TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Mandar Datar, Mattia Merluzzi	Balancing Costs and Utilities in Future Networks via Market Equilibrium with Externalities	2026	Early Access	Modeling Energy Resource management	Today, wireless networks are shifting towards systems that also involve computing resources, distributed across edge and cloud facilities. As such, radio and computing aspects shall be balanced continuously, to maximize the utilities of Service Providers (SPs), users quality of experience and fairness, while guaranteeing energy and carbon footprint constraints among others. In this paper, we tackle the problem of communication and compute resource allocation under energy constraints, with multiple SPs competing to get their preferred resource bundle by spending a fictitious currency budget. We model the system as a Fisher market (FM), incorporating energy use and carbon output as market externalities. Building on this framework, we develop a low-complexity, market-equilibrium (ME) based solution that ensures high utility, meets energy constraints, and promotes fairness among providers. To make the proposed resource allocation scheme practically viable and scalable, we design an alternating direction method of multipliers (ADMM) based equilibrium learning algorithm that enables SPs to reach the ME in a decentralized fashion. Finally, we run numerical simulations to validate the effectiveness of the proposed allocation mechanism, also for a practical use case of edge image classification, as well as the convergence rates of the distributed algorithm when scaling the number of players.	10.1109/TNSM.2026.3733169
Alexandros Zervopoulos, Konstantinos Oikonomou	Packet Delegation for Distributed Load Sharing in Service Function Chains: End-to-End Performance Analysis using Queuing Networks	2026	Early Access	Modeling Delays Loading	In Network Function Virtualization environments, centralized orchestration for load balancing often faces scalability limitations and high signaling overheads. To address this, packet delegation is investigated as a distributed load sharing mechanism where Virtual Network Functions (VNFs) autonomously monitor local congestion and probabilistically delegate excess traffic to peers without global coordination. While this localized decision-making enhances scalability and resilience, it introduces probabilistic and bursty traffic that challenges standard performance modeling. This paper proposes an analytical framework to estimate the end-to-end performance of Service Function Chains using queuing networks. The delegation process is modeled as a Markov-Modulated Poisson Process, and a burstiness-corrected Poisson approximation scheme is introduced to accurately capture the impact of traffic variability on downstream VNFs. Furthermore, an iterative fixed-point algorithm is developed to resolve the interdependencies between VNFs and estimate network-wide metrics. Simulation results validate the accuracy of these models, with the burstiness-corrected approximation outperforming a previously proposed Poisson approximation under certain conditions, which are investigated in this paper. The results highlight that packet delegation effectively mitigates local hotspots and improves system performance using only local state information, even when evaluated using a real network trace.	10.1109/TNSM.2026.3733637
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
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
Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan	Performance Drift Detection in Machine Learning as a Service (MLaaS) for IoT Environments	2026	Early Access	Modeling Signal detection Internet of Things	Machine Learning as a Service (MLaaS) is a powerful cloud paradigm enabling data-driven intelligent applications in Internet of Things (IoT) environments, widely adopted across healthcare, smart homes, and industry due to its costeff-ectiveness. However, the dynamic nature of IoT frequently alters data distributions, affecting MLaaS stability, while periodic MLaaS updates further introduce performance drift. Unlike traditional ML systems, MLaaS clients operate as black-box users without access to internal data or parameters, making drift detection particularly challenging. To address this, we propose a novel MLaaS Performance Drift Detection framework for IoT environments. The framework first employs an MLaaS extraction model that learns service behavior from input–output pairs and identifies prediction-influenced features. Building on this, the proposed MLaaS Performance Drift Detection (MPDD) model jointly captures variations in input data and MLaaS behavior.We further design an Adaptive-Temporal Performance Drift Detection Mechanism (APDDM) that dynamically adjusts monitoring frequency based on behavioral and data variations, enabling timely drift detection for effective service management. Extensive experiments on real-world datasets demonstrate that MPDD achieves up to 22–25% accuracy improvement over baseline drift detection methods. APDDM provides an average accuracy gain of approximately 4% and reduces the miss detection rate by around 9% compared to fixed-interval monitoring.	10.1109/TNSM.2026.3732372
