TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Wencheng Chen, Jun Wang, Jeng-Shyang Pan, R. Simon Sherratt, Jin Wang	Enhancing the Delegated Proof of Stake Consensus Mechanism for Secure and Efficient Data Storage in the Industrial Internet of Things	2026	Early Access	Industrial Internet of Things Security Games	The rapid advancement of Industry 5.0 has accelerated the adoption of the Industrial Internet of Things (IIoT). However, challenges such as data privacy breaches, malicious attacks, and the absence of trustworthy mechanisms continue to hinder its secure and efficient operation. To overcome these issues, this paper proposes an enhanced blockchain-based data storage framework and systematically improves the Delegated Proof of Stake (DPoS) consensus mechanism. A four-party evolutionary game model is developed, involving agent nodes, voting nodes, malicious nodes, and supervisory nodes, to comprehensively analyze the dynamic effects of key factors—including bribery intensity, malicious costs, supervision, and reputation mechanisms—on system stability. Furthermore, novel incentive and punishment strategies are introduced to foster node collaboration and suppress malicious behaviors. The simulation results show that the improved DPoS mechanism achieves significant enhancements across multiple performance dimensions. Under high-load conditions, the system increases transaction throughput by approximately 5%, reduces consensus latency, and maintains stable operation even as the network scale expands. In adversarial scenarios, the double-spending attack success rate decreases to about 2.6%, indicating strengthened security resilience. In addition, the convergence of strategy evolution is notably accelerated, enabling the system to reach cooperative and stable states more efficiently. These results demonstrate that the proposed mechanism effectively improves the efficiency, security, and dynamic stability of IIoT data storage systems, providing strong support for reliable operation in complex industrial environments.	10.1109/TNSM.2025.3650612
Jack Wilkie, Hanan Hindy, Craig Michie, Christos Tachtatzis, James Irvine, Robert Atkinson	A Novel Contrastive Loss for Zero-Day Network Intrusion Detection	2026	Early Access	Contrastive learning Anomaly detection Training	Machine learning has achieved state-of-the-art results in network intrusion detection; however, its performance significantly degrades when confronted by a new attack class— a zero-day attack. In simple terms, classical machine learning-based approaches are adept at identifying attack classes on which they have been previously trained, but struggle with those not included in their training data. One approach to addressing this shortcoming is to utilise anomaly detectors which train exclusively on benign data with the goal of generalising to all attack classes— both known and zero-day. However, this comes at the expense of a prohibitively high false positive rate. This work proposes a novel contrastive loss function which is able to maintain the advantages of other contrastive learning-based approaches (robustness to imbalanced data) but can also generalise to zero-day attacks. Unlike anomaly detectors, this model learns the distributions of benign traffic using both benign and known malign samples, i.e. other well-known attack classes (not including the zero-day class), and consequently, achieves significant performance improvements. The proposed approach is experimentally verified on the Lycos2017 dataset where it achieves an AUROC improvement of.000065 and.060883 over previous models in known and zero-day attack detection, respectively. Finally, the proposed method is extended to open-set recognition achieving OpenAUC improvements of.170883 over existing approaches.The implementation and experiments are open-sourced and available at: https://github.com/jackwilkie/CLOSR	10.1109/TNSM.2026.3652529
Pieter Moens, Bram Steenwinckel, Femke Ongenae, Bruno Volckaert, Sofie Van Hoecke	Towards Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs	2026	Early Access	Microservice architectures Monitoring Anomaly detection	Microservice applications are omnipresent due to their advantages, such as scalability, flexibility and consequentially resource cost efficiency. The loosely-coupled microservices can be easily added, replicated, updated and/or removed to address the changing workload. However, the distributed and dynamic nature of microservice architectures introduces a complexity with regard to monitoring and observability, which is paramount to ensure reliability, especially in critical domains. Anomaly detection has become an important tool to automate microservice monitoring and detect system