TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Kim Hammar, Neil Dhir, Rolf Stadler	Optimal Defender Strategies for CAGE-2 using Causal Modeling and Tree Search	2026	Early Access	Modeling Timing Trees (botanical)	The CAGE-2 challenge is considered a standard benchmark to compare methods for autonomous cyber defense. Current state-of-the-art methods evaluated against this benchmark are based on model-free (offline) deep reinforcement learning techniques, which do not provide provably optimal defender strategies. We address this limitation and present a formal (causal) model of CAGE-2 together with a method that converges to a provably optimal defender strategy, which we call causal partially observable Monte-Carlo planning (C-POMCP). Our method has two novel properties. First, it incorporates the causal structure of the target system through causal relationships among the system variables. This structure allows for a significant reduction of the search space of defender strategies. Second, it is an online method that updates the defender strategy at each time step via tree search. Evaluations against the CAGE-2 benchmark show that C-POMCP achieves state-of-the-art performance with respect to effectiveness and requires two orders of magnitude less computation than the closest competitor method.	10.1109/TNSM.2026.3735865
Messaoud Ait-Yahia, Wael Jaafar, Rami Langar	Joint Design of Blockchain-Enabled Service Placement and Task Assignment in Vehicular Fog Computing Networks	2026	Early Access	Delays Timing Optimization	Driven by the evolution of blockchain and fog computing, vehicular networks are increasingly capable of supporting latency-sensitive applications with enhanced security and trust guarantees. However, the joint resource allocation for task offloading and blockchain services has been insufficiently investigated in existing works. To address this gap, this paper proposes a framework for jointly allocating resources of blockchain, users’ virtualized services, and Mobile Edge Computing (MEC) task assignment in Vehicular Fog Computing (VFC) networks. Specifically, we formulate the optimization problem as an integer nonlinear programming model aiming to maximize the satisfaction rate of users’ service requests while minimizing the corresponding blockchain operation time under mobility, queuing, instantiation, and resource constraints. To solve it in a timely manner, we design two-stage hierarchical low-complexity solutions, namely a Particle Swarm Optimization-based Joint Blockchain-enabled Service placement and Task Assignment algorithm (PSO-JBSTA), and a Genetic Algorithm-based approach (GA-JBSTA). Through extensive simulations, we demonstrate the effectiveness of PSO-JBSTA (resp. GA-JBSTA) and their adaptability to network conditions, achieving an average 35% (resp. 24%) improvement in users’ service satisfaction rate and 9.5% (resp. 10.2%) reduction in average blockchain validation delay compared with the baselines.	10.1109/TNSM.2026.3737068
Serkut Ayvaşık, Alba Jano, Fidan Mehmeti, Wolfgang Kellerer	Sentinel: Vision-Based Signaling-Free SNR Prediction for Proactive 5G Resource Management	2026	Early Access	Resource management 5G mobile communication Convolutional neural networks	Reliable and efficient radio resource management in 5G systems critically depends on accurate Channel State Information (CSI) availability at the base stations. Traditionally, base stations perform scheduling, resource allocation, and link adaptation using the Channel Quality Indicator (CQI), either computed directly for uplink or obtained via CSI feedback reports for downlink. Both uplink and downlink procedures rely on frequent pilot and feedback transmissions, introducing significant overhead that challenges scalability and ultra-reliable communication demands. In this work, we introduce Sentinel, a vision-based machine learning system that leverages grayscale image sequences from an indoor environment to predict the SNR between user equipment and base station with a foresight window of 200 ms. Sentinel’s SNR prediction enables flexible CQI acquisition, allowing different SNR-to-CQI mappings without modifying the system, and eliminates the need for CQI-related pilot or feedback signaling. The proposed system is evaluated in a dynamic multi-user scenario comprising three heterogeneous 5QI service profiles across 40 users. Sentinel demonstrates superior CQI prediction performance, achieving substantial to near-perfect agreement with true CQI labels, as measured by the quadratic weighted kappa, and outperforming benchmark foresight-based CQI prediction models in both CQI classification and resource management effectiveness. Proactive resource management evaluations show that Sentinel meets the strict reliability targets of mission-critical 5QI services, achieving packet error rates below 10−4, and approaching 10−5 when integrated with signaling. Furthermore, Sentinel reduces total radio resource usage by up to 24% in the 40-user scenario by eliminating CQI-related signaling overhead.	10.1109/TNSM.2026.3735937
