TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Junior Momo Ziazet, Brigitte Jaumard	Energy Efficient Placement of Logical Functionalities in 5G Networks	2026	Early Access	Energy Copper Modeling	Although 5G networks are more efficient in terms of power consumption to traffic ratio, efforts still need to be made to further increase energy efficiency not only for the radio part, but also with respect to the growing cloud component with edge servers. Consolidation of traffic workloads onto shared infrastructures is a key feature of cloud computing to reduce energy consumption, and logical functionality placement plays a key role in this regard. Here, in the cloud RAN context, we propose a unified and energy-aware logical placement of 5G E2E functionalities, i.e., distributed units (DUs), centralized units (CUs), and user plane functions (UPFs), together with traffic routing. The placement problem is formulated as a large-scale integer linear program and solved using a column generation-based decomposition technique, complemented by an efficient heuristic to ensure tractability and improved scalability. The model captures key network and cloud (compute) resources, jointly optimizing the placement of DU, CU, and UPF components, along with traffic routing, to minimize energy consumption while maintaining low latency and high Quality of Service (QoS). Numerical results, based on an open Montreal traffic dataset, demonstrate that the proposed column generation algorithm achieves near-optimal solutions, while the heuristic approach offers significantly better scalability with consistently strong performance. The proposed methods reduce energy consumption by up to 14% and maintain low-latency service delivery. Furthermore, the results highlight that static, peak-time-based placement strategies can lead to inefficiencies throughout the day, emphasizing the importance of accounting for broader temporal traffic patterns.	10.1109/TNSM.2026.3729149
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
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
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
Yao Xin, Yuqiao Luo, Shufan Cao, Chongwu Dong, Qingfeng Tan	HBT: A Hybrid Bidding Tree for High-Performance Packet Classification	2026	Early Access	Heterojunction bipolar transistors Trees (botanical) Vegetation	Traditional packet classification algorithms based on decision trees often rely on rule replication to increase lookup speed, which inevitably leads to memory explosion. Conversely, existing zero-replication methods frequently suffer from extreme tree depth and structural fragmentation. To address this dilemma, this paper proposes the Hybrid Bidding Tree (HBT), a high-performance architecture designed to enforce zero-replication while sustaining deterministic lookup throughput. First, HBT employs an Overlap-Aware Rule Decomposition (OARD) framework to proactively isolate topologically entangled rules, purifying the primary geometric space. Second, HBT introduces a dynamic competitive bidding mechanism for tree construction. At each node, a discrete bit-selection path and a continuous range-partitioning path compete to determine the optimal splitting strategy based on local geometric heterogeneity. Finally, to guarantee an O(N) memory boundary, unpartitionable residual rules are assigned to a single-level Onion-Peeling fallback structure, preserving linear memory growth while introducing additional sequential checks in the auxiliary path. Experimental evaluations on ClassBench-ng rulesets containing up to 256k rules demonstrate the efficacy of HBT. Compared with state-of-the-art algorithms such as PT-Tree and TupleTree, HBT achieves the highest lookup throughput across all twelve evaluated rulesets at both the 128k and 256k scales, while maintaining strong memory efficiency and highly competitive construction and update latencies.	10.1109/TNSM.2026.3734240
Abdul Samim, Attiq Ur Rehman, KyungHi Chang	Intelligent Handover Management for 6G LEO Satellite Constellations: A Predictive Multi-Agent PPO Approach	2026	Early Access	Satellites Handover Loading	The integration of Low Earth Orbit (LEO) satellite constellations into 6G networks promises ubiquitous connectivity, yet poses unprecedented challenges for handover management due to rapid orbital motion and dynamic channel conditions. Traditional reactive handover algorithms, designed for quasistatic terrestrial networks, fail to address the multi-dimensional optimization requirements of LEO systems where satellites move at velocities exceeding 7 km/s and user-satellite connections last only 2-4 minutes. This paper proposes a Predictive Multi-Agent Proximal Policy Optimization (PMA-PPO) framework for SNR-aware load-balanced handover management in dual-layer LEO satellite networks. The framework integrates three core components: Gated Recurrent Unit (GRU) networks for temporal forecasting of channel conditions and satellite loads, distributed PPO agents for autonomous handover decision-making, and a coordination mechanism that balances signal quality with load distribution. Through comprehensive simulations of a realistic dual-layer constellation, PMA-PPO achieves significant performance improvements: up to 77.7% reduction in handover failure rates, 74.06% reduction in satellite overload duration, 35.13% improvement in throughput fairness, and ping-pong handover rates consistently below the practical 5% threshold across all load conditions, compared to state-of-the-art base-line approaches. The proposed approach achieves polynomial computational complexity versus exponential cost for exhaustive optimization, making it suitable for real-time deployment in large-scale LEO constellations.	10.1109/TNSM.2026.3735527