failures. Nevertheless, state-of-the-art solutions assume the topology of the monitored application to remain static over time and fail to account for the dynamic changes the application, and the infrastructure it is deployed on, undergoes. This paper tackles these shortcomings by introducing a context-aware anomaly detection methodology using dynamic knowledge graphs to capture contextual features which describe the evolving state of the monitored system. Our methodology leverages resource and network monitoring to capture dependencies between microservices, and the infrastructure they are running on. In addition to the methodology for anomaly detection, this paper presents an open-source benchmark framework for context-aware anomaly detection that includes monitoring, fault injection and data collection. The evaluation on this benchmark shows that our methodology consistently outperforms the non-contextual baselines. These results underscore the importance of contextual awareness for robust anomaly detection in complex, topology-driven systems. Beyond these achieved improvements, our benchmark establishes a reproducible and extensible foundation for future research, facilitating the experimentation with broader ranges of models and a continued advancement in context-aware anomaly detection.	10.1109/TNSM.2026.3652304
Marco Polverini, Andrés García-López, Juan Luis Herrera, Santiago García-Gil, Francesco G. Lavacca, Antonio Cianfrani, Jaime Galán-Jiménez	Avoiding SDN Application Conflicts With Digital Twins: Design, Models and Proof of Concept	2026	Early Access	Digital twins Analytical models Routing	Software-Defined Networking (SDN) enables flexible and programmable control over network behavior through the deployment of multiple control applications. However, when these applications operate simultaneously, each pursuing different and potentially conflicting objectives, unexpected interactions may arise, leading to policy violations, performance degradation, or inefficient resource usage. This paper presents a Digital Twin (DT)-based framework for the early detection of such application-level conflicts. The proposed framework is lightweight, modular, and designed to be seamlessly integrated into real SDN controllers. It includes multiple DT models capturing different network aspects, including end-to-end delay, link congestion, reliability, and carbon emissions. A case study in a smart factory scenario demonstrates the framework’s ability to identify conflicts arising from coexisting applications with heterogeneous goals. The solution is validated through both simulation and proof-of-concept implementation tested in an emulated environment using Mininet. The performance evaluation shows that three out of four DT models achieve a precision above 90%, while the minimum recall across all models exceeds 84%. Moreover, the proof of concept confirms that what-if analyses can be executed in a few milliseconds, enabling timely and proactive conflict detection. These results demonstrate that the framework can accurately detect conflicts and deliver feedback fast enough to support timely network adaptation.	10.1109/TNSM.2026.3652800
Jian Ye, Lisi Mo, Gaolei Fei, Yunpeng Zhou, Ming Xian, Xuemeng Zhai, Guangmin Hu, Ming Liang	TopoKG: Infer Internet AS-Level Topology From Global Perspective	2026	Early Access	Business Topology Routing	Internet Autonomous System (AS) level topology includes AS topology structure and AS business relationships, describes the essence of Internet inter-domain routing, and is the basis for Internet operation and management research. Although the latest topology inference methods have made significant progress, those relying solely on local information struggle to eliminate inference errors caused by observation bias and data noise due to their lack of a global perspective. In contrast, we not only leverage local AS link features but also re-examine the hierarchical structure of Internet AS-level topology, proposing a novel inference method called topoKG. TopoKG introduces a knowledge graph to represent the relationships between different elements on a global scale and the business routing strategies of ASes at various tiers, which effectively reduces inference errors resulting from observation bias and data noise by incorporating a global perspective. First, we construct an Internet AS-level topology knowledge graph to represent relevant data, enabling us to better leverage the global perspective and uncover the complex relationships among multiple elements. Next, we employ knowledge graph meta paths to measure the similarity of AS business routing strategies and introduce this global perspective constraint to infer the AS business relationships and hierarchical structure iteratively. Additionally, we embed the entire knowledge graph upon completing the iteration and conduct knowledge inference to derive AS business relationships. This approach captures global features and more intricate relational patterns within the knowledge graph, further enhancing the accuracy of AS-level topology inference. Compared to the state-of-the-art methods, our approach achieves more accurate AS-level topology inference, reducing the average inference error across various AS link types by up to 1.2 to 4.4 times.	10.1109/TNSM.2026.3652956
