TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Xiaonan Wang, Yajing Song	Personalized Preference and Social Attribute Based Data Sharing for Information-Centric IoT	2025	Early Access	Internet of Things Smart devices Performance evaluation	With the rapid increase in the number of smart devices connected to the Internet of Things (IoT), network traffic has imposed serious overload on backhaul networks and led to network congestion. Data sharing among IoT devices through multi-hop communication between smart devices is expected to ease increasing pressure of backhaul traffic. In this paper, we propose a personalized preference and social attribute based data sharing framework for information-centric IoT, aiming to improve success rates of data sharing among IoT devices and reduce data sharing delays. This framework proposes personalized preferences and social attributes to reduce data response time and avoid data delivery failures caused by obsolete FIB and broken reverse paths. The experiment results justify the advantages of the proposed framework in terms of data sharing success rates and delays.	10.1109/TNSM.2025.3529291
Xiaojun Zhang, Qing Liu, Bingyun Liu, Yuan Zhang, Jingting Xue	Dynamic Certificateless Outsourced Data Auditing Mechanism Supporting Multi-Ownership Transfer via Blockchain Systems	2025	Early Access	Cloud computing Servers Blockchains	Data auditing contributes to checking the integrity of outsourced data, promoting the vigorous development of cloud storage services. In actual scenarios, such as migration of electronic medical records or data transfer of enterprise mergers and acquisitions, it always require data auditing to help clients with dynamic data migration and integrity checking. In this paper, we present an efficient dynamic certificateless outsourced data auditing mechanism supporting multi-ownership transfer (CDA-MOT), addressing the issue of key escrow and without needing complex certificate management. By integrating a certificateless multi-signature on the same data file into the construction of a homomorphic authenticator based on the Lagrange inverse Multinomial theorem, CDA-MOT not only achieves integrity verification but also enables clients to transfer ownership rights and responsibilities for multi-ownership data in collaboration with cloud servers. Utilizing blockchain systems to store necessary data conversion and update records, as well as smart contracts to fulfill auditing tasks, CDA-MOT owns the characteristics of openness, transparency, accountability, and decentralized public auditing. Besides, CDA-MOT could be further applied in the extension of dynamic update operations, even if outsourced data have been transferred. The security analysis and performance evaluation have demonstrated the feasibility of CDA-MOT in the secure deployment of cloud storage.	10.1109/TNSM.2025.3525462
Ehsan Nowroozi, Imran Haider, Rahim Taheri, Mauro Conti	Federated Learning Under Attack: Exposing Vulnerabilities Through Data Poisoning Attacks in Computer Networks	2025	Early Access	Training Servers Data models	Federated Learning is an approach that enables multiple devices to collectively train a shared model without sharing raw data, thereby preserving data privacy. However, federated learning systems are vulnerable to data-poisoning attacks during the training and updating stages. Three data-poisoning attacks–label flipping, feature poisoning, and VagueGAN–are tested on FL models across one out of ten clients using the CIC and UNSW datasets. For label flipping, we randomly modify labels of benign data; for feature poisoning, we alter highly influential features identified by the Random Forest technique; and for VagueGAN, we generate adversarial examples using Generative Adversarial Networks. Adversarial samples constitute a small portion of each dataset. In this study, we vary the percentages by which adversaries can modify datasets to observe their impact on the Client and Server sides. Experimental findings indicate that label flipping and VagueGAN attacks do not significantly affect server accuracy, as they are easily detectable by the Server. In contrast, feature poisoning attacks subtly undermine model performance while maintaining high accuracy and attack success rates, highlighting their subtlety and effectiveness. Therefore, feature poisoning attacks manipulate the server without causing a significant decrease in model accuracy, underscoring the vulnerability of federated learning systems to such sophisticated attacks. To mitigate these vulnerabilities, we explore a recent defensive approach known as Random Deep Feature Selection, which randomizes server features with varying sizes (e.g., 50 and 400) during training. This strategy has proven highly effective in minimizing the impact of such attacks, particularly on feature poisoning.	10.1109/TNSM.2025.3525554