Marco Garofalo, Luca D’Agati, Laura García, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro, Antonio Puliafito, Giovanni Merlino, Francesco Longo	Trustless SLA Enforcement and Roaming in LoRaWAN through Smart Contracts	2026	Early Access	Roaming Service level agreements LoRaWAN	LoRaWAN is widely used for Internet of Things (IoT) services that require long-range, low-power wireless connectivity. As deployments grow, roaming between different network operators becomes increasingly important to maintain service continuity for mobile IoT devices. In practice, however, roaming still depends on bilateral agreements and trusted intermediaries, which limit scalability and reduce transparency in multi-operator settings. This work introduces a blockchain-based roaming architecture that uses Algorand smart contracts to automate Service Level Agreement (SLA) management between providers. The system supports dynamic roaming agreements, immutable packet accounting, and transparent settlement. In our system, the enforced guarantee concerns forwarding-level service quality at the roaming interface, namely payment conditional on observed delivery ratio, rather than deterministic radio-layer latency or jitter guarantees. We implemented the full infrastructure, including a custom Gateway Bridge that extracts the Network Identifier (NetID), a blockchain service that interacts with Algorand smart contracts for SLA validation, and a decentralized provider catalog for operator discovery. We evaluated the system on a testbed with production-grade ChirpStack network servers and compared it with our previous non-blockchain implementation. Both versions achieve comparable throughput (5800–5900 packets/minute with 1000 devices) and maintain 99% packet forwarding efficiency. Blockchain integration adds measurable overhead, including a forwarding latency overhead in the 400–490 ms range for SLA validation, largely independent of the underlying network delay, but remains acceptable for delay-tolerant IoT services. Overall, the results show that decentralized LoRaWAN roaming can be implemented without breaking compatibility with existing network architectures.	10.1109/TNSM.2026.3734694
José Antonio Pastor Valera, Martin Husák, Jesús García Rodríguez, Jorge Bernal Bernabé, Antonio Skarmeta	Real Time Cyber Situational Awareness for 6G Networks Leveraging Spatial Metrics	2026	Early Access	Modeling Timing Fluid flow	The dynamic and heterogeneous nature of 6G networks demands continuous, real-time cyber situational awareness (CSA) to support cognitive security operations such as behavior analysis, threat hunting, and adaptive defense. Traditional CSA frameworks like CRUSOE capture structural and mission-level data but cannot process the dynamic, high-frequency telemetry typical of 6G environments. This paper presents the Extended Infrastructure and Service Information Model (EISIM) and its implementation within a Cyber Situational Awareness Platform (CSAP) designed for real-time, context-aware security management. CSAP aggregates and models data from diverse sources into a unified graph representing assets, flows, services, and vulnerabilities, enabling continuous assessment of operational and security posture. The platform introduces novel spatial risk metrics, including Flow Load Centrality and Operational Risk Centrality, which integrate topological position, traffic intensity, vulnerability exposure, and resource capacity to quantify risk, exposure, and criticality. By combining workflow-driven data collection with real-time spatial analytics, CSAP enhances situational awareness and decision-making within 6G Security Operation Centers (6G-SOCs), enabling proactive identification of critical network elements and improved resilience through cognitive, data-driven management.	10.1109/TNSM.2026.3734629