Haoran Hu, Huazhi Lun, Ya Wang, Zhifeng Deng, Jiahao Li, Yuexiang Cao, Ying Liu, Heng Zhang, Jie Tang, Huicun Yu, Jiahua Wei, Xingyu Wang, Lei Shi	Effective Resource Scheduling Design for Concurrent Competing Requests in Quantum Networks	2026	Early Access	Purification Quantum networks Quantum entanglement	Quantum networks, as a pivotal platform to support numerous quantum applications, have the potential to far exceed traditional communication networks. Establishing end-to-end entanglement connections with guaranteed fidelity is a key prerequisite for realizing the functionality of quantum networks. Entanglement purification techniques are commonly used in the entanglement distribution process to provide end-to-end entanglement connections that meet the fidelity requirements. Since the purification operation sacrifices a certain amount of entanglement resources, it is critical and challenging to efficiently utilize the scarce entanglement resources in quantum networks with concurrent competing requests. To address this problem, we propose a novel demand-oriented resource scheduling (DRS) algorithm. Considering the overall network demand, DRS introduces a congestion factor to evaluate the resource demand of each link, and performs purification operations sequentially based on the congestion level of the links, thus avoiding the excessive consumption of entanglement resources of bottleneck links. Extensive simulation results show that the DRS algorithm can achieve higher network throughput with similar resource conversion rates compared to traditional resource allocation schemes. Our work provides a new scheme for the resource scheduling problem under concurrent competing requests, which can promote the further development of existing entanglement routing techniques.	10.1109/TNSM.2026.3651862
M. Gharbaoui, F. Sciarrone, M. Fontana, P. Castoldi, B. Martini	Assurance and Conflict Detection in Intent-Based Networking: A Comprehensive Survey and Insights on Standards and Open-Source Tools	2026	Early Access	Surveys Translation Bandwidth	Intent-Based Networking (IBN) enables operators to specify high-level outcomes while the system translates these intents into concrete policies and configurations. As IBN deployments grow in scale, heterogeneity and dynamicity, ensuring continuous alignment between network behavior and user objectives becomes both essential and increasingly difficult. This paper provides a technical survey of assurance and conflict detection techniques in IBN, with the goal of improving reliability, robustness, and policy compliance. We first position our survey with respect to existing work. We then review current assurance mechanisms, including the use of AI, machine learning, and real-time monitoring for validating intent fulfillment. We also examine conflict detection methods across the intent lifecycle, from capture to implementation. In addition, we outline relevant standardization efforts and open-source tools that support IBN adoption. Finally, we discuss key challenges, such as AI/ML integration, generalization, and scalability, and present a roadmap for future research aimed at strengthening robustness of IBN frameworks.	10.1109/TNSM.2026.3651896
Shagufta Henna, Upaka Rathnayake	Hypergraph Representation Learning-Based xApp for Traffic Steering in 6G O-RAN Closed-Loop Control	2026	Early Access		This paper addresses the challenges in resource allocation within disaggregated Radio Access Networks (RAN), particularly when dealing with Ultra-Reliable Low-Latency Communications (uRLLC), enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). Traditional traffic steering methods often overlook individual user demands and dynamic network conditions, while multi-connectivity further complicates resource management. To improve traffic steering, we introduce Tri-GNN-Sketch, a novel graph-based deep learning approach employing Tri-subgraph sampling to enhance link prediction in Open RAN (O-RAN) environments. Link prediction refers to accurately forecasting optimal connections between users and network resources using current and historical measurements. Tri-GNN-Sketch is trained on