TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
Mohammad A. Massad, Abdallah Y. Alma’aitah, Hossam S. Hassanein	A Lightweight Elevation-Angle Prediction for LEO NTNs: Analytical Guarantees and Empirical Validation	2026	Early Access		In Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs), rapid satellite motion creates short visibility windows and fast link transitions; in dense urban canyons, these challenges become more critical, especially when many users may require tightly timed and coordinated handover decisions. This paper presents a lightweight real-time predictor based on a Nonlinear Harmonic Autoregressive (NHAR) model that combines autoregressive memory, nonlinear curvature correction, and harmonic terms to capture orbital and diurnal patterns. All parameters are estimated in closed form, enabling fast and interpretable deployment at the network edge. A variability-aware analysis further identifies when NHAR can outperform simpler lightweight baselines and provides practical guidance for estimation-window selection. Evaluated on a year-long STK-generated dataset for dense urban canyon scenarios, NHAR achieves a mean absolute error below 3°, maintains accuracy above 90% over the full 24-hour cycle and under varying orbital conditions, and requires sub-millisecond inference time on standard CPU hardware. These results demonstrate that NHAR is a practical lightweight predictor for proactive mobility support in future LEO NTN systems.	10.1109/TNSM.2026.3744447
Shyam Kumar Shrestha, Shiva Raj Pokhrel, Jonathan Kua	Adapting Large Language Models for TCP Fairness over Wi-Fi	2026	Early Access		Designing Transmission Control Protocol (TCP) Congestion Control Algorithms (CCAs) for heterogeneous Wi-Fi networks remains a challenge due to rapidly varying delay, loss, and contention dynamics. These effects often lead to CCA incompatibility, flow unfairness, and starvation among competing TCP flows. Although Deep Reinforcement Learning (DRL) has shown promise in mitigating these challenges, its slow convergence, high training costs, and limited generalization hinder practical deployments. In this paper, we propose TCP-LLM, a learning-augmented transport framework that leverages Large Language Models (LLMs) for compatibility-aware CCA selection. TCP-LLM encodes multivariate TCP time-series into token embeddings and performs single-step discrete CCA decisions through a lightweight LLM decision head. Parameter-efficient low-rank adaptation (LoRA) reduces the number of trainable parameters by approximately two orders of magnitude. Evaluation using traces collected from a physicalWi-Fi testbed with competing TCP Cubic, Bottleneck Bandwidth and Round-trip propagation time (BBR), and Performance-oriented Congestion Control (PCC) flows shows that TCP-LLM achieves the highest mean, geometric-mean, and minimum per-flow throughput among the evaluated schemes. TCP-LLM also exhibits faster and more stable training convergence and supports low-latency bounded inference. These results demonstrate the feasibility of LLM-based sequence adaptation for compatibility-aware transport-layer supervision under heterogeneous wireless conditions.	10.1109/TNSM.2026.3739364
Taylor Henderson, Eric Osterweil, Pavan Dinesh, Robert Simon	Scalable and Robust User Deobfuscation using Pattern of Life	2026	Early Access		Online message boards have become increasing popular with cyber criminals and those wishing to conduct illegal acts. They are used in coordinating drug/human trafficking, terrorist activity, and distributing information from data breaches to name a few. We provide a novel method for deobfuscating message board users: Deobfuscating Using Patterns of Life (DUPOL). DUPOL uses a novel technique to take advantage of system artifacts arising from the proliferation of Public DNS. DUPOL is validated on a test bed which accurately maps the Internet to different information scopes. This provides an understanding of how DUPOL would perform if it was performed by different entities on the Internet. DUPOL is shown to be able to correctly identify message board users even when users employ privacy preserving technologies using only observable network traffic and without the need to decrypt any information. Additionally, DUPOL is shown to be robust to missing data and scalable using a novel decomposition of the NCC which removes the need for pairwise comparison.	10.1109/TNSM.2026.3744089