Xiaodi Wang, Yunwei Dong, Weizhi Meng, Meng Li, Yining Liu	Dropout-Tolerant Privacy-Preserving Aggregation for Federated Mobile Crowdsensing	2026	Early Access	Modeling Privacy Internet of Things	Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in heterogeneous environments. Although asynchronous FL methods have been explored to alleviate this, they often introduce the critical issue of stale updates, which can degrade model convergence and accuracy. To simultaneously address the challenges of efficiency, staleness, and robustness, this paper proposes a novel Dropout-Tolerant Privacy Aggregation (DTPA) scheme for FL that operates without a trusted third party (TTP). Our solution leverages the distributed decryption feature of the lifted EC-ElGamal cryptosystem to enable secure, decentralized model aggregation. We further introduce an efficient worker selection algorithm to systematically reduce waiting time. Moreover, a dedicated dropout-tolerant mechanism is developed to maintain protocol execution even under a high rate of client failures, thereby enhancing robustness. Security analysis confirms that our scheme fulfills essential privacy and security requirements. Extensive simulations demonstrate that the proposed DTPA scheme significantly improves training efficiency and convergence stability compared to state-of-the-art methods, while remaining practical for deployment on resource-constrained mobile devices.	10.1109/TNSM.2026.3732465
Muhammad Muhammad Bala, Abdullahi Uwaisu Muhammad, Kamaluddeen Ibrahim Yarima, Aseel Smerat, Mulikatu Yakubu Ibrahim, Safiyanu Yahaya, Hamza Adamu	Isolation and Optimization Cost of Service-based Radio Access Network Slicing: A Smart-Contract-Based Approach	2026	Early Access	Network slicing Smart contracts Radio access networks	The service-based Radio Access Network (RAN) slicing enabled via Software Defined Networking (SDN) and Network Function Virtualization (NFV) can support diverse service requirements and address the rapid data traffic growth from both the vertical industry and the Internet of Things (IoT). However, network slice isolation and resource sharing between slices should be be improved for future wireless network requirements. Firstly, this paper address the isolation enhancement of future wireless networks through Blockchain-Smart-Contract, by creating two smart-contract-based access control to secure access to different service-based RAN applications and secure the sharing of resources. These contract are Verification and Authorization Contract (VAC), as well as Misconduct and Revocation Contract (MRC). The proposed framework is designed to support key 6G service classes, such as enhanced Mobile Broadband (eMBB) and ultra-Reliable Low-Latency Communications (uRLLC), enabling high data rates and low-latency communication. Secondly, to ensure the servicebased RAN achieves better isolation the optimization goal is to minimize the deployment cost to obtain the best deployment scheme. Hence, we divide the service-based RAN slice isolation deployment problem into two sub-problems, i.e., service-based RAN slice isolation and slice deployment problem, by formulating a Mixed Integer Linear Programming (MILP) model to minimize the deployment cost. Finally, to verify the feasibility of the design implementation an experimental platform is built and the results show the architecture achieves isolation enhancement through smart-contract and reduces the deployment cost by 78% and improve the isolation performance by 93% compared to the Blockchain-enabled Network Slice (BcNS) and the service-based RAN.	10.1109/TNSM.2026.3732250
Vinícius Gruske Domeles, Laura Rodrigues Soares, Jéferson Campos Nobre, Edison Pignaton De Freitas	An Energy Cost-Benefit Analysis of Client-Side VPNs on CPE Devices	2026	Early Access	Energy Licenses Nuclear facility regulation	The reduction of CO2 emissions and conscientious use of energy resources is one of the biggest current challenges. Computer networks and the Internet are no exception to the global necessity of reassessing current energy consumption paradigms, and security mechanisms are some of the most costly in the networking stack. In the other hand, Customer-Premises Equipment (CPE) devices at the edge of the Internet structure play a significant role in service provisioning and securing the connection of the customer. As such, the impact of standard security tools on the energy consumption profile of these devices should be studied in depth. In this context, this work evaluates the energy cost-benefit of client-side Virtual Private Networks (VPNs) implemented on commercial CPE devices. Through experimental measurement and precise instrumentation, both energy consumption and network performance across different traffic profiles are analyzed. The main finding is that the use of VPNs can reduce the energy efficiency of the CPE per megabyte transferred by half, even under moderate load, highlighting a significant energy overhead imposed by security mechanisms on edge devices. Furthermore, the study shows that the most suitable protocol depends directly on scenario-specific requirements. Finally, the study proposes comparative metrics, a device-protocol calibrated model and presents the future directions for assessing the energy impact of Software-Defined Wide Area Network (SD-WAN) architectures.	10.1109/TNSM.2026.3733609