real-world 4G/5G RAN monitoring data. The model demonstrates robust performance across multiple metrics, including precision, recall, F1 score, and ROC-AUC, effectively modeling interfering nodes for accurate traffic steering. We further propose Tri-HyperGNN-Sketch, which extends the approach to hypergraph modeling, capturing higher-order multi-node relationships. Using link-level simulations based on Channel Quality Indicator (CQI)-to-modulation mappings and LTE transport block size specifications, we evaluate throughput and packet delay for Tri-HyperGNN-Sketch. Tri-HyperGNN-Sketch achieves an exceptional link prediction accuracy of 99.99% and improved network-level performance, including higher effective throughput and lower packet delay compared to Tri-GNN-Sketch (95.1%) and other hypergraph-based models such as HyperSAGE (91.6%) and HyperGCN (92.31%) for traffic steering in complex O-RAN deployments.	10.1109/TNSM.2026.3654534
Dongbin He, Aiqun Hu, Xiaochuan He	A Novel Reciprocal Signal Generating Method Based on Network Delay of Random Routing Protocols	2026	Early Access	Delays Routing protocols Internet of Things	The growing popularity of Internet of Things (IoT) devices raises significant challenges for secure key distribution in wide-area networks. Traditional solutions often face high deployment costs or distance limitations. This paper proposes a novel method that leverages the inherent reciprocity of Internet transmission delay to achieve lightweight symmetric keys distribution. The core of the method lies in the generation of reciprocal delay signals, and then in the enhancement of their randomness through randomized routing protocols and additional artificial delays. Moreover, an eavesdropping model is proposed to analyze single attackers, and a probabilistic framework is established to evaluate security limits against collusion attacks. Furthermore, error correction codes are implemented to allow the raw key to be directly used for encryption, eliminating additional communication overhead. Experimental results demonstrate a correlation coefficient of 0.97 for delay signals in a local area network (LAN), confirming strong reciprocity. On the public internet, the proposed randomness enhancement improves the entropy by 2 bits. Similarly, the correlation coefficient between signals obtained by an eavesdropper and the legitimate party in this wide-area environment ranges from 0.02 to 0.26, indicating that the method is resilient to eavesdropping. This work demonstrates the feasibility of utilizing public network characteristics for secure key distribution among wide-area terminals.	10.1109/TNSM.2026.3651520
Yilu Chen, Ye Wang, Ruonan Li, Yujia Xiao, Lichen Liu, Jinlong Li, Yan Jia, Zhaoquan Gu	TrafficAudio: Audio Representation for Lightweight Encrypted Traffic Classification in IoT	2026	Early Access	Feature extraction Cryptography Telecommunication traffic	Encrypted traffic classification has become a crucial task for network management and security with the widespread adoption of encrypted protocols across the Internet and the Internet of Things. However, existing methods often rely on discrete representations and complex models, which leads to incomplete feature extraction, limited fine-grained classification accuracy, and high computational costs. To this end, we propose TrafficAudio, a novel encrypted traffic classification method based on audio representation. TrafficAudio comprises three modules: audio representation generation (ARG), audio feature extraction (AFE), and spatiotemporal traffic classification (STC). Specifically, the ARG module first represents raw network traffic as audio to preserve temporal continuity of traffic. Then, the audio is processed by the AFE module to compute low-dimensional Mel-frequency cepstral coefficients (MFCC), encoding both temporal and spectral characteristics. Finally, spatiotemporal features are extracted from MFCC through a parallel architecture of one-dimensional convolutional neural network and bidirectional gated recurrent unit layers, enabling fine-grained traffic classification. Experiments on five public datasets across six classification tasks demonstrate that TrafficAudio consistently outperforms ten state-of-the-art baselines, achieving accuracies of 99.74%, 98.40%, 99.76%, 99.25%, 99.77%, and 99.74%. Furthermore, TrafficAudio significantly reduces computational complexity, achieving reductions of 86.88% in floating-point operations and 43.15% of model parameters over the best-performing baseline.	10.1109/TNSM.2026.3651599
