL), which provides lightweight privacy-enhanced authentication and efficient consensus while ensuring the privacy of local models and datasets. Specifically, we first introduce a blockchain-based federated learning architecture that enhances privacy and efficient consensus, utilizing the consortium blockchain to replace the centralized server. Subsequently, we design a lightweight anonymous authentication and key agreement protocol using efficient cryptographic primitives to establish secure session keys for the transmission of local models. Furthermore, we propose a utility-based Raft consensus algorithm, which selects the optimal fog server as the leader node using a resource matrix and weight vector, and enhances the performance of the blockchain network by leveraging the idle computing resources of fog servers. Security analysis and experimental results confirm that the proposed scheme shows superior performance without sacrificing security.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108196"},"PeriodicalIF":4.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928713","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}

{"title":"Identification of path congestion status for network performance tomography using deep spatial–temporal learning","authors":"Chengze Du,&nbsp;Zhiwei Yu,&nbsp;Xiangyu Wang","doi":"10.1016/j.comcom.2025.108194","DOIUrl":"10.1016/j.comcom.2025.108194","url":null,"abstract":"<div><div>Network tomography plays a crucial role in assessing the operational status of internal links within networks through end-to-end path-level measurements, independently of cooperation from the network infrastructure. However, the accuracy of performance inference in internal network links heavily relies on comprehensive end-to-end path performance data. Most network tomography algorithms employ conventional threshold-based methods to identify congestion along paths, while these methods encounter limitations stemming from network complexities, resulting in inaccuracies such as misidentifying abnormal links and overlooking congestion attacks, thereby impeding algorithm performance. This paper introduces the concept of Additive Congestion Status to address these challenges effectively. Using a framework that combines Adversarial Autoencoders (AAE) with Long Short-Term Memory (LSTM) networks, this approach robustly categorizes (as uncongested, single-congested, or multiple-congested) and quantifies (regarding the number of congested links) the Additive Congestion Status. Leveraging prior path information and capturing spatio-temporal characteristics of probing flows, this method significantly enhances the localization of congested links and the inference of link performance compared to conventional network tomography algorithms, as demonstrated through experimental evaluations.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108194"},"PeriodicalIF":4.5,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143941096","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}

{"title":"Disjoint end-to-end walks for Service Function Chain provisioning and protection","authors":"Mohand Yazid Saidi","doi":"10.1016/j.comcom.2025.108193","DOIUrl":"10.1016/j.comcom.2025.108193","url":null,"abstract":"<div><div>Service Function Chains (SFCs) enable traffic to flow through Virtual Network Functions (VNFs) deployed on physical servers to deliver network services. While SDN (Software Defined Network)/NFV (Network Function Virtualization) provides flexible network management, ensuring service continuity remains challenging and requires robust protection mechanisms.</div><div>Network failures can be addressed through local or end-to-end protection approaches. Local protection relies on multiple detours, potentially protecting against failures of the same components, which leads to complex management and multiple detour activations for single failures. End-to-end protection ensures service continuity through two end-to-end disjoint SFC provisioning paths, significantly reducing both route maintenance overhead and resource allocations.</div><div>This paper addresses the challenge of finding two end-to-end disjoint paths for SFC provisioning and protection. We first model the problem using ILP and prove its <span><math><mi>NP</mi></math></span>-completeness, even in over-resourced networks where single SFC provisioning is polynomial-time solvable. To address this complexity, we propose a novel three-step heuristic that enhances protection through transient route computation that is intended to “leave room” and facilitate disjoint provisioning identification. Recomputation of the transient route is established to enhance the resource allocation while improving the protection efficiency. Our extensive simulations demonstrate significant improvements over conventional approaches, showing notable enhancement in both protection efficiency and SFC path quality without incurring additional costs.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108193"},"PeriodicalIF":4.5,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928712","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}