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
Liwei Zhang, Tong Zhang, Xiaoqin Feng, Wenxue Wu, Hao Yang, Ping Liu, Yanying Ma, Fengyuan Ren	Leveraging Hot Standby Routing to Improve Reliability in TSN	2026	Early Access	Fluid flow Timing Joining processes	Time-Sensitive Networking (TSN) is widely deployed in industrial networks because it can provide deterministic transmission services for Time-Triggered (TT) flows. Link failures pose severe threats to the reliability of TT flows. Frame Replication and Elimination for Reliability (FRER) defined by IEEE 802.1 CB tolerates such failures by transmitting the same frames via disjoint paths, but this introduces excessive bandwidth overhead. To this end, we present a Hot Standby Routing (HSR) mechanism tailored for TSN to ensure the reliability of TT flows while minimizing bandwidth usage. Unlike FRER, HSR can locally reroute a single frame to achieve tolerance to link failures. Specifically, the primary and secondary paths are computed hop-by-hop for each TT flow and installed on the switches in the network. Under normal conditions, the secondary path is in a silent standby state. If the primary path fails, the affected TT flow will be seamlessly rerouted to the secondary path by the local switch for transmission. The simulation results show that HSR can provide highly reliable transmission for TT flows while significantly reducing bandwidth consumption. Furthermore, HSR exhibits stronger robustness in large-scale networks.	10.1109/TNSM.2026.3733170
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
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
Ahmed Rjiba, Hicham Lakhlef, Joachim Bruneau-Queyreix, Meriem Afif	Federated Learning in Fog Computing within IoT Environments: An up-to-date and comprehensive survey	2026	Early Access	Federated learning Internet of Things Edge computing	The Internet of Things (IoT) connects diverse, resource-constrained devices, driving innovation in domains such as healthcare, smart cities, and industrial automation. However, the exponential growth of IoT devices poses critical challenges in data processing, privacy, security, and latency. Fog Computing (FC) mitigates these issues by decentralizing computational resources, processing and storing data locally to enable low-latency, high-quality services. This makes FC an ideal platform for integrating Federated Learning (FL), a decentralized machine learning paradigm that trains models locally on IoT devices and shares only aggregated updates, preserving data privacy. Since its introduction, FL has garnered considerable attention for enabling privacy-preserving collaborative model training in distributed environments. The convergence of IoT, FC, and FL offers substantial opportunities to advance IoT system performance, but it also presents challenges in resource allocation, security, energy efficiency, computational complexity, and system heterogeneity. This survey provides a comprehensive and up-to-date analysis of the integration of FL and FC within IoT environments, exploring their synergies, challenges, and state-of-the-art advancements.We review critical aspects, including infrastructure enhancements, security mechanisms, and the emerging role of Digital Twin (DT) technology, which creates virtual replicas of IoT devices to optimize system efficiency and real-time performance. Through case studies in healthcare and smart cities, we highlight practical applications of FL-FC integration. We compare our work with existing surveys, highlight its specific focus on the FL-FC-IoT-DT convergence, and identify open challenges and future research directions toward secure, scalable, and intelligent IoT ecosystems.	10.1109/TNSM.2026.3731410
Bita Fatemipour, Zhe Zhang, Marc St-Hilaire	Adaptive Routing Optimization with Cost and Deadline Awareness Using Hierarchical Deep Reinforcement Learning	2026	Early Access	Costing Costs Routing	Timely and cost-efficient data transfers in large-scale networks remain challenging due to diverse topologies, non-uniform pricing models, and variable traffic demands. Existing literature often relies on multi-objective optimization, employing heuristic methods to reduce computational complexity; however, these approaches typically assume stable or predictable demand and struggle to scale effectively. Reinforcement Learning (RL) has been explored for its adaptability, yet many RL-based methods remain single-objective or topology-agnostic. This paper introduces CD-DRL, a hierarchical Deep RL framework that jointly optimizes transmission cost and deadline satisfaction, two objectives that often conflict in large-scale networks, through two cooperative agents. A routing agent, built on a Graph Neural Network, selects paths over a structured, multi-binary action space, enabling topology-aware routing across varying network scales and demand patterns. An adaptive tuning agent observes network state and recent performance to dynamically adjust the cost-deadline tradeoff to best fit current conditions. This hierarchical design allows CD-DRL to respond to dynamic network events such as congestion and bandwidth fluctuations, where no single fixed tradeoff remains optimal. We validate CD-DRL through extensive experiments on diverse backbone topologies and request distributions under static and time-varying network conditions. Compared with a state-of-the-art GNN-based RL method and traditional heuristics, CD-DRL improves the deadline-met ratio by up to 25% while maintaining competitive total cost and demonstrating strong scalability. Additionally, CD-DRL achieves faster execution time than mathematical optimization baselines, enabling high-throughput, latency-sensitive routing in dynamic environments.	10.1109/TNSM.2026.3731031
