TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Huixiang Zhang, Faria Khandaker, Mahzabeen Emu	A Topology-Aware LLM-Augmented Digital Twin Framework for Scalable IoT Device Management	2026	Early Access	Internet of Things Topology Management	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
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
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
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
Hussein Fawaz, Jacopo Talpini, Marco Savi, Silvia Giordano, Omran Ayoub	Detecting Zero-Day Attacks via Reconstruction of Feature Influence and Model Uncertainty	2026	Early Access	Modeling Uncertainty Training	In practical Network Intrusion Detection System (NIDS) deployments, detecting anomalies is only the first step, while determining the exact nature of those anomalies is equally important. Commonly, anomalous traffic is forwarded to a supervised multiclass classifier trained to identify known attack categories. While effective for known threats, this step presents a significant limitation, as zero-day attacks can be misclassified as known attacks. Therefore, there is a need for approaches that go beyond standard classification and can reliably recognize when an input does not conform to any learned attack pattern, i.e., zero-day attacks. To tackle this problem, we propose a novel detection strategy that leverages per-instance feature importance scores from an explainable Artificial Intelligence (XAI) framework and prediction uncertainty estimates derived from an ensemble classifier. To evaluate our approach, we conduct extensive experiments using a leave-one-attack-out strategy across three benchmark datasets, CICIoT2023, NF–TON–IoT, and CIC–DDoS2019, and test performance under two underlying classifiers, namely XG-Boost and Random Forest, demonstrating the model-agnostic nature of our method. Experimental results show that our approach achieves best-case AUROC gains approaching 40% and F1-score improvements of up to 73%, while maintaining positive or near-neutral worst-case performance across datasets, highlighting the effectiveness and robustness of jointly modeling explanation-driven reconstruction error and predictive uncertainty for reliable zero-day threat identification.	10.1109/TNSM.2026.3731401
Yingjie Hu, Weiping Wang, Shigeng Zhang, Hong Song, Ziheng Huang, Song Guo	Dual-State Representation Learning for Multi-Granularity IoT Device Identification	2026	Early Access	Internet of Things Modeling Training	The rapid growth of IoT devices has increased demand for traffic-based network asset management and security monitoring. Most existing methods operate in closed-set settings and may misclassify unseen devices as known models or return only an unknown label. To address this problem, this paper proposes a multi-granularity IoT device identification method based on dual-state representation learning. Device identity is modeled at three levels: type, manufacturer, and model, retaining type and manufacturer information when the device model cannot be reliably identified. The method extracts statistical, sequence, and raw-byte features and learns sequence and byte embeddings from idle and behavior traffic. Self-supervised learning and contrastive learning are used to improve the discriminative ability of representations. A state-aware gating mechanism then dynamically fuses the dual-state embeddings. Multi-task classification heads and confidence thresholds are used to support joint identification and rejection. Experiments on three public datasets show over 98% accuracy for known-device identification. The method also achieves over 97% accuracy for type and manufacturer prediction on the unknown-model test set and over 95% rejection rate for unknown models. Online deployment achieves an average latency of 3.1 ms and a throughput of 322 samples/s, demonstrating practical potential in open network environments.	10.1109/TNSM.2026.3738728
Heng He, Qin Xu, Hai Yu, Lei Nie, Jianfeng Lu	LFNC: A Lightweight and Fine-Grained Two-Stage Network Flow Classification Framework with Programmable Data Planes	2026	Early Access	Fluid flow Planing Modeling	Flow classification is a crucial component of network intrusion detection systems. Existing approaches mainly fall into two categories: in-network classification and control-data plane collaborative classification. The former is constrained by the computing and memory resources of programmable switches, often sacrificing classification accuracy and efficiency. The latter requires transmitting large volumes of packets to the control plane, leading to high processing latency, excessive control-channel overhead, and limited flow coverage. To address these challenges, we propose LFNC, a Lightweight and Fine-grained two-stage Network flow Classification framework with programmable data planes. In the first stage, LFNC introduces a Decision Tree Segmentation (DTS) algorithm