Qaisar Ali, Ihsan Ullah, Youn-Hee Han	Topology-Preserving Virtual Network Embedding Using Persistent Homology and Graph Attention Reinforcement Learning	2026	Early Access	Substrates Topology Joining processes	Virtual Network Embedding (VNE) maps virtual network requests (VNRs) onto shared substrate networks while satisfying node, link, and service constraints. Existing VNE methods mainly optimize resource-centric objectives such as CPU utilization, bandwidth consumption, acceptance ratio, and revenue, but often treat structural service patterns as implicit connectivity requirements. However, patterns such as redundancy loops, hierarchical aggregation, centralized control, and fault-tolerant structures may encode critical service logic, and their degradation can reduce reliability and violate service-level guarantees. This paper proposes TP-VNE, a topology-preserving and pattern-aware VNE framework that integrates persistent-homology-assisted structural characterization, graph attention-based representation learning, and proximal policy optimization. Persistent homology extracts multi-scale topological signatures from VNRs and substrate candidate regions, while complementary graph features distinguish acyclic structures such as stars and trees. A graph attention network learns topology-aware node embeddings by combining resource and structural features, and a PPO agent performs sequential node and link mapping under feasibility-aware action masking. Finally, a topology verification module validates the path-induced embedded subgraph before resource commitment. Simulation results show that TP-VNE improves the acceptance ratio from 48.5% to 83.5% at λarr = 0.20 and increases long-term average revenue from 2,780 to 4,980 compared with Baseline PPO, while achieving 89.3% pattern preservation accuracy with acceptable embedding overhead. Scalability experiments on GT-ITM substrates with up to 1000 nodes further demonstrate the robustness of TP-VNE under increasing network size.	10.1109/TNSM.2026.3744368
Di Wu, Jian Xu	LLM-LogHGC: Hypergraph Enhanced Unsupervised Log Parsing with Lightweight LLMs	2026	Early Access	Large language models Modeling Accuracy	Log parsing converts log messages into structured data, serving as a critical step in log analysis. Traditional log parsers rely on predefined rules or on trainable models that require labeled data. Although they are effective in particular settings, they exhibit limited generalizability and struggle to handle complex log data. Recent advances in large language models (LLMs) have enabled LLM-based log parsers to achieve outstanding performance. However, existing LLM-based parsers exhibit limited sensitivity to structural information, and pose privacy risks when using commercial models. Log data naturally exhibits pronounced structural patterns, and its intrinsic regularities implicitly encode log template information, enabling lightweight LLMs to parse it accurately. In this paper, we propose LLM-LogHGC, a log parsing framework enhanced with hypergraph structures, deployable on lightweight open-source LLMs. Without requiring manually labeled logs for contextual learning or fine-tuning, it leverages the hypergraph structure to capture latent relationships and similarities between logs to assist parsing. Specifically, LLM-LogHGC models each log message as a hyperedge, with the tokens in the message serving as nodes, thereby constructing the log hypergraph. Logs are preliminarily partitioned based on hyperedge connectivity. Subsequently, LLM-LogHGC uses a specially designed Masked Attention Hypergraph Convolutional Network to learn hyperedge embeddings and compute their similarities. Finally, similar log messages are input into the LLM for parsing. We introduce a template cache to reduce LLM queries and a structural verification mechanism to mitigate LLM-induced errors, improving parsing efficiency and accuracy. Comprehensive experiments on public datasets demonstrate that LLM-LogHGC outperforms state-of-the-art methods.	10.1109/TNSM.2026.3743629
Ara Cho, Yeram Kang, Hyesook Lim	A New Flow Table Update Method Using Invertible Bloom Filters in SDN-IoT and SDN-IoV Environments	2026	Early Access	Software defined networking Fluid flow Switches	Reducing the communication overhead associated with flow table updates is important for software-defined networking (SDN) in dynamic Internet of Things (IoT) and Internet of Vehicles (IoV) environments, where a large number of flow entries must be installed to accommodate diverse devices and traffic flows. However, the OpenFlow protocol updates resulting in considerable communication overhead between the controller and the switches when a large number of flow entries are added or modified. To address this limitation, we propose an invertible Bloom filter (IBF)-based differential flow table synchronization method that represents the updated flow table as a compact IBF and transmits it to the switch only once. The switch reconstructs the updated flow table by decoding a difference-IBF (d-IBF), which is generated from the received IBF and the IBF constructed from its own local flow table. This differential synchronization mechanism reduces both communication overhead and update latency while preserving correct flow table reconstruction. The comparative analysis shows that the proposed method successfully reconstructs updated flow tables and reduces the communication cost by up to 49.35% compared with the conventional OpenFlow update method when installing 50 flow entries. Decoding experiments show that the proposed method sustains a decoding success rate above 99.9% for flow tables ranging from 1,000 to 1,000,000 entries. Mininet-based evaluations using OpenFlow confirm that the proposed method reduces actual control-channel communication cost in both single-switch and multi-switch environments, achieving reductions of up to 36.1% and 33.3%, respectively. Furthermore, to demonstrate the hardware implementation feasibility of the proposed method, we implement a d-IBF decoding hardware accelerator on an FPGA-based switch platform.	10.1109/TNSM.2026.3743515