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Larisa-Mihaela Tufeanu, Marius-Constantin Vochin, Frank Y. Li	Residual Artifact Governance With ML-Enabled Garbage Collection Prediction in Kubernetes-Based 5G/6G Service-Based Architecture	2026	Early Access	Management Retrieval augmented generation Monitoring	The evolution of the service-based architecture (SBA) requires cloud-native core networks, and its open and modular design nature makes Kubernetes the most eminent platform for SBA implementation. One critical-yet-underexplored issue when deploying the SBA based on Kubernetes is the accumulation of residual artifacts that persist and degrade observability and stability inside Kubernetes clusters. In this paper, we propose residual artifact governance (RAG), a non-intrusive add-on module to the SBA that enforces bounded garbage collection (GC) policies without modifying the underlying architecture. The module follows a master–agent structure: a GC-master derives data-driven machine learning-enabled cleanup policies using the GC data provided by GC-agents, while GC-agents co-located at each network function execute deterministic, auditable cleanup actions. As a proof-of-concept demonstration, we implement a Kubernetes-based prototype to validate the operability and feasibility of RAG. While a machine learning predictor located at the GC-master provides early warning signals on time-to-collapse caused by the accumulation of residual artifacts, periodic fractional cleanup actions at GC-agents keep residual peaks bounded under both nominal and stress leak regimes. Results reveal that the accumulation of residual artifacts can be measured, predicted, and controlled using Kubernetes-native mechanisms through our RAG enhancement while remaining compatible with 3GPP-aligned observability and stability assurance principles.	10.1109/TNSM.2026.3734448
Jianer Zhou, Xinyi Qiu, Zhenyu Li, Gareth Tyson, Encheng Yu, Weichao Li, Heng Pan, Xinyi Zhang, Zhiwei Xu	Themis: An Adjustable Congestion Control Framework for Improving Video QoE	2026	Early Access	Quality of experience Videos Fluid flow	Optimizing congestion control algorithms (CCAs) has the potential to enhance video quality of experience (QoE). The goal of this work is to devise a congestion control framework that (i) ensures that individual users enjoy high video QoE, while (ii) minimizing variance, such that QoE is fairly distributed across all users, especially in fluctuating network, such as cellular network. We present Themis, a video-centric congestion control framework. Themis first uses a distributed approach to allocate a fair target QoE for each client. Based on this fair QoE, Themis then selects congestion control actions to optimize for video QoE (rather than throughput) based on application-layer signals provided by the client. Thus, rather than trying to maximize a flow’s (fair) share of bandwidth, Themis optimizes a flow’s share of the QoE budget. We evaluate Themis in both emulated and production networks. We show that in cellular network Themis achieves a 12.4% QoE improvement compared with BBR, and 37.1% QoE standard deviation decrease compared with the state-of-the-art, Minerva.	10.1109/TNSM.2026.3732350
Nilesh Chakraborty, Petar Djukic, Burak Kantarci	Aggressive-YoYo: Exploiting Intent-Semantic Misalignment in AI-Native 6G Management Planes	2026	Early Access	Central Processing Unit Management Modeling	Intent-Based Networking (IBN) enables operators to express high-level service objectives that are automatically translated into low-level control and orchestration policies. In AI-native 6G management planes, semantic misalignment during this translation can induce unsafe configurations that amplify conventional resource-exhaustion attacks. We investigate this vulnerability through aggressive-YoYo, a compound threat combining YoYo-style burst traffic with prematurely configured Kubernetes readiness probes. We implement an end-to-end Intent-to-Configuration pipeline