to train resource-aware models in the control plane. The trained DTS models are converted into switch-compatible matching rules and deployed in the data plane to perform line-rate binary classification for preliminary anomaly detection. In the second stage, LFNC employs a cuckoo filter together with dual circular queues to selectively buffer essential packet features of preliminarily anomalous flows in the data plane and efficiently transfer them to the control plane. A multi-class energy-based flow classifier is then applied in the control plane to achieve accurate and fine-grained classification of anomalous flows. Experimental results on the Tofino hardware switch demonstrate that LFNC outperforms eight state-of-the-art baselines, improving flow collection rate by 1.07% and classification accuracy by 3.34%, while significantly reducing hardware resource consumption and maintaining low packet processing latency.	10.1109/TNSM.2026.3738578
Hamidreza Mazandarani, Masoud Shokrnezhad, Tarik Taleb	A Semantic-Aware Multiple Access Scheme Leveraging Spatial Redundancy for Uplink-Dominant Network Services	2026	Early Access	Timing Titanium Modeling	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
Mubashir Murshed, Glaucio H. S. Carvalho, Robson E. De Grande	Holistic Intelligent Traffic Steering Management in Multi-RAT Vehicular Networks	2026	Early Access	Radio access technologies Rats Vehicles	Multiple Radio Access Technology (multi-RAT) environments provide a promising foundation for service-aware communication in intelligent transportation systems (ITS) and smart cities. However, traffic steering (TS) in highly mobile and ultra-dense vehicular networks remains challenging due to dynamic network conditions, heterogeneous RAT capabilities, varying vehicle requirements, packet loss, latency, and frequent ping-pong RAT switching. In this context, we propose Holistic Intelligent Traffic Steering (HITS), a proactive bi-level TS management framework for multi-RAT vehicular networks. HITS integrates centralized network-wide guidance with local vehicleside decision-making. At the central level, a Graph Convolutional Network–Long Short-Term Memory (GCN–LSTM) model captures holistic spatio-temporal network dynamics and evaluates RAT optimality. At the local level, a State-Action-Reward- State-Action (SARSA) reinforcement learning agent performs adaptive, vehicle-specific RAT selection using local observations and central-level optimality guidance. Results show that HITS achieves up to 6.5% higher average throughput, reduces packet loss ratio by more than 30.2%, lowers latency by nearly 12.2%, and reduces the ping-pong RAT switching rate by over 24% compared with baseline and state-of-the-art (SoTA) TS approaches.	10.1109/TNSM.2026.3729840
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 Mohamed	ZTCI: Zero-Touch Cooperative Immersion, a Plug-and-Deploy Deep Reinforcement Learning-Based Framework for Resource Allocation and Cooperation in the Metaverse	2026	Early Access	Resource management Modeling Metaverse	The Metaverse requires edge providers, termed Metaverse Service Stations (MSSs), to provision high-quality virtual environments (VEs) and faithful digital twins (DTs) for heterogeneous virtual venues under tight compute and network budgets. In multi-MSS deployments, localized demand surges can overwhelm one station while neighboring stations remain underutilized. Cooperation is therefore essential but challenging because venue requirements are heterogeneous, demand is bursty, and proximity-based neighborhood sets change over time. These dynamics motivate zero-touch operation that requires neither retraining nor reconfiguration at deployment. We introduce the General Optimized Agent (GOA), a plug-and- deploy Deep Reinforcement Learning (DRL) agent that jointly optimizes per-venue VE/DT service levels and inter- MSS resource sharing. GOA is trained through staged curriculum learning and uses a position-aware set encoder with learned positional embeddings and attention-based pooling to map variable-size neighbor sets to fixed-dimensional representations. This design yields a single policy that generalizes across MSS types, budget levels, and dynamic team sizes. Extensive evaluation shows that GOA improves request satisfaction, load balancing, and cost efficiency over representative baselines, supporting scalable, zero-touch Metaverse cooperation under realistic infrastructure constraints.	10.1109/TNSM.2026.3737187
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