Hnin Pann Phyu, Diala Naboulsi, Razvan Stanica	ICE-CREAM: multI-agent fully CooperativE deCentRalizEd frAMework for Energy Efficiency in RAN Slicing	2025	Early Access	Quality of service Base stations Energy efficiency	Network slicing is one of the major catalysts proposed to turn future telecommunication networks into versatile service platforms. Along with its benefits, network slicing is introducing new challenges in the development of sustainable network operations, as it entails a higher energy consumption compared to non-sliced networks.Using a sliced architecture, which includes guaranteeing the communication and computation requirements for each slice, is essential for operators to provide a satisfying user quality of service (QoS) in a multi-service network. At the same time, building sustainable mobile networks, with the least amount of resources used, is crucial today, for both economic and environmental reasons. As a result, mobile operators need to find a middle ground between these two objectives – a tough nut considering they are both antithetical and important. In this light, we investigate a joint slice activation/deactivation and user association problem, with the aim of minimizing energy consumption and maximizing the QoS. The proposed multI-agent fully CooperativE deCentRalizEd frAMework (ICE-CREAM) addresses the formulated joint problem, with agents acting at two different granularity levels. Not only all the agents can access the shared information with their direct neighbors, but also they are trained with one global reward, which is an ideal approach in multi-agent cooperative settings. We evaluate ICE-CREAM using a real-world dataset that captures the spatio-temporal consumption of three different mobile services in France. Experimental results demonstrate that the proposed solution provides more than 30% energy efficiency improvement compared to a configuration where all the slice instances are always active while maintaining the same level of QoS. From a broader perspective, our work explicitly shows the impact of prioritizing the energy over QoS, and vice versa.	10.1109/TNSM.2024.3524503
Tao Huang, Jingyuan Liu, Zheng Chang, Yao Wei, Xu Zhao, Ying-Chang Liang	Energy Efficient Spectrum Sharing and Resource Allocation for 6G Air-Ground Integrated Networks	2025	Early Access	Resource management Autonomous aerial vehicles Interference	In this paper, we investigate the spectrum sharing and resource allocation scheme for air-ground integrated wireless network which consists of multiple unmanned aerial vehicles (UAVs) and a high altitude platform (HAP). We consider the UAVs are required to provide services or execute certain missions in the area that HAP owns the spectrum and other resources. Correspondingly, we propose an energy efficient spectrum sharing and resource allocation scheme so that the UAVs can flexibly utilize the radio resources within the area without degrading the quality of service (QoS) of the HAP. In the proposed scheme, we jointly optimize pricing of spectrum and transmit power to maximize the utility of both the HAP and UAVs in the considered system in an energy efficient manner. A game theoretic approach is then presented to find the spectrum sharing and resource allocation strategies for both HAP and UAVs and the problem has been addressed via convex optimization. Our extensive simulations demonstrate marked improvements in system utility, spectrum and energy efficiency, and also highlight the effectiveness of the proposed scheme.	10.1109/TNSM.2025.3527651
Xinyu Yuan, Yan Qiao, Zhenchun Wei, Zeyu Zhang, Minyue Li, Pei Zhao, Rongyao Hu, Wenjing Li	Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis With Diffusion-Based Approach	2025	Early Access	Estimation Routing Training	Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets.	10.1109/TNSM.2025.3527442
Chaofeng Lin, Jinchuan Tang, Shuping Dang, Gaojie Chen	Priority-Based Blockchain Packing for Dependent Industrial IoT Transactions	2025	Early Access	Time factors Industrial Internet of Things Blockchains	Blockchain plays a key role in establishing secure and decentralized Industrial Internet of Things (IIoT) systems. Currently, the dependent transactions generated by IIoT devices require a packing process to select a set of non-conflicted transactions, which results in significant delay and deviation of the transaction response time. In this paper, we propose a novel transaction packing algorithm named Priority-Pack to address the above issue. Firstly, we use directed acyclic graphs to model the dependent transactions in IIoT systems to establish the mathematical relationships between transaction priority and waiting time as well as dependencies. Secondly, we propose an algorithm to specify a higher priority to a transaction with longer waiting time without violating transaction dependencies. It eliminates the time required to traverse the subsets of transactions in other algorithms. Thirdly, to further reduce the response delay for transactions with the same priority level, we choose to first pack transactions with smaller sizes. We prove that this selection can achieve the lowest average response time. Finally, simulations are conducted to benchmark the Priority-Pack against the state-of-the-art algorithms including Fair-Pack and Random-Pack. The results demonstrate that Priority-Pack outperforms the others in terms of average response time and deviations.	10.1109/TNSM.2025.3527810