that resolves natural-language service intents into structured policies, compiles them into Kubernetes probe settings, and evaluates the resulting behavior using a representative slice-assurance management function on Google Kubernetes Engine (GKE). Controlled readiness-delay experiments show that premature readiness can increase replica provisioning, aggregate CPU and memory consumption, storage activity, and request failures, while inducing non-trivial service-level tradeoffs. Similar resource amplification under a different N1-family machine type and deployment zone indicates that the effect is not specific to a single configuration.We further analyze readiness misconfiguration across multiple Kubernetes scaling mechanisms and derive service-specific safe and amplifying configuration regions. From the detection perspective, we show-case that aggressive-YoYo is detectable using fully supervised temporal classifiers evaluated with cycle-disjoint testing and feature-set ablation; the best configuration achieves an average accuracy of 92.6%. Under scarce aggressive-YoYo supervision, i.e., limited exposure to aggressive-YoYo traces, the supervised approach improves detection over the one-class setting. These results show that intent-semantic misalignment creates measurable cross-layer management risks and motivate semantic validation and telemetry-aware monitoring for trustworthy AI-native 6G orchestration.	10.1109/TNSM.2026.3736983
Amr Aboeleneen, Mohamed Abdallah, Aiman Erbad, Amr Salem	CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing service-profile provisioning and budget-credit sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic budget support. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC’s resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.	10.1109/TNSM.2026.3737119
Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb	A Semantic-Aware Multiple Access Scheme Leveraging Spatial Redundancy for Uplink-Dominant Network Services	2026	Early Access		The transition toward semantic-aware communication offers a paradigm shift for next-generation mobile networks, promising to decouple information significance from raw data transmission. Despite advances in semantic extraction, the integration of semantic intelligence into the Medium Access Control (MAC) layer remains underexplored, particularly in exploiting spatial correlations among users. To address this, we introduce a novel multiple access scheme designed for uplink-dominant network services. This framework optimizes the trade-off between spectrum utilization and sustainability by formulating variable-packet-length access as distinct α-fairness and energy efficiency problems. A key innovation of our approach is the quantification of spatial redundancies through novel metrics of self-throughput and assisted-throughput, which account for the semantic correlation of data across user equipment. We analyze these formulations to identify optimal bounds before proposing PRISM (Protocol for Redundancy Identification in Semantic Multiple-access). Grounded in Model-free Multi-Agent Deep Reinforcement Learning (MADRL), PRISM enables devices to autonomously govern spectrum access using only local observations. Extensive evaluations demonstrate that PRISM successfully leverages redundancies to outperform semantic-oblivious schemes, achieving up to 90% of the centralized optimal benchmark and improving both objectives by up to 2× across diverse user-semantic association matrices. These results validate PRISM as a viable candidate for future distributed mobile network applications, complemented by orthogonal Multiple Access Schemes where signals are multiplexed in the semantic domain.	10.1109/TNSM.2026.3737571
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Martine S. Lenders, Carsten Bormann, Thomas C. Schmidt, Matthias Wählisch	A Leaner and Faster Web: How CBOR Can Improve Dynamic Content Encoding in JSON and DNS over HTTPS	2026	Early Access	Internet of Things Encoding Internet	The Internet community has taken major efforts to decrease latency on the World Wide Web with significant improvements in accelerating content transport and in compressing static content. Less attention, however, has been dedicated to compression of dynamic content. Such content is commonly provided by JSON and DNS over HTTPS. Dynamic content objects continue to grow in size, which increases latency and fosters the digital inequality. In this paper, we propose to mitigate this increase by utilizing Concise Binary Object Representation (CBOR), a standard