Jesús F. Cevallos-Moreno, Alessandra Rizzardi, Sabrina Sicari, Alberto Coen-Porisini	TIGER: an open-source cyber-Threat Intelligence Game Environment for Reinforcement learning	2026	Early Access	Cyber threat intelligence Labeling Training	Open-source testbeds for intrusion detection and mitigation enable benchmarking the efficacy of machine-learningbased cyber-defensive systems under increasingly realistic, heterogeneous network scenarios. In this context, the open-world nature of network intrusion detection requires defences to use continual learning strategies to adapt pattern-matching to new attack classes. The cost of periodically fine-tuning pre-trained detectors is not only computational but also encompasses the broader Cyber Threat Intelligence (CTI) life-cycle, which involves collecting, analyzing, and processing raw data into actionable insights. For ML-driven defensive systems, such actionable CTI ultimately takes the form of curated, labelled traffic traces of novel attacks. However, the concurrent optimisation of these intelligence-gathering costs and defence effectiveness has received little attention from the research community. In this respect, this work presents TIGER, an open-source Threat Intelligence Game Environment for Reinforcement learning-based agents to be trained and evaluated toward the optimisation of the costs-benefit trade-off associated with realistic ML-driven cyberdefence life-cycles. TIGER uses realistic network simulation software to model an active-learning game in which an agent learns to timely purchase CTI—abstracted in our environment as labelled samples of Zero-day attacks— to retrain its intrusion detection machinery on new attack patterns, while considering a constrained resource availability scenario.	10.1109/TNSM.2026.3732249
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		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
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
Ren-Hung Hwang, Jiao-Chuan Huang, Yuan-Cheng Lai, Ying-Dar Lin	Reinforcement Learning Meets LLM Honeypots: A MITRE Engage–Aligned Approach	2026	Early Access	Large language models Modeling Training	The growing sophistication of cyberattacks, accelerated by large language models (LLMs), highlights the limitations of traditional honeypots, which often lack realism, require heavy maintenance, and rely on static deception strategies. Recent LLM-based honeypots generate fluent, context-aware responses but cannot adapt to evolving attacker behavior, limiting long-term effectiveness. This work presents an adaptive honeypot that integrates reinforcement learning (RL) with LLM-generated deception, aligning state, reward, and action spaces with the MITRE ATT&CK and MITRE Engage frameworks. A finetuned LLM infers attacker tactics, techniques, and procedures (TTPs) from live command sequences, providing semantically rich states for the RL agent, which then selects context-sensitive actions from Engage’s Affect strategies to guide adversaries toward deeper and higher-value engagement. Evaluated on Linux and Windows testbeds, the system achieved a 23% increase in cumulative engagement reward on Windows over a non-RL baseline (p < 0.001). Ablation over five random seeds shows that replacing the learned policy with random action selection over the same action space collapses attack depth from 9.52 to 4.25 on Linux (p < 0.001), confirming that the learned policy, not the action space alone, drives engagement. Intent analysis accuracy improved by 55 percentage points relative to a rule-based baseline (Wazuh), and LLM-generated responses fell within 10 percentage points of a real system, a substantially smaller gap than Cowrie, an ordering confirmed by an independent cross-family judge. These results demonstrate that RL-driven adaptation, combined with LLM realism and standardized engagement frameworks, enables honeypots that sustain realistic, intelligence-rich interactions and enhance threat analysis without compromising system safety.	10.1109/TNSM.2026.3731455
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
Mohamed Anis Sakka, Fahdah Alalyan, Wael Jaafar, Rami Langar	FML-AD: A Federated Learning Framework with Meta-Model Refinement for Cyberattack Duration Prediction in 5G O-RAN	2026	Early Access	Modeling 5G mobile communication Open RAN	The emergence of fifth-generation (5G) and open radio access network (O-RAN) architectures has expanded the attack surface for cyber threats, creating an urgent need for enhanced and proactive mitigation strategies to ensure the preservation of quality of service (QoS), network reliability, and user data privacy in highly distributed and virtualized environments. In this context, we introduce FML-AD, a federated learning framework with meta-model refinement for adaptive attack duration prediction without centralizing raw training traffic. The proposed method combines a FLAD-trained Transformer for distributed temporal learning with an XGBoost-based second-level regression model that refines the initial predictions using controlled O-RAN testbed examples, thereby reducing prediction errors associated with the benchmark-to-deployment distribution shift. Extensive evaluation on the CICIoT2023 dataset shows that FML-AD improves prediction accuracy compared with conventional centralized and federated baselines. Furthermore, an evaluation on a controlled 5G O-RAN testbed involving ten TCP SYN and UDP flooding scenarios provides a proof-of-concept demonstration of the feasibility of the proposed post-detection prediction pipeline in the evaluated configuration. A separate exploratory transfer-learning assessment using 5G V2X radio-jamming scenarios and leave-one-scenario-out (LOSO) validation further examines whether the duration-prediction pipeline can be adapted to a different disruption mechanism under limited target-domain data. For the considered jamming configurations, several operating points in the early 3–5 sec range also produce favorable prediction results, providing preliminary evidence of transferability.	10.1109/TNSM.2026.3731093