{"title":"Accountable privacy-enhanced multi-authority attribute-based authentication scheme for cloud services","authors":"Xin Liu ,&nbsp;Hao Wang ,&nbsp;Bo Zhang ,&nbsp;Bin Zhang","doi":"10.1016/j.comcom.2025.108205","DOIUrl":"10.1016/j.comcom.2025.108205","url":null,"abstract":"<div><div>Current attribute-based authentication (ABA) schemes have three major drawbacks: first, the single attribute authority (AA) becomes the system bottleneck, i.e., if the AA is corrupted, the entire system will stop working; second, user privacy is not completely secured; and third, malicious users may exploit their anonymity. To overcome these defects, we improved a previously established privacy-preserving decentralized ciphertext policy attribute-based encryption (PPD-CP-ABE) scheme, obtaining a PPD-CP-ABE with verifiable outsourced decryption (PPD-CP-ABE-VOD). This improved scheme uses outsourced decryption, secure two-party computation protocol, and zero-knowledge proofs. We transformed the PPD-CP-ABE-VOD scheme into a new privacy-enhanced multi-authority ABA scheme using an identity tracing mechanism based on linear encryption. This new scheme has the following advantages over similar schemes. First, it introduces multiple AAs and does not require users to trust AA fully. Second, it protects users’ attributes, global identifiers, and access behavior, thus strengthening user privacy protection. Finally, it balances user privacy protection and user accountability. Theoretical and experimental analyses have shown that the new scheme is comparable to recently proposed ABA systems in terms of performance in the key generation and authentication phases, despite appending multiple security properties.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"242 ","pages":"Article 108205"},"PeriodicalIF":4.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144863898","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}

{"title":"An anonymous and privacy-preserving lightweight authentication protocol for secure communication in UAV-assisted IoAV networks","authors":"Mohd Shariq ,&nbsp;Norziana Jamil ,&nbsp;Gopal Singh Rawat ,&nbsp;Shehzad Ashraf Chaudhry ,&nbsp;Mehedi Masud ,&nbsp;Angelo Cangelosi","doi":"10.1016/j.comcom.2025.108192","DOIUrl":"10.1016/j.comcom.2025.108192","url":null,"abstract":"<div><div>With the rapid proliferation of the Internet of Things (IoT), autonomous vehicles (AVs), or self-driving cars, rely heavily on real-time data sharing and message exchanges over wireless networks. AVs use sensors, artificial intelligence, machine learning, and advanced algorithms to perform various functions, enabling users to operate without human intervention. Owing to the flexibility and high mobility of drones, they could aid in the operations of AVs. However, the security and privacy are the main concerns; specifically, the threat of physical capture and violation of anonymity are the main hurdles for realization of secure communication among the AVs and drones. To address these challenges, we propose an anonymous and provably Secure Lightweight Authentication Protocol for unmanned-aerial-vehicle-assisted Internet of Autonomous Vehicles (SLAP-IoAV). The proposed protocol uses cryptographic primitives such as exclusive-OR operations, elliptic-curve cryptography, collision-resistant one-way hashing, and concatenation to ensure robust security. An informal security analysis found that SLAP-IoAV is secure against several known attacks, and a performance analysis established that the protocol has less computational and communication overhead than existing competitive protocols. Additionally, Scyther simulation results confirm that no security vulnerabilities are present. Overall, our protocol delivers superior security and performance, making it well-suited to real-world applications in the AV industry.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108192"},"PeriodicalIF":4.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947434","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}

{"title":"Adaptive layer-wise personalized federated learning via dual delay update in future communication networks","authors":"Haowen Xu ,&nbsp;Yingchi Mao ,&nbsp;Si Chen ,&nbsp;Yi Rong ,&nbsp;Tasiu Muazu ,&nbsp;Xiaoming He","doi":"10.1016/j.comcom.2025.108195","DOIUrl":"10.1016/j.comcom.2025.108195","url":null,"abstract":"<div><div>The future communication networks refers to large-scale mass-connected networks consisting of billions of cloud, edge, and end devices, which are expected to support the ever-growing communication demands. In the future communication networks, billions of end devices generate massive amount of data that needs to be processed and analyzed (<em>e.g.</em>, model training). Artificial Intelligence of Things (AIoT) is a groundbreaking technology that leverages artificial intelligence models to process and analyze data generated by a large number of internet of things devices. As an emerging AIoT method, personalized Federated Learning (pFL) has emerged prominently in distributed model training using massive data from the future communication networks. However, it is challenging to accomplish high-performance and communication-efficient model training by existing pFL methods in the future communication networks, due to the following limitations. a) Dynamic role differences in each layer of a multi-layer model are neglected, leading to poor accuracy in customized models deployed on end devices. b) Owing to numerous end devices in the future communication networks, the communication frequency between a cloud server and end devices is extremely high in each communication round, resulting in expensive communication cost. To solve these two limitations, this paper presents a novel pFL framework for distributed model training in the future communication networks, called <em>Adaptive Layer-wise personalized Federated Learning via Dual Delay Update</em> (ALpFLDDU). First, in end devices, a layer-wise aggregation scheme based on an adaptive weight calculation mechanism is designed to capture the dynamic role differences of model layers. Second, in each communication round, we develop a dual delay update strategy to reduce communication frequency between a cloud server and end devices while ensuring model performance. Simulation experiments on text and image classification datasets are conducted. The experimental results show that ALpFLDDU realizes higher classification precision and lower communication cost than advanced pFL benchmarks on various classification tasks.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108195"},"PeriodicalIF":4.5,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918540","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}