originally designed for the constrained Internet of Things (IoT) to restrict packet sizes and enable efficient encoding of data objects. We provide protocol design and three new data sets for the evaluation of dynamic content, DNS, and the loading of websites. Our key findings are the following: (i) Switching the data representation from JSON to CBOR reduces data by up to 80%. This size reduction can decrease loading times by up to 13.8% when downloading large objects—even in local setups. (ii) Enabling CBOR for DNS over HTTPS (DoH) and DNS over CoAP (DoC) reduces packet sizes significantly. Compressing only names combined with unpacked CBOR achieves maximum gain of 52.2%, using more complex but still lightweight Packed CBOR allows minimizing packets by up to 95.5%. Our lean decoder for name compression can fit into as little as 314 bytes of build size. Our results clearly show the potential of CBOR outside of IoT scenarios. Parts of this research have already influenced work within the IETF.	10.1109/TNSM.2026.3722114
Jing Zhang, Chao Luo, Rui Shao	MTG-GAN: A Masked Temporal Graph Generative Adversarial Network for Cross-Domain System Log Anomaly Detection	2026	Early Access	Anomaly detection Adaptation models Generative adversarial networks	Anomaly detection of system logs is crucial for the service management of large-scale information systems. Nowadays, log anomaly detection faces two main challenges: 1) capturing evolving temporal dependencies between log events to adaptively tackle with emerging anomaly patterns, 2) and maintaining high detection capabilities across varies data distributions. Existing methods rely heavily on domain-specific data features, making it challenging to handle the heterogeneity and temporal dynamics of log data. This limitation restricts the deployment of anomaly detection systems in practical environments. In this article, a novel framework, Masked Temporal Graph Generative Adversarial Network (MTG-GAN), is proposed for both conventional and cross-domain log anomaly detection. The model enhances the detection capability for emerging abnormal patterns in system log data by introducing an adaptive masking mechanism that combines generative adversarial networks with graph contrastive learning. Additionally, MTG-GAN reduces dependency on specific data distribution and improves model generalization by using diffused graph adjacency information deriving from temporal relevance of event sequence, which can be conducive to improve cross-domain detection performance. Experimental results demonstrate that MTG-GAN outperforms existing methods on multiple real-world datasets in both conventional and cross-domain log anomaly detection.	10.1109/TNSM.2026.3654642
Deemah H. Tashman, Soumaya Cherkaoui	Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks	2026	Early Access	Reconfigurable intelligent surfaces Reliability Optimization	Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without harmful interference to primary users (PUs). To address unreliable direct SU links and energy constraints common in next-generation wireless networks, this work introduces an adaptive, energy-aware hybrid reconfigurable intelligent surface (RIS) for underlay multiple-input single-output (MISO) CRNs. Distinct from prior approaches relying on static RIS architectures, our proposed RIS dynamically alternates between passive and active operation modes in real time according to harvested energy availability. We also model our scenario under practical hardware impairments and cascaded fading channels. We formulate and solve a joint transmit beamforming and RIS phase optimization problem via the soft actor-critic (SAC) deep reinforcement learning (DRL) method, leveraging its robustness in continuous and highly dynamic environments. Notably, we conduct the first systematic study of reward poisoning attacks on DRL agents in RIS-enhanced CRNs, and propose a lightweight, real-time defense based on reward clipping and statistical anomaly filtering. Numerical results demonstrate that the SAC-based approach consistently outperforms established DRL base-lines, and that the dynamic hybrid RIS strikes a superior trade-off between throughput and energy consumption compared to fully passive and fully active alternatives. We further show the effectiveness of our defense in maintaining SU performance even under adversarial conditions. Our results advance the practical and secure deployment of RIS-assisted CRNs, and highlight crucial design insights for energy-constrained wireless systems.	10.1109/TNSM.2026.3660728