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
Siyu Jiang, Feng Guo, Di Chen, Yuan Liu, Ying Chen, Weijun Sun, Yu Wang, Shen Su	Smart Contract Vulnerability Detection via Mask Consistency with Dynamic Margin Adjustment	2026	Early Access	Labeling Modeling Smart contracts	With the rise of smart contract applications, new attacks that exploit contract vulnerabilities continue to emerge, and effective vulnerability detection methods are urgently needed. Deep learning-based methods have shown excellent performance. However, for new types of vulnerabilities, due to the lack of real labels to help the model learn subtle code differences, previous methods have difficulty distinguishing between vulnerable contracts and safe contracts with similar key code segments, resulting in false negatives. To address this problem, this paper proposes a smart contract vulnerability detection method that uses mask consistency (MC) and dynamic margin adjustment (DMA). Unlike traditional Masked Language Modeling (MLM) in CodeBERT that performs token-level reconstruction for general representation learning, our MC enforces classification-level consistency between a masked student network and an unmasked EMA teacher network at the semantic graph block level under semi-supervised domain adaptation. This enhances the model’s discriminative ability by adding contextual information of similar code segments as additional clues. Specifically, we define a student network to learn masked contracts, a teacher network to learn complete contracts, and implement few-shot learning through semi-supervised domain adaptation. In this process, the student network is helped to learn to correctly distinguish similar contracts by fusing contextual information. In order to guide students more effectively, we use DMA to screen high-quality pseudo-labels. We conduct extensive experiments on open source real-world vulnerability datasets, and the results show that our method significantly outperforms current mainstream deep learning methods in detecting six types of vulnerabilities. This approach also pioneers the application of domain adaptation and integrates MC with DMA in vulnerability detection, providing guidance for detecting different types of vulnerabilities.	10.1109/TNSM.2026.3733072
Wei Zhang, Shiyun Xiong, Yixin Li, Yan Lei, Hongyi Li	TCAA: An Efficient Blockchain Consensus Based on Transaction Collector and Address Aggregation for UAV Ad Hoc Networks	2026	Early Access	Synchronization Probability Autonomous aerial vehicles	Blockchain technology provides a promising solution to data security and trust challenges in UAV ad hoc networks. However, conventional consensus mechanisms suffer from notable inefficiencies in resource-constrained environments, characterized by high computational and communication overhead, and prolonged consensus latency. To address these limitations, this paper proposes a blockchain consensus mechanism based on Transaction Collector and Address Aggregation (TCAA) for UAV ad hoc networks. We introduce a block proposal algorithm using a transaction collector, which dynamically assigns block proposal rights through a difficulty threshold. This threshold is defined by transaction types and quantities, thereby eliminating dependence on hash competition or voting verification. The block proposal algorithm operates in parallel with the gossip protocol, ensuring randomness in mempool transactions and unpredictability in proposer selection. Moreover, TCAA incorporates an encrypted address aggregation and recognition scheme to accelerate block synchronization. This scheme allows the block sender to proactively discern the state of the receiver’s mempool, which substantially reduces communication rounds and message redundancy across the network. We model the message synchronization process using a two-dimensional Markov chain and derive closed-form expressions for the end-to-end latency, bandwidth consumption, consensus latency, and transaction collection rounds. Experimental results demonstrate that for a block containing 1000 transactions and a RTT of 50 ms, TCAA reduces the end-to-end latency by 29.7%, 25.3%, and 22.9% compared to Compact, Graphene, and XThin, respectively, while achieving a communication complexity of 2n.	10.1109/TNSM.2026.3732846