Tao Zhang	How to select blockchain applications for covert communication	2026	Early Access	Bitcoin Privacy Receivers	With characteristics like decentralization, transparency, anonymity and security, blockchain is an ideal platform for covert communication. How to select suitable blockchain platform is important to achieve secure and efficient covert communication with blockchain in practical scenarios. However, there is no systematical evaluation metrics currently. In the paper, we propose the first evaluation system with 10 key evaluation metrics from identity privacy, information stealthiness, robustness, performance and cost to systematically evaluate blockchain based covert communication schemes. Then different blockchain applications are compared with these metrics, comparative analysis shows that these blockchain applications have both advantages and disadvantages for covert communication. In the future, blockchain applications with improved privacy protection, lower latency and transaction fee can be explored in covert communication scenarios. Our work can lay the foundation and provide guidelines to assess blockchain based covert communication schemes and select blockchain applications suitable for covert communication.	10.1109/TNSM.2026.3742609
Ke He, Muhammad Rizwan Asghar, Jeong Do Yoo, Young Hun Lee, Hyunjae Kang, Huy Kang Kim, Dan Dongseong Kim	Liuer Mihou: A Practical Grey-box Adversarial Attack against Anomaly-based Network Intrusion Detection Systems	2026	Early Access	Modeling Signal detection Timing	Deep learning has gained popularity as a detection algorithm for Network-based Intrusion Detection Systems (NIDS) due to its expressiveness and speed. However, Deep learning algorithms are susceptible to adversarial attacks that introduce imperceptible changes to deceive the Deep learning algorithm into misclassifying the input. Existing adversarial attacks often overlook the practicality and replayability of the attack. In this paper, we present Liuer Mihou (LM), an adversarial attack designed to generate practical and replayable adversarial network packets, specifically targeting unsupervised anomaly-based NIDS deployed in Internet of Things (IoT) networks. The core concept behind LM is to leverage adversarial transferability and generate adversarial packets on a surrogate NIDS while considering predefined, semantic-preserving mutation operations to ensure practicality. To assess the effectiveness of LM, we objectively analyze its evasiveness against five general-purpose machine learning-based outlier detection algorithms (SOM, RRCF, LOF, OCSVM, and FROCC), as well as the state-of-the-art NIDS, Kitsune. Rather than claiming broad practicality, we characterize when and how packet-space attacks remain feasible: we quantify the trade-off between evasion and attack effectiveness, the distortion introduced by physical replay, and the conditions under which the attack transfers across detector families. We further show that LM’s mutation set is extensible beyond timing and size, adding TCP-window and payload-byte operators to evade a header-field-aware detector. Furthermore, we demonstrate that adversarial detectors such as Feature Squeezing and MagNet, as well as defenses designed for unsupervised anomaly detection, are largely ineffective in defending against LM. Moving beyond a theoretical setting, we conduct a replay of LM in two real IoT testbeds to analyze its practicality.	10.1109/TNSM.2026.3742885
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
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
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
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
Chengsheng Pan, Yingzhi Wang, Huaifeng Shi, Lishang Qin, Xiaosong Cui	DBSDD-AQM: Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management for Edge IoT Networks	2026	Early Access	Delays Satellite broadcasting Internet of Things	Bursty heterogeneous traffic can build persistent queues at edge IoT gateways when the offered load converges on a lower-rate bottleneck. Conventional active queue management (AQM) regulates packet dropping under a fixed buffer capacity, whereas adaptive buffer sizing alone does not coordinate capacity adjustment with queue-removal control. We propose Dynamic Buffer Sizing-Based Deep Deterministic Active Queue Management (DBSDD-AQM), which combines a bounded buffersizing loop with a faster continuous queue-removal controller. DBS adjusts the admissible capacity according to sustained queue evolution, while the DDPG Actor selects packet-granular removal actions for the resident backlog. A class-aware extension maps three concurrent service classes to logical FIFO queues sharing the same dynamic physical buffer. DBSDD-AQM is implemented using an NS3-PyTorch framework. Aggregate experiments compare it with RED, ARED, PIE, CoDel, and DQN-AQM, while the class-aware evaluation additionally includes FQ-CoDel and FQ-PIE. Matched-DBS comparisons show similar throughput but different delay–loss operating points among the aggregate controllers, and component ablation distinguishes the effects of capacity adaptation and learned queue removal. Relative to a fixed 50 KB buffer, DBS reduces average queueing delay by 16.23% for UDP and 17.70% for TCP Westwood in controlled transport experiments. In the evaluated three-class overload scenarios, all classes retain nonzero throughput. Class-aware DBSDD-AQM provides stronger D- and T-class differentiation, whereas FQ-CoDel and FQ-PIE produce more uniform class-level outcomes and stronger R-class performance. The measured 95th-percentile batch-one Actor inference latency is 0.109 ms on the reported x86-64 platform.	10.1109/TNSM.2026.3741334