{"title":"FedBH: Efficient federated learning through shared base and adaptive hyperparameters in heterogeneous systems","authors":"Zediao Liu ,&nbsp;Yayu Luo ,&nbsp;Tongzhijun Zhu ,&nbsp;Ziyi Chen ,&nbsp;Tenglong Mao ,&nbsp;Huan Pi ,&nbsp;Ying Lin","doi":"10.1016/j.comcom.2025.108190","DOIUrl":"10.1016/j.comcom.2025.108190","url":null,"abstract":"<div><div>Federated learning is a distributed learning framework that protects client data privacy. However, in real-world applications, due to the significant differences in data distribution among different clients, a single global model often fails to meet personalized needs. Moreover, existing personalized federated learning methods often struggle to balance computational efficiency and model personalization while inadequately addressing the heterogeneous computational capabilities of clients. To address these challenges, we propose FedBH—an innovative personalized federated learning method that combines the feature extraction capabilities of deep learning with an adaptive dynamic training mechanism. FedBH decomposes each client’s model into a global base layer and client-specific local heads, enhancing both computational efficiency and personalized training. To further optimize learning, FedBH incorporates a dynamic adjustment mechanism that adapts training parameters based on each client’s specific conditions. In each local training round, the algorithm samples a subset of the dataset and explores different configurations of local head and global base training rounds. The optimal configuration is determined based on validation loss and then applied for full training. This adaptive mechanism enables FedBH to dynamically adjust to varying client demands, ensuring efficient utilization of computational resources while maintaining robust personalization. Experimental results across multiple benchmark datasets and diverse data distribution scenarios show that FedBH achieves faster convergence and higher accuracy in most cases. These findings demonstrate that FedBH not only effectively adapts to heterogeneous data environments but also significantly improves training efficiency, showing great potential in addressing issues related to diverse and non-IID data distributions.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108190"},"PeriodicalIF":4.5,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918539","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}

{"title":"A methodology for reproducible and portable experiment workflows","authors":"Henning Stubbe,&nbsp;Sebastian Gallenmüller,&nbsp;Georg Carle","doi":"10.1016/j.comcom.2025.108178","DOIUrl":"10.1016/j.comcom.2025.108178","url":null,"abstract":"<div><div>Testbeds allow the creation of research prototypes to test new ideas through practical experiments. This central role in validating ideas makes them irreplaceable tools for data-driven research in computer science. Various testbeds were created to provide testbeds for the scientific community. To simplify testbed usage, frameworks help to authenticate users, allocate resources, and run experiments. Each testbed typically implements its own framework using a specific API to realize experiments. Such an experiment design impedes the portability of experiments between different testbeds. In this paper, we present a solution where we port the pos experiment controller to the Chameleon and CloudLab testbed. The well-structured pos experiment workflow allows the creation of inherently reproducible experiments. Previously, the experiments using the pos workflow were only possible in dedicated testbeds. By introducing the portability feature, these experiments can run on Chameleon and CloudLab. We demonstrate that experiments can be executed on any of the mentioned platforms without changing the experiment definition. Based on these results, we discuss how the portability feature will be used in the upcoming SLICES-RI testbeds to create reproducible and easily-shareable experiments.</div></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"239 ","pages":"Article 108178"},"PeriodicalIF":4.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894582","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}