Yeryeong Cho, Sungwon Yi, Soohyun Park	Joint Multi-Agent Reinforcement Learning and Message-Passing for Resilient Multi-UAV Networks	2026	Early Access	Servers Heuristic algorithms Autonomous aerial vehicles	This paper introduces a novel resilient algorithm designed for distributed unmanned aerial vehicles (UAVs) in dynamic and unreliable network environments. Initially, the UAVs should be trained via multi-agent reinforcement learning (MARL) for autonomous mission-critical operations and are fundamentally grounded by centralized training and decentralized execution (CTDE) using a centralized MARL server. In this situation, it is crucial to consider the case where several UAVs cannot receive CTDE-based MARL learning parameters for resilient operations in unreliable network conditions. To tackle this issue, a communication graph is used where its edges are established when two UAVs/nodes are communicable. Then, the edge-connected UAVs can share their training data if one of the UAVs cannot be connected to the CTDE-based MARL server under unreliable network conditions. Additionally, the edge cost considers power efficiency. Based on this given communication graph, message-passing is used for electing the UAVs that can provide their MARL learning parameters to their edge-connected peers. Lastly, performance evaluations demonstrate the superiority of our proposed algorithm in terms of power efficiency and resilient UAV task management, outperforming existing benchmark algorithms.	10.1109/TNSM.2025.3650697
Haftay Gebreslasie Abreha, Ilora Maity, Youssouf Drif, Christos Politis, Symeon Chatzinotas	Revenue-Aware Seamless Content Distribution in Satellite-Terrestrial Integrated Networks	2026	Vol. 23, Issue	Satellites Topology User experience	With the surging demand for data-intensive applications, ensuring seamless content delivery in Satellite-Terrestrial Integrated Networks (STINs) is crucial, especially for remote users. Dynamic Ad Insertion (DAI) enhances monetization and user experience, while Mobile Edge Computing (MEC) in STINs enables distributed content caching and ad insertion. However, satellite mobility and time-varying topologies cause service disruptions, while excessive or poorly placed ads risk user disengagement, impacting revenue. This paper proposes a novel framework that jointly addresses three challenges: (i) service continuity- and topology-aware content caching to adapt to STIN dynamics, (ii) Distributed DAI (D-DAI) that minimizes feeder link load and storage overhead by avoiding redundant ad-variant content storage through distributed ad stitching, and (iii) revenue-aware content distribution that explicitly models user disengagement due to ad overload to balance monetization and user satisfaction. We formulate the problem as two hierarchical Integer Linear Programming (ILP) optimizations: one content caching that aims to maximize cache hit rate and another optimizing content distribution with DAI to maximize revenue, minimize end-user costs, and enhance user experience. We develop greedy algorithms for fast initialization and a Binary Particle Swarm Optimization (BPSO)–based strategy for enhanced performance. Simulation results demonstrate that the proposed approach achieves over a 4.5% increase in revenue and reduces cache retrieval delay by more than 39% compared to the benchmark algorithms.	10.1109/TNSM.2025.3629810
Guiyun Liu, Hao Li, Lihao Xiong, Zhongwei Liang, Xiaojing Zhong	Attention-Model-Based Multiagent Reinforcement Learning for Combating Malware Propagation in Internet of Underwater Things	2026	Vol. 23, Issue	Malware Mathematical models Predictive models	Malware propagation in Internet of Underwater Things (IoUT) can disrupt stable communications among wireless devices. Timely control over its spread is beneficial for the stable operation of IoUT. Notably, the instability of the underwater environment causes the propagation effects of malware to vary continuously. Traditional control methods cannot quickly adapt to these abrupt changes. In recent years, the rapid development of reinforcement learning (RL) has significantly advanced control schemes. However, previous RL methods relied on long-term interactions to obtain a large amount of interaction data in order to form effective strategy. Given the particularity of underwater communication media, data collection for RL in IoUT is challenging. Therefore, improving sample efficiency has become a critical issue that current RL methods need to address urgently. The algorithm of Attention-Model-Based Multiagent Policy Optimization (AMBMPO) is proposed to achieve efficient use of data samples in this study. First, the algorithm employs an explicit prediction model to reduce the dependence on precise model. Secondly, an attention mechanism network is designed to capture high-dimensional state sequences, thereby reducing the compound errors during policy training. Finally, the proposed method is validated for optimal control problems and compared with verified benchmarks. The experimental results show that, compared with existing advanced RL algorithms, AMBMPO demonstrates significant advantages in sample efficiency and stability. This work effectively controls the spread of malware in underwater systems through an interactive evolution based approach. It provides a new implementation approach for ensuring the safety of underwater systems in deep-sea exploration and environmental monitoring applications.	10.1109/TNSM.2025.3628881