Sheng-Shan Chen, Ren-Hung Hwang, Ying-Dar Lin, Tun-Wen Pai, Chin-Yu Sun	Extracting Attack Pattern from WAF Logs and CTIs Using Contrastive Semantic Learning	2026	Early Access	Modeling Payloads Cyber threat intelligence	Web Application Firewalls (WAFs) are widely deployed to protect web services, but their rule-based design provides limited visibility into attacker intent. WAF logs consist primarily of low-level HTTP artifacts that lack the behavioral context required for effective threat analysis. To address this limitation, we propose the first automated framework that mapsWAF logs to MITRE ATT&CK techniques through CTI-grounded semantic learning. The approach integrates structure-aware Monte Carlo Tree Search-based payload generation, CodeBERT-driven contrastive learning for attack classification, and cyber threat intelligence (CTI) alignment for TTP retrieval. The framework is evaluated on over 714,000 WAF logs derived from validated attack payloads across eight attack types, generated within a controlled environment using ModSecurity and OWASP Core Rule Set (CRS). Experimental results demonstrate 99.38% multi-class classification F1 score and identification of 206 unique ATT&CK techniques. Compared with a Rule-ID Heuristic baseline derived from OWASP CRS rule semantics, the proposed framework identifies 7.4× more unique ATT&CK techniques and provides substantially broader TTP-level visibility. External validation on a real-world ModSecurity log dataset further demonstrates that the framework preserves reliable classification and retrieval performance beyond the controlled payload-generation setting.	10.1109/TNSM.2026.3738730
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
Awais Bilal, Kashif Sharif, Liehuang Zhu, Fan Li, Chang Xu	SLA-Aware RSU-Edge Delegate Orchestration for IoV Consensus	2026	Early Access	Modeling Information rates Throughput	Ensuring reliable and timely consensus among Internet of Vehicles (IoV) nodes is critical for safety and operational efficiency, particularly under high mobility and dynamic network conditions. Traditional consensus protocols, however, do not explicitly incorporate service-level objectives (SLOs) such as commit latency, tail latency, or delegate-set diversity, limiting their applicability in real-world deployments. In this paper, we present a service-level agreement (SLA)-aware road-side unit (RSU)-edge orchestration framework for IoV consensus delegate selection, which leverages reinforcement learning (RL) to optimize committee composition while preserving quorum safety. Our approach embeds SLO metrics directly into the proximal policy optimization (PPO) reward function, enabling the RSU-edge to adapt delegate selection online under varying vehicle densities, speeds, and network conditions. A shortlist-based candidate reduction mechanism reduces computational overhead, while certificate-governed reconfiguration and state transfer support safe committee activation and recovery. Extensive simulations across multiple scenarios, including burst losses and mobility-induced churn, demonstrate that our method reduces median and tail commit latency, increases throughput, and maintains higher delegate-set diversity than baseline heuristics and the adapted BFTBrain-style service comparator. Under the simulator reference configuration, the modeled proposal-construction components yield a component-wise tail budget of 18.8 ms, excluding governance certification and state synchronization. The framework provides a practical blueprint for service-level-aware management of IoV consensus, bridging the gap between protocol-level designs and operational network management. Within the controlled service-level simulation scope, the study demonstrates the feasibility, robustness, and performance advantages of RL-driven RSU-edge orchestration. Packet-level and field deployment validation remain future work.	10.1109/TNSM.2026.3739347
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
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
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
Mohamed Zalat, Chris Barber, Babak Esfandiari, Thomas Kunz	A Reusable Network Digital Twin Architecture for QoS-Centric Network Management	2026	Early Access	Modeling Fluid flow Optimization	We propose a network digital twin approach for Quality of Service (QoS)-centric network management and demonstrate it on multiple network management problems. Our network digital twin involves running many ”what-if?” network configurations using a fast inference model for predicting network behavior, and applying the best configuration found based on the criteria of the network operator. We demonstrate the flexibility of this approach by applying it to 3 different network management problems: Interior Gateway Protocol (IGP) weight optimization, Border Gateway Protocol (BGP) route assignments, and gray fault detection and localization. We test our approach for each application on various OMNeT++ topologies and compare it to existing benchmarks in the respective literature. Our results indicate that the proposed network digital twin approach performs comparably to existing benchmarks in the network management problems explored and sometimes outperforms them in quality of service metrics.	10.1109/TNSM.2026.3737654
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