Ammar Kamal Abasi, Moayad Aloqaily, Mohsen Guizani	6G mmWave Security Advancements through Federated Learning and Differential Privacy	2025	Early Access	Millimeter wave communication 6G mobile communication Data models	This paper presents a new framework that integrates Federated Learning (FL) with advanced privacy-preserving mechanisms to enhance the security of millimeter-wave (mmWave) beam prediction systems in 6G networks. By decentralizing model training, the framework safeguards sensitive user information while maintaining high model accuracy, effectively addressing privacy concerns inherent in centralized Machine learning (ML) methods. Adaptive noise augmentation and differential privacy principles are incorporated to mitigate vulnerabilities in FL systems, providing a robust defense against adversarial threats such as the Fast Gradient Sign Method (FGSM). Extensive experiments across diverse scenarios, including adversarial attacks, outdoor environments, and indoor settings, demonstrate a significant 17.45% average improvement in defense effectiveness, underscoring the framework’s ability to ensure data integrity, privacy, and performance reliability in dynamic 6G environments. By seamlessly integrating privacy protection with resilience against adversarial attacks, the proposed solution offers a comprehensive and scalable approach to secure mmWave communication systems. This work establishes a critical foundation for advancing secure 6G networks and sets a benchmark for future research in decentralized, privacy-aware machine learning systems.	10.1109/TNSM.2025.3528235
Abdulsamet Dağaşan, Ezhan Karaşan	Resilient Multi-Hop Autonomous UAV Networks With Extended Lifetime for Multi-Target Surveillance	2025	Early Access	Autonomous aerial vehicles Relays Target tracking	Cooperative utilization of Unmanned Aerial Vehicles (UAVs) in public and military surveillance applications has attracted significant attention in recent years. Most UAVs are equipped with sensors and wireless communication equipment with limited ranges. Such limitations pose challenging problems to monitor mobile targets. This paper examines fulfilling surveillance objectives to achieve better coverage while building a resilient network between UAVs with an extended lifetime. The multiple target tracking problem is studied by including a relay UAV within the fleet whose trajectory is autonomously calculated in order to achieve a reliable connected network among all UAVs. Optimization problems are formulated for single-hop and multi-hop communications among UAVs. Three heuristic algorithms are proposed for multi-hop communications and their performances are evaluated. A hybrid algorithm, which dynamically switches between single-hop and multi-hop communications is also proposed. The effect of the time horizon considered in the optimization problem is also studied. Performance evaluation results show that the trajectories generated for the relay UAV by the hybrid algorithm can achieve network lifetimes that are within 95% of the maximum possible network lifetime which can be obtained if the entire trajectories of all targets were known a priori.	10.1109/TNSM.2025.3528495
Xingyu Yang, Jipeng Hou, Lei Xu, Liehuang Zhu	zkFabLedger: Enabling Privacy Preserving and Regulatory Compliance in Hyperledger Fabric	2025	Early Access	Blockchains Privacy Regulation	Preserving the privacy of transactions and ensuring the regulatory compliance of transactions are two important requirements for blockchain-based financial applications. However, these two requirements are somewhat contradictory. Techniques for protecting transaction privacy, such as data encryption and zero-knowledge proof, generally make it difficult to regulate and audit the transactions. In this paper, we propose a system named zkFabLedger which enhances both the privacy and the auditability of the classic permissioned blockchain platform Hyperledger Fabric. The proposed system utilizes commitments and non-interactive zero-knowledge proofs to hide the detailed information of transactions while enabling the endorsing peer nodes to verify the regulatory compliance of transactions. Transactions are recorded on table-structured ledgers, so that the regulator can perform complex auditing of transactions. Moreover, we utilize the ring signature scheme and the secret handshake protocol to ensure the anonymity of the transaction sender while enabling the regulator to trace the sender’s identity. Simulation results demonstrate that the proposed system can balance well between privacy, regulation and efficiency.	10.1109/TNSM.2024.3525045