Huaide Liu, Fanqin Zhou, Yikun Zhao, Lei Feng, Zhixiang Yang, Yijing Lin, Wenjing Li	Autonomous Deployment of Aerial Base Station Without Network-Side Assistance in Emergency Scenarios Based on Multi-Agent Deep Reinforcement Learning	2026	Vol. 23, Issue	Heuristic algorithms Disasters Optimization	Aerial base station (AeBS) is a promising technology for providing wireless coverage to ground user equipment. Traditional methods of optimizing AeBS networks often rely on pre-known distribution models of ground user equipment. However, in practical scenarios such as natural disasters or temporary large-scale public events, the distribution of user clusters is often unknown, posing challenges for the deployment and application of AeBS. To adapt to complex and unknown user environments, this paper studies a method of estimating information from local to global and proposes a multi-agent AeBSs autonomous deployment algorithm based on deep reinforcement learning (DRL). This method attempts to dynamically deploy AeBS to autonomously identify hotspots by sensing user equipment signals without network-side assistance, providing a more comprehensive and intelligent solution for AeBS deployment. Simulation results indicate that our method effectively guides the autonomous deployment of AeBS in emergency scenarios, addressing the challenge of the lack of network-side assistance.	10.1109/TNSM.2025.3603875
Fátima Khan, Óscar Gil, Luis Diez, Elena Serna Santiago, Luis M. Contreras, Ramón Agüero	Evaluating Fronthaul Network Performance Under the O-RAN Paradigm: A Novel Methodology Based on Queuing Theory	2026	Vol. 23, Issue	Delays 5G mobile communication Open RAN	Open Radio Access Network (O-RAN) fosters a new networking paradigm that supports flexible and open architectures, while enabling the functional disaggregation of traditional, monolithic, base stations. Specifically, the Lower-Layer Split (LLS) option 7.2x divides the PHY layer into Low-PHY at Open - Radio Unit (O-RU) and High-PHY at Open - Distributed Unit (O-DU), leveraging the so-called open fronthaul network, which imposes stringent latency requirements. In this work, we introduce and thoroughly examine the time window relationships that define the latency budget allocated to the open fronthaul network. Additionally, we propose a novel methodology to analyze the delay experienced by a realistic traffic pattern over a spine-leaf topology, accounting for various O-DU locations and site pools. To address the increased traffic load introduced by the fronthaul, we present a theoretical model to characterize the average delay across different priority levels when open fronthaul traffic coexists with flows from other Radio Access Technologies (RATs). This model is then validated and broadened through ns-3 simulations. Our findings show the applicability of the proposed methodology, which could be exploited by network operators in the planning phase, evincing its suitability to obtain benchmark delay values, offering a reliable tool for performance evaluation in open fronthaul networks.	10.1109/TNSM.2025.3646845
Meixia Miao, Peihong Qiang, Siqi Zhao, Jiawei Li, Guohua Tian, Jianghong Wei	Verifiable Data Streaming Protocol Supporting Keyword Queries	2026	Vol. 23, Issue	Protocols Cloud computing Trees (botanical)	The rapid deployment of emerging networks, such as the Internet of Things and cloud computing, has generated massive amounts of data. Data streaming is significant among these various data types due to its widespread use in many critical applications, such as gene sequencing, network intrusion detection, and stock trading. On the other hand, the continuously increased size of data streaming makes it impractical to store and manage the data locally, especially for those resource-constrained devices. Outsourcing the data streaming to cloud servers provides an ideal solution to the above storage issue. However, this raises the problem of how to guarantee the integrity of the outsourced data, as cloud servers may maliciously modify the data. To this end, the primitive of verifiable data streaming (VDS) was introduced to preserve the integrity of