Michael Georgiades, Iacovos Ioannou, Hasan Islam, Ferheen Ayaz, Yun Hou, Kin-Hon Ho	Enhanced Vehicular Edge Offloading through SLA-Based Deep Reinforcement Learning and Risk-Aware Bandits	2026	Early Access	Service level agreements Modeling Timing	This paper investigates service-level agreement (SLA)-aware computation offloading in vehicular edge computing using two complementary online learners. SRM-FA-UCB combines staleness-aware transport exploration, contextual service-time prediction, empirical SLA screening, and selected-arm feedback. SLA-MA-PPO couples a state-dependent SLA action mask with a two-hidden-layer actor–critic, generalized advantage estimation, and cooperative KPI shaping. We evaluate both methods in an extended EdgeCloudSim environment with deterministic SUMO urban-grid mobility, V2X radio, congestion and handover effects, 200–1000 vehicles, and 10 paired replications per load. Among the six primary schemes, SRM-FA-UCB attains the lowest mean SLA violation, service time, and modeled energy with near-unity fairness. Additional controlled attribution with two simple screened heuristics and a screened FA-UCB control shows that the large improvement over conventional FA-UCB is primarily associated with the shared SLA-aware prediction and admissibility-screening stage; no independent endpoint advantage of the more complex post-screen SRM selector is established after Holm correction. SLA-MA-PPO significantly reduces SLA violations, mean service time, and modeled energy relative to PPO-GAE, while ablations identify action masking as its critical component. A controlled change-point experiment does not establish a transient benefit from the restless term. Code, configurations, seeds, and analysis scripts are provided through a public EdgeCloudSim research fork.	10.1109/TNSM.2026.3742669
Aymen Daoud, Md. Zoheb Hassan, Georges Kaddoum	SpectraTwin: Generative Intelligence-Powered Digital Twin for Dynamic Spectrum Awareness	2026	Early Access	Modeling Radio frequency Labeling	Spectrum monitoring and classification under evolving RF spectrum landscapes are key enablers of spectrum awareness for dynamic spectrum management and interference detection in 6G networks. Although artificial intelligence (AI) and machine learning (ML) techniques have shown promising performance in spectrum classification, most existing models are static, non-generative, and optimized for specific training environments. As a result, channel distribution shifts, model brittleness, and the scarcity of labeled RF data for fine-tuning AI/ML models lead to significant performance degradation in evolving RF spectrum landscapes. In this context, digital twin (DT) provides a promising approach by maintaining a virtual representation of the RF spectrum environment that supports what-if analysis, data generation, and iterative model updates. In this paper, we propose SpectraTwin, a multi-AI-enabled, adaptive, and self-evolving DT framework for multi-timescale spectrum classification under evolving RF spectrum states and incrementally increasing task complexity. SpectraTwin integrates three tightly coupled modules: (i) a Dynamic Mask Generative Adversarial Network (GAN) that serves as the virtual replica of the RF spectrum environment, synthesizing realistic, classand channel-conditioned spectrograms on demand to mitigate RF data scarcity; (ii) a continual learning-based spectrum classification model that incrementally adapts to evolving RF environments while preserving prior knowledge; and (iii) an active-learning-inspired orchestration module that decides when to adapt, which classes to synthesize, and which exemplars to retain, so that model updates are performed in response to detected change rather than on a fixed retraining schedule. By jointly combining generative, continual, and active learning, SpectraTwin enables robust spectrum awareness through (a) faithful emulation of RF spectrum evolution, (b) detection of domain distribution shifts, and (c) efficient incremental adaptation under non-stationary conditions while maintaining a favorable balance between accuracy and forgetting. Simulation results on both custom dynamic RF datasets and multiple benchmark datasets (consisting of simulated and real-world signal captures) show that SpectraTwin achieves superior adaptability and classification performance compared with existing approaches.	10.1109/TNSM.2026.3743810
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