Xiaodi Wang, Yunwei Dong, Weizhi Meng, Meng Li, Yining Liu	Dropout-Tolerant Privacy-Preserving Aggregation for Federated Mobile Crowdsensing	2026	Early Access	Modeling Privacy Internet of Things	Federated Learning (FL) has emerged as a key enabler for privacy-preserving, decentralized sensing systems, giving rise to Federated Mobile Crowdsensing (F-MCS). A well-known bottleneck in such systems is the inefficiency of synchronous training, which stalls for all participants and is susceptible to stragglers in heterogeneous environments. Although asynchronous FL methods have been explored to alleviate this, they often introduce the critical issue of stale updates, which can degrade model convergence and accuracy. To simultaneously address the challenges of efficiency, staleness, and robustness, this paper proposes a novel Dropout-Tolerant Privacy Aggregation (DTPA) scheme for FL that operates without a trusted third party (TTP). Our solution leverages the distributed decryption feature of the lifted EC-ElGamal cryptosystem to enable secure, decentralized model aggregation. We further introduce an efficient worker selection algorithm to systematically reduce waiting time. Moreover, a dedicated dropout-tolerant mechanism is developed to maintain protocol execution even under a high rate of client failures, thereby enhancing robustness. Security analysis confirms that our scheme fulfills essential privacy and security requirements. Extensive simulations demonstrate that the proposed DTPA scheme significantly improves training efficiency and convergence stability compared to state-of-the-art methods, while remaining practical for deployment on resource-constrained mobile devices.	10.1109/TNSM.2026.3732465
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
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
Francisco Muro, Eduardo Baena, Tomaso De Cola, Sergio Fortes, Raquel Barco	AI-Driven Optimization of Virtual Network Function Allocation in 6G Non-Terrestrial Networks	2026	Early Access	Resource management Optimization Satellites	The integration of 6G technologies into Non-Terrestrial Networks (NTNs) raises a fundamental orchestration problem: how to allocate Virtual Network Functions (VNFs) across satellite and terrestrial domains under tight onboard resource constraints and a continuously changing topology. The virtualized 6G Open Radio Access Network (O-RAN) paradigm makes it possible to run 5G software stacks on Software-Defined Radios (SDRs) based on General Purpose Processors (GPPs), but it also turns VNF placement into a high-dimensional, multi-objective decision that static heuristics and model-based formulations struggle to capture. This paper addresses that gap by introducing an AI-driven VNF allocation framework for 6G-NTN environments built on an O-RAN-based distributed architecture and orchestrated on top of Kubernetes. The VNF allocation problem is formalized for a multi-domain 6G-NTN scenario with constrained satellite resources, and a measurement-based test campaign is designed to characterize the emulated platform in terms of virtual resource utilization and end-to-end performance. The framework couples tree-based machine learning predictors with a gradient-free optimizer to reach the optimal feasible allocation, outperforming two heuristic baselines drawn from the VNF placement literature by reducing the service RTT by up to 39% and delivering up to 3× higher YouTube DL throughput with respect to the best feasible heuristic. Beyond these gains, the proposed framework establishes a measurement-driven, reproducible methodology for VNF allocation in 6GNTN scenarios, demonstrating that AI-driven orchestration can systematically uncover non-obvious resource configurations that purely analytical or static approaches consistently miss.	10.1109/TNSM.2026.3724474
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
Rita Ingabire, Antonio Bazco-Nogueras, Vincenzo Mancuso, Luis M. Contreras, Jesús Folgueira	Explainable AI to Understand the Latency Behavior of Public Cloud Service Platforms	2026	Early Access	Clouds Distance measurement Forecasting	Cloud platforms have become a core component of the Internet because most services and products rely on them to host their backends. Estimating and understanding the latency experienced when accessing those cloud platforms is a challenge of growing importance that has not been sufficiently studied. To address this relevant matter, we conducted a three-month measurement campaign, collecting traceroute data every 30 min across 256 source–destination probe pairs. Our specific goal is to analyze whether the current network is able to provide adequate performance for emerging applications and services. We use this dataset to evaluate the performance of forecasting algorithms when predicting cloud latency from both temporal and spatial perspectives, and we leverage post-hoc explainability methods to identify the drivers affecting latency. Several prior studies provide public cloud-latency datasets, but these datasets are generally analyzed in isolation. To close this gap and provide a cross-dataset comparative analysis of cloud-latency measurements, we analyzed the related publicly available datasets and applied a common forecasting and explainability workflow to compare their findings. Our analysis reveals that operators do not require complex methods to predict latency and that distance and a few other simple features are sufficient to achieve operationally accurate predictions. We find latency to be remarkably stable from the user’s perspective, both over the duration of the campaign and across hours of the day, which contrasts with previous findings, and we show that the specific path traversed has a significant impact on latency.	10.1109/TNSM.2026.3730351