the outsourced data streaming, enabling data users to ensure that queried data items, including the contents and corresponding positions, are correct. Despite many proposed VDS protocols, most can only use the position index to query outsourced data streaming. Consequently, they fail to fulfill the requirements of those practical applications that need keyword queries. For example, in the setting of network intrusion detection, the data analyst would like to query all access records from the same IP address. In this paper, we extend the original VDS protocol to support keyword queries, i.e., allowing data users to retrieve outsourced data items with particular keywords. Specifically, we use a prefix tree to maintain keywords and another chameleon authentication tree to store data items. The two trees are bound together with cryptographic query proofs, ensuring the consistency between the position index and keyword queries. The proposed VDS protocol, which supports keyword queries, is proven secure in the standard model and outperforms previous VDS protocols in terms of functionality. The experimental results indicate that our proposal is also efficient and practical.	10.1109/TNSM.2025.3629071
Monolina Dutta, Anoop Thomas, B. Sundar Rajan	Novel Delivery Algorithms for Decentralized Multi-Access Coded Caching Systems	2026	Vol. 23, Issue	Prefetching Servers Indexes	In this paper, we propose a multi-access coded caching system under decentralized setting tailored for Content Delivery Networks (CDNs). In this system, a central server hosts N files, each of size F bits, and serves $K\leq N$ users through a shared link. The network is equipped with c caches, each with a capacity of MF bits, distributed across the network, where each of the K users is connected to a random set of $r\leq c$ caches. Initially, we consider a model where each cache subset is accessed by an equal number of users. We introduce a novel content delivery algorithm for the central server, which allows us to derive a closed-form expression for the per user transmission rate. Using techniques from index coding, we prove the optimality of the proposed delivery scheme. Additionally, we extend the model to propose a more general and novel framework by allowing each subset of caches to serve an arbitrary number of users, thereby greatly enhancing the system’s flexibility and applicability. We also propose a new delivery algorithm tailored to this generalized setting and demonstrate its optimality under specific user-to-cache association scenarios. Numerical results demonstrate that, in a specific scenario where the user-to-cache associations do not satisfy the optimality conditions, the proposed generalized scheme shows improvement over the order-optimal state-of-the-art decentralized multi-access coded caching scheme for small cache sizes. Specifically, when approximately 25% of the content is stored at every cache, the proposed scheme achieves up to a 20% reduction in the per user transmission rate. Considering that both schemes serve an equal number of users, the observed improvements indicate a potential reduction in server bandwidth requirements, lower latency, and enhanced energy efficiency during content delivery.	10.1109/TNSM.2025.3629715
Hai Anh Tran, Nam-Thang Hoang	Towards Efficient and Adaptive Traffic Classification: A Knowledge Distillation-Based Personalized Federated Learning Framework	2026	Vol. 23, Issue	Adaptation models Training Federated learning	Traffic classification plays a crucial role in optimizing network management, enhancing security, and enabling intelligent resource allocation in distributed network systems. However, traditional Federated Learning (FL) approaches struggle with domain heterogeneity, as network traffic characteristics vary significantly across different domains due to diverse infrastructure, applications, and usage patterns. This results in degraded performance when applying a single global model across all domains. To overcome this challenge, we propose KD-PFL-TC, a Knowledge Distillation-based Personalized Federated Learning framework for Traffic Classification, aimed to balance global knowledge sharing with personalized model adaptation in heterogeneous network environments. Our approach leverages knowledge distillation to enable collaborative learning without directly sharing raw data, preserving privacy while mitigating the negative effects of domain shifts. Each domain refines its local model by integrating insights from a global model and peer domains while maintaining its unique traffic distribution. To further enhance performance, we introduce an adaptive distillation strategy that dynamically adjusts the influence of global, peer, and local knowledge based on the similarity between traffic distributions, ensuring optimal knowledge transfer designed to each domain’s characteristics. Extensive experiments on real-world traffic datasets show that KD–PFL–TC maintains 88.0% accuracy under high heterogeneity (vs. 75.0% for FedAvg) while reducing communication overhead by ~60%, delivering an efficient and robust solution for large-scale, heterogeneous networks.	10.1109/TNSM.2025.3629241