Lei Liu, Yong Wang, Qian He, Junqi Chen, Yuming Liu, Baokang Zhao	Accelerating Full-Node Repair in Erasure-Coded Storage via Adaptive In-Network Aggregation	2026	Early Access	Maintenance engineering Bandwidth Codes	The erasure-coded data redundancy mechanism is extensively adopted in distributed storage systems to guarantee data reliability with low storage redundancy. However, recovering a failed node (full-node repair) inherently incurs high network overhead and latency. Existing full-node repair methods typically allocate tasks at block granularity, making it difficult to align each helper’s transmitted data volume with its available upload bandwidth. In addition, receiver-side download bottlenecks further limit repair throughput. To address these challenges, we propose FinRepair, a full-node repair framework that couples fine-grained task allocation with adaptive in-network aggregation (INA). FinRepair first formulates upload task allocation as a constrained max-min problem, and solves it with an iterative maximum flow-based algorithm, which determines the sub-block sizes and helper assignments. It then employs a bipartite graph-based placement strategy to scatter reconstructed blocks across storage nodes, and performs in-network aggregation for selected sub-blocks to satisfy each receiver’s download constraint. We establish a necessary and sufficient condition for upload allocations and prove the optimality of the selective in-network aggregation rule. We implement FinRepair with a Tofino programmable switch and 16 storage nodes. Experimental results show that FinRepair improves repair throughput by 16.85%–46.20% compared with state-of-the-art methods, while applying in-network aggregation to only 22.86%–32.43% of sub-blocks.	10.1109/TNSM.2026.3743621
Ashesh Gaur, Sridhar Radhakrishnan, Mohammed Atiquzzaman	Inverse Packet Classification: Reconstructing Behaviorally Equivalent Rule Sets from Observed Traffic	2026	Early Access	Merging Labeling Modeling	Network devices classify packets with ordered rule tables, but those tables are often inaccessible. Passive reconstruction can support behavior audits, misconfiguration analysis, and policy compliance without privileged device access. We define inverse packet classification as inferring a compact rule set from observed packet headers and device actions. The goal is observational equivalence on observed traffic, not recovery of the original configuration or priority order. The problem is difficult because traffic covers only part of the header space. Packet classifiers also use first-match semantics and structured prefix or interval predicates. We propose InversePC, which combines entropy-guided k-d-tree partitioning with greedy region merging. For a dominant default action, InversePC-HiB uses a hybrid split loss, exception-only merging, and a wildcard default rule. On synthetic classifiers, accuracy reaches 100% once coverage is sufficient. On native-resolution ClassBench filters, InversePC reaches 94.1% action agreement versus 88.3% for cross-validated CART on 1,000-rule sets. At 8-bit quantization, the two methods are statistically tied (98.1% versus 98.0%). In a well-covered 250-rule setting, InversePC uses 1.20× the original rule count versus 1.32× for CART. HiB is most useful for sparse, irregular default-permit ACLs, where it uses up to 2.5× fewer rules than InversePC at 99.8% accuracy. Optimized implementation makes native-resolution reconstruction practical in seconds.	10.1109/TNSM.2026.3744323
Nazmul Islam, Mohammad Zulkernine	CONTEX-T: Contextual Exploitation of Encrypted Traffic for Device Fingerprinting via Transformer Time-Frequency Analysis	2026	Early Access	Modeling Internet of Things Frequency	The rapid expansion of internet of things (IoT) devices has created a pervasive ecosystem where encrypted wireless communications serve as the primary privacy and security protection mechanism. While encryption effectively protects message content, contextual information from packet metadata and statistics inadvertently expose device identities. Various studies have exploited raw packet statistics and their visual representations for device fingerprinting and identification. However, these approaches remain confined to the spatial domain with limited feature representation. Therefore, this paper presents CONTEX-T, a novel framework that exploits device-level information from encrypted traffic metadata using temporal and spectral representation. CONTEX-T first transforms raw packet-length sequences into temporal and spectral representations and then utilizes vision transformers (ViTs) for device identification. We systematically evaluated multiple time-frequency (TF) decomposition techniques and transformer-based models across two heterogeneous, encrypted IoT traffic datasets. The experiments demonstrated that TF analysis provides new and rich feature representation, exposing device-discriminative structure that remains latent in spatial-domain representation. It also demonstrated that temporal-spectral signatures persist regardless of payload encryption, highlighting packet-length metadata as an exploitable side channel for IoT device fingerprinting.	10.1109/TNSM.2026.3743753