Lo-Yao Yeh, Sheng-Po Tseng, Chia-Hsun Lu, Chih-Ya Shen	Auditable Homomorphic-Based Decentralized Collaborative AI with Attribute-based Differential Privacy	2025	Early Access	Servers Blockchains Data models	In recent years, the notion of federated learning (FL) has led to the new paradigm of distributed artificial intelligence (AI) with privacy preservation. However, most current FL systems suffer from data privacy issues due to the requirement of a trusted third party. Although some previous works introduce differential privacy to protect the data, however, it may also significantly deteriorate the model performance. To address these issues, we propose a novel decentralized collaborative AI framework, named Auditable Homomorphic-based Decentralised Collaborative AI (AerisAI), to improve security with homomorphic encryption and fine-grained differential privacy. Our proposed AerisAI directly aggregates the encrypted parameters with a blockchain-based smart contract to get rid of the need of a trusted third party. We also propose a brand-new concept for eliminating the negative impacts of differential privacy for model performance. Moreover, the proposed AerisAI also provides the broadcast-aware group key management based on ciphertext-policy attribute-based encryption (CP-ABE) to achieve fine-grained access control based on different service-level agreements. We provide a formal theoretical analysis of the proposed AerisAI as well as the functionality comparison with the other baselines. We also conduct extensive experiments on real datasets to evaluate the proposed approach. The experimental results indicate that our proposed AerisAI significantly outperforms the other state-of-the-art baselines.	10.1109/TNSM.2025.3529774
Zhengran Tian, Hao Wang, Zhi Li, Ziyu Niu, Xiaochao Wei, Ye Su	MDTL: Maliciously Secure Distributed Transfer Learning Based on Replicated Secret Sharing	2025	Early Access	Transfer learning Protocols Cryptography	As data continues to grow at an unprecedented rate and informationization accelerates, concerns over data privacy have become more prominent. In image classification tasks, the challenge of insufficient labeled data is common. Transfer learning, an effective and important machine learning method, can address this issue by leveraging knowledge from the source domain to enhance performance in the target domain. However, existing privacy-preserving transfer learning schemes continue to face challenges related to low security and multiple rounds of communication. In the following works, we design a three-party privacy-preserving transfer learning protocol based on the Joint Distributed Adaptation (JDA) algorithm, which ensures malicious security under an honest majority model. To realize this protocol, we designed a series of sub-protocols for constant-round communication, including distributed solving of eigenvalues and eigenvectors based on replicated secret sharing techniques. Compared to existing work, our protocol requires fewer rounds and satisfies malicious security. We provide formal security proofs for the designed protocol and assess its performance using real datasets. Our protocol for computing the eigenvalues of matrices in a given dimension is approximately 2.5 times faster than existing methods. The results of the experiments demonstrate both the security and effectiveness of the proposed approach.	10.1109/TNSM.2025.3529471
Ruowen Yan, Qiao Li, Huagang Xiong	Optimizing Traffic Management in Airborne Power Line Communication Networks: A Credit-Based Shaping Approach Using Network Calculus	2025	Early Access	Real-time systems Protocols Media Access Control	As the aviation industry progresses towards More Electric Aircraft (MEA), the demand for robust and efficient data communication systems intensifies. Traditional fieldbus systems are burdened by high installation costs and substantial weight due to extensive cabling requirements. The Power Line Communication (PLC) technology presents a promising alternative; however, its adaptation to the stringent real-time demands of airborne environments poses significant challenges. To address this, this paper introduces a novel Credit-Based Shaper with Channel Contention (CBSCC) mechanism designed to optimize traffic management in airborne PLC networks. This mechanism operates at the Medium Access Control (MAC) layer of the HomePlug AV 2 protocol, employing a dynamic configuration approach informed by Network Calculus (NC). This approach utilizes End-to-End Delay (E2ED) requirements of data flows and network configuration details to calculate the parameters for the CBSCC traffic shaper. Comprehensive simulations conducted with OMNeT++ demonstrate the efficacy of CBSCC, demonstrating marked improvements in E2ED satisfaction for all data frames, reduced average access delays, and enhanced fairness across different priority levels compared to the HomePlug AV2 protocol and previous traffic management strategies. The findings confirm that the CBSCC mechanism substantially alleviates the starvation of lower-priority traffic, boosts network efficiency, and ensures robust real-time guarantees essential for the safety and reliability of airborne communication systems. This research represents a substantial advancement over existing solutions, aligning with the evolving needs of MEA implementations.	10.1109/TNSM.2025.3529871
Gerald Budigiri, Christoph Baumann, Eddy Truyen, Wouter Joosen	Elastic Cross-Layer Orchestration of Network Policies in the Kubernetes Stack	2025	Early Access	Security Containers Virtual machines	Packaging applications in Containers, dynamically managed using a cluster orchestrator, is the de-facto approach for deployment of cloud-native applications. When Containers run inside Virtual Machines (VMs) to protect infrastructural assets, Network Policies at the Container layer and Security Groups at the VM layer provide complementary firewall mechanisms that strengthen defenses against lateral movement of attackers. However, least-privilege network policies at the Container layer may not always be consistent with statically defined, over-permissive Security Groups at the VM layer. This is especially a problem with low-latency configuration of Container networking solutions that requires every opened Container protocol, port and traffic direction also to be opened at the VM layer. In any post-exploitation scenario where attackers escape from within an already compromised or infected Container, such over-permissive Security Groups do not prevent the attacker from spreading across VMs to find powerful tokens for accessing the cluster orchestrator. In this paper, we introduce GrassHopper, a fast and dynamic cross-layer enforcement approach for Network Policies, which automatically generates Security Group configurations from dynamically verified Network Policies and Container scheduling decisions. Given the low-latency context, the design of GrassHopper must ensure that dynamically generated Security Group rules come in a timely manner to effect before the newly scheduled Containers become ready to serve traffic. We evaluate the performance of GrassHopper on a Kubernetes cluster running on OpenStack at the network and application level. In comparison to a Security Group management approach that is not scheduling-aware, our findings show that for low-latency applications GrassHopper can reduce the network attack surface between VMs at a ratio of 78-to-99%, while causing no network performance overhead at the application level with respect to latency and throughput.	10.1109/TNSM.2025.3531040
Lihui Zhang, Gang Sun, Rulin Liu, Wei Quan, Hongfang Yu, Dusit Niyato	Priority-Dominated Traffic Scheduling Enabled ATS in Time-Sensitive Networking	2025	Early Access	Time-Sensitive Networking Traffic Scheduling Asynchronous Traffic Shaping	Time-Sensitive Networking (TSN) employs shaping mechanisms such as Time-Aware Shaping (TAS) and Cyclic Queuing and Forwarding (CQF), which depend heavily on precise time synchronization and complex Gate Control Lists (GCL) configurations, limiting their effectiveness in large-scale mixed traffic networks like those in vehicular systems. In response, IEEE 802.1Qcr protocol introduces the Asynchronous Traffic Shaping (ATS) mechanism, based on Urgency-Based Schedulers (UBS), to asynchronously address diverse traffic needs and ensure low and predictable latency. Nonetheless, no traffic scheduling algorithm exists that can be directly applied to ATS shapers in generic large-scale traffic scenarios to solve for fixed end-to-end (E2E) delay constraints and the number of priority queues. In this paper, we propose an urgency-based fast flow scheduling algorithm (UBFS) to address the issue. UBFS leverages domain-specific optimizing strategies with a focus on traffic delay urgency inspired by greedy algorithm for priority allocation across hops and flows, complemented by preprocessing for scenario solvability and dynamic verification to ensure scheduling feasibility. We benchmark UBFS against the method with both scalability and solution quality in typical network topology and demonstrate that UBFS achieves more rapid scheduling within seconds across linear, ring, and star topologies. Notably, UBFS significantly outperforms the baseline algorithm in scheduling efficiency in mixed and large-scale traffic environments, scheduling a larger number of flows. UBFS also reduces time costs by 2-10 times in delay-sensitive environments and by more than 10 times in large-scale scenarios, effectively balancing time efficiency, performance and scalability, thereby enhancing its applicability in real-world industrial settings.	10.1109/TNSM.2025.3532080