Stephen Jasina, Loqman Salamatian, Joshua Mathews, Scott Anderson, Paul Barford, Mark Crovella, Walter Willinger	Matisse: Visualizing Measured Internet Latencies as Manifolds	2026	Early Access	Manifolds Internet Measurement	Manifolds are complex topological spaces that can be used to represent datasets of real-world measurements. Visualizing such manifolds can help with illustrating their topological characteristics (e.g., curvature) and providing insights into important properties of the underlying data (e.g., anomalies in the measurements). In this paper, we describe a new methodology and system for generating and visualizing manifolds that are inferred from actual Internet latency measurements between different cities and are projected over a 2D Euclidean space (e.g., a geographic map). Our method leverages a series of graphs that capture critical information contained in the data, including well-defined locations (for vertices) and Ricci curvature information (for edges). Our visualization approach then generates a curved surface (manifold) in which (a) geographical locations of vertices are maintained and (b) the Ricci curvature values of the graph edges determine the curvature properties of the manifold. The resulting manifold highlights areas of critical connectivity and defines an instance of “Internet delay space” where latency measurements manifest as geodesics. We describe details of our method and its implementation in a tool, which we call Matisse, for generating, visualizing and manipulating manifolds projected onto a base map. We illustrate Matisse with three case studies: a simple example to demonstrate key concepts, and visualizations of the US and Europe public Internet to show Matisse’s utility.	10.1109/TNSM.2026.3730274
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
Kim Hammar, Rolf Stadler	Online Identification of IT Systems through Active Causal Learning	2026	Early Access		Identifying a causal model of an IT system is fundamental to many branches of systems engineering and operation. Such a model can be used to predict the effects of control actions, optimize operations, diagnose failures, detect intrusions, etc., which is central to achieving the longstanding goal of automating network and system management tasks. Traditionally, causal models have been designed and maintained by domain experts. This, however, proves increasingly challenging with the growing complexity and dynamism of modern IT systems. In this paper, we present the first principled method for online, data-driven identification of an IT system in the form of a causal model. The method, which we call active causal learning, estimates causal functions that capture the dependencies among system variables in an iterative fashion using Gaussian process regression based on system measurements, which are collected through a rollout-based intervention policy. We prove that this method is optimal in the Bayesian sense and that it produces effective interventions. Experimental validation on two testbeds shows that our method enables accurate identification of a causal system model while inducing low interference with system operations.	10.1109/TNSM.2026.3736090
Nilesh Chakraborty, Petar Djukic, Burak Kantarci	Aggressive-YoYo: Exploiting Intent-Semantic Misalignment in AI-Native 6G Management Planes	2026	Early Access		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
Huixiang Zhang, Faria Khandaker, Mahzabeen Emu	A Topology-Aware LLM-Augmented Digital Twin Framework for Scalable IoT Device Management	2026	Early Access		The growing scale and dynamic nature of Internet of Things (IoT) deployments demand management approaches that can maintain accurate system awareness. Existing large language models (LLMs) can reduce the interface burden of network management. However, without explicit grounding in the physical system state, they may generate nonexistent devices, incorrect topological relations, or non-executable management actions. To address this problem, this paper proposes a digital twin (DT) grounded LLM augmented management framework for IoT device management. The framework uses the DT as a structured state source, allowing the model to access topology consistent device, connection, and status information before generating management responses. A topology importance driven adapter training method, implemented through Hierarchical Importance Organizer (HIO), is further developed to encode hierarchical paths and critical nodes into training samples. We further characterize how grounded management degrades as the DT drifts from the physical topology, isolating the robustness contribution of topology-aware adaptation. Across 34,200 completed per-sample model outputs, including a 7,200-output main benchmark and a 27,000-output topology-drift sweep, HIO is evaluated against schema-only prompting, a base plus DT model, and a GenTwin-like adapter. On the 1,800-sample main benchmark, HIO achieves 0.869 Direct F1, improving over the GenTwin-like adapter by 3.3 points and over the base plus DT model by 29.1 points. HIO also improves Exact Match from 0.753 to 0.827. The gain is most pronounced in topology-sensitive impact analysis, where HIO improves Direct F1 from 0.784 to 0.918. HIO has positive gains in all nine topology–scale cells, with 95% confidence intervals excluding zero in seven cells. Under DT topology drift, HIO consistently outperforms the GenTwin-like adapter over δ ∈ [0, 0.20] and degrades more slowly, with Direct F1 degradation slopes of −0.157 versus −0.189.	10.1109/TNSM.2026.3736467
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		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
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
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