F. Busacca, L. Galluccio, S. Palazzo, A. Panebianco, R. Raftopoulos	Bandits Under the Waves: A Fully-Distributed Multi-Armed Bandit Framework for Modulation Adaptation in the Internet of Underwater Things	2026	Vol. 23, Issue	Throughput Scalability Training	Acoustic communications are the most exploited technology in the so-called Internet of Underwater Things (IoUT). UnderWater (UW) environments are often characterized by harsh propagation features, limited bandwidth, fast-varying channel conditions, and long propagation delay. On the other hand, IoUT nodes are usually battery-powered devices with limited processing capabilities. Accordingly, it is necessary to design optimization algorithms to address the challenging propagation features while balancing them with the limited device capabilities. To address the constraints of the nodes in energy and processing resources, it is crucial to adjust the transmission parameters based on the channel conditions while also developing communication procedures that are both lightweight and energy-efficient. In this work, we introduce a novel Multi-Player Multi-Armed Bandit (MP-MAB) framework for modulation adaptation in Multi-Hop IoUT Acoustic Networks. As opposed to widely used, computation-demanding Deep Reinforcement Learning (DRL) techniques, MP-MAB algorithms are simple and lightweight and allow to iteratively make decisions by selecting one among multiple choices, or arms. The framework is fully-distributed and is able to dynamically select the best modulation technique at each IoUT node by leveraging on high-level statistics (e.g., network throughput), without the need to exploit hard-to-extract channel features (e.g., channel state). We evaluate the performance of the proposed framework using the DESERT UW simulator and compare it with state-of-the-art centralized solutions based on Deep Reinforcement Learning (DRL) for cognitive and heterogeneous networks, namely DRL-MCS, DRL-AM, PPO, SAC, as well as with a multiple-agent, distributed version of the PPO. The results highlight that, despite its simplicity and fully-distributed nature, the proposed framework achieves superior performance in UW networks in terms of throughput, convergence speed, and energy efficiency. Compared to DRL-MCS and DRL-AM, our approach improves network throughput by up to 33% and 20%, respectively, and reduces energy consumption by up to 18% and 16%. When compared to PPO, SAC, and Multi-PPO, the proposed solution achieves up to 11%, 34%, and 38% higher throughput, and up to 7%, 17%, and 33% lower energy consumption, respectively.	10.1109/TNSM.2025.3629240
Leonardo Lo Schiavo, Genoveva García, Marco Gramaglia, Marco Fiore, Albert Banchs, Xavier Costa-Perez	The TES Framework: Joint Statistical Modeling and Machine Learning for Network KPI Forecasting	2026	Vol. 23, Issue	Predictive models Forecasting Time series analysis	The vision of intelligent networks capable of automatically configuring crucial parameters for tasks such as resource provisioning, anomaly detection or load balancing largely hinges upon efficient AI-based algorithms. Time series forecasting is a fundamental building block for network-oriented AI and current trends lean towards the systematic adoption of models based on deep learning approaches. In this paper, we pave the way for a different strategy for the design of predictors for mobile network environments, and we propose the Thresholded Exponential Smoothing (TES) framework, a hybrid Statistical Modeling and Deep Learning tool that allows for improving the performance of network Key Performance Indicator (KPI) forecasting. We adapt our framework to two state-of-the-art deep learning tools for time series forecasting, based on Recurrent Neural Networks and Transformer architectures. We experiment with TES by showcasing its superior support for three practical network management use cases, i.e., (i) anticipatory allocation of network resources, (ii) mobile traffic anomaly prediction, and (iii) mobile traffic load balancing. Our results, derived from traffic measurements collected in operational mobile networks, demonstrate that the TES framework can yield substantial performance gains over current state-of-the-art predictors in the applications considered.	10.1109/TNSM.2025.3628788