Tien Van Do, Nam H. Do, Csaba Rotter, T.V. Lakshman, Csaba Biro, T. Bérczes	Properties of Horizontal Pod Autoscaling Algorithms and Application for Scaling Cloud-Native Network Functions	2025	Early Access	Measurement Cloud computing Clustering algorithms	With the growing adoption of network function virtualization, telco core network elements and network functions will increasingly be designed and deployed as cloud-native application instances. To ensure the efficient use of virtualised resources and meet diverse requirements for quality of services a resource scaling algorithm is used to scale the the number of application instances up or down depending on variations in offered traffic from customers. Most of the observed performance metrics for a service are a function of the current customer traffic and the current number of application instances providing the service. The ubiquitous use of Kubernetes, the popular open-source framework for deployment and management of cloud-native functions, has resulted in variants of the Kubernetes Horizontal Pod Autoscaling (HPA) algorithm being widely used to change the number of application instances providing network functions as traffic demands vary. This change is done by determining whether a selected performance metric of interest is outside a range set by two input parameters (the desired metric value and the tolerance parameter). In this paper, we invesitigate the characteristics of the HPA algorithms and prove that there are only a finite number of intervals for its tolerance parametere. Further any choice of the tolerance parameter from each interval leads to similar computational decisions on the recommended number of application instances. As a consequence, the number of parameter setting choices is finite due to the rule that the desired metric value can only be an integer in specific ranges. Additionally, we investigate the use of HPA for scaling application instances that provide session-based services and establish lower and the upper bounds for performance of the HPA scaling algorithms in this scenario. Our contributions can help operators find appropriate parameter settings efficiently -administrators of Kubernetes clusters only need to select parameters from a limited and finite number of choices (instead of infinite) for scaling cloud-native applications.	10.1109/TNSM.2025.3532121
Tamás Lévai, Balázs Vass, Gábor Rétvári	Programmable Real-Time Scheduling of Disaggregated Network Functions: A Theoretical Model	2025	Early Access	Real-time systems Software Switches	Novel telecommunication systems build on a cloudified architecture running softwarized network services as disaggregated virtual network functions (VNFs) on commercial off-the-shelf (COTS) hardware to improve costs and flexibility. Given the stringent processing deadlines of modern applications, these systems are critically dependent on a closed-loop control algorithm to orchestrate the execution of the disaggregated components. At the moment, however, the formal model for implementing such real-time control loops is mostly missing. In this paper, we introduce a new real-time VNF execution environment that runs entirely on COTS hardware. First, we define a comprehensive formal model that enables us to reason about packet processing delays across disaggregated VNF processing chains analytically. Then we integrate the model into a gradient-optimization control algorithm to provide optimal scheduling for real-time infocommunication services in a programmable way. We present experimental evidence that our model gives a proper delay estimation on a real software switch. We evaluate our control algorithm on multiple representative use cases using a software switch simulator. Our results show the algorithm drives the system to a real-time capable state in just a few control periods even in case of complex services.	10.1109/TNSM.2025.3531989
Ahsan Raza Khan, Habib Ullah Manzoor, Rao Naveed Bin Rais, Sajjad Hussain, Lina Mohjazi, Muhammad Ali Imran, Ahmed Zoha	Semantic-Aware Federated Blockage Prediction (SFBP) in Vision-Aided Next-Generation Wireless Network	2025	Early Access	Sensors Wireless sensor networks Semantics	Predicting signal blockages in millimetre-wave and terahertz networks is essential for enabling proactive handover (PHO) and ensuring seamless connectivity. Existing approaches utilising deep learning, multi-modal vision and wireless sensing data primarily depend on centralised model training. Although these techniques are effective, they come with high communication costs, inefficient bandwidth usage, and latency issues, which restrict their real-time applicability. This paper proposes a Semantic-Aware Federated Blockage Prediction (SFBP) framework, leveraging the lightweight computer vision technique MobileNetV3 for edge-based semantic extraction, lowering communication and computation costs. Furthermore, we introduce a Similarity-Driven Federated Averaging (SD-FedAVG) mechanism to enhance the robustness of the model aggregation process, effectively mitigating the impact of noisy updates and adversarial attacks. Our proposed SFBP framework achieves 97.1% blockage prediction accuracy, closely matching centralised learning methods, while reducing communication costs by 88.75% compared to centralised learning and by 57.87% compared to FL without semantic extraction. Moreover, on-device inference reduces the latency by 23% compared to centralised learning and 18% compared to FL without semantic extraction, improving real-time decision-making for PHO. Additionally, the SD-FedAVG mechanism improves prediction accuracy under noisy conditions, directly impacting the PHO by reducing the handover failure rate by 7%.	10.1109/TNSM.2024.3525338
Hongping Gan, Hejie Zheng, Zhangfa Wu, Chunyan Ma, Jie Liu	TFD-Net: Transformer Deviation Network for Weakly Supervised Anomaly Detection	2024	Early Access	Anomaly detection Transformers Feature extraction	Deep Learning (DL)-based weakly supervised anomaly detection methods enhance the security and performance of communication and networks by promptly identifying and addressing anomalies within imbalanced samples, thus ensuring reliable communication and smooth network operations. However, existing DL-based methods often overly emphasize the local feature representations of samples, thereby neglecting the long-range dependencies and the prior knowledge of the samples, which imposes potential limitations on anomaly detection with a limited number of abnormal samples. To mitigate these challenges, we propose a Transformer deviation network for weakly supervised anomaly detection, called TFD-Net, which can effectively leverage the interdependencies and data priors of samples, yielding enhanced anomaly detection performance. Specifically, we first use a Transformer-based feature extraction module that proficiently captures the dependencies of global features in the samples. Subsequently, TFD-Net employs an anomaly score generation module to obtain corresponding anomaly scores. Finally, we introduce an innovative loss function for TFD-Net, named Transformer Deviation Loss Function (TFD-Loss), which can adequately incorporate prior knowledge of samples into the network training process, addressing the issue of imbalanced samples, and thereby enhancing the detection efficiency. Experimental results on public benchmark datasets demonstrate that TFD-Net substantially outperforms other DL-based methods in weakly supervised anomaly detection task.	10.1109/TNSM.2024.3485545
Reo Uneyama, Takehiro Sato, Eiji Oki	Flow Update Model Based on Probability Distribution of Migration Time in Software-defined Networks	2024	Early Access	Probability distribution Routing Packet loss	In a software-defined network (SDN), routes of packet flows need to be updated in situations such as maintenance and router replacement. Each flow is migrated from its old path to new path. The SDN update has an asynchronous nature; the time when the switches process commands by the controller varies depending on flows. Therefore, it is difficult to control an order of flow migrations, and packets can be lost by congestion. Existing models divide the time axis into rounds and assign migrations to these rounds. However, congestion caused by multiple migrations in the same round is uncontrollable. Based on the probability distribution of time required for each migration, congestion can occur. This paper proposes a flow update model which minimizes the expected amount of excessive traffic by shifting the probability distributions. The time axis is divided into time slots which are fine-grained than rounds, so that each probability distribution is shifted. The proposed model assigns the time when the controller injects a command of flow migration to time slots. The proposed model is formulated as an optimization problem to determine the command times to minimize the expected amount. This paper introduces two methods to compute the expected amount. This paper also introduces a two-stage scheduling scheme (2SS) that divides the optimization problem into two stages. 2SS suppresses the computation time from O(\|T\|\|F\|-1) to O(\|T\|\|F\|-1/2) at the cost of including at most 0.12% error. 2SS suppresses the amount of excessive traffic than an existing model by at most 71.2%.	10.1109/TNSM.2024.3485753