TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Yuchao Dang, Xuefen Chi, Linlin Zhao, Zhu Han	Improving Spectrum Efficiency through Multi-hop QoS Analysis and Interference Decomposition in Integrated Access and Backhaul Networks	2026	Early Access	Interference Quality of service Backhaul networks	The dense deployment of Integrated Access and Backhaul (IAB) networks exacerbates spectrum consumption. This paper aims to enhance Spectrum Efficiency (SE) in IAB networks through multi-hop Quality of Service (QoS) analysis and network interference decomposition. We propose a multi-hop delay QoS analysis method that increases computational efficiency and accuracy, thus preventing spectrum over-allocation. We introduce a Transformer-based Interference Path Loss Assessment Neural Network (TIPA-NN) to tackle the issue of inadequate interference information in complex IAB networks, ensuring efficient spectrum reuse. The simulation results show that the proposed QoS analysis method effectively approximates delay unreliability probability across varying hop counts, demonstrating good scalability. The minimum service rate derived supports diverse QoS requirements in multi-hop scenarios. Our algorithm guarantees QoS and enhances SE in IAB networks, outperforming baselines and exhibiting topology-agnostic adaptability. Notably, there is a minimum of 25.03% reduction in subcarrier consumption compared to existing approaches, while ensuring improved SE.	10.1109/TNSM.2026.3660735
Rajasekhar Dasari, Sanjeet Kumar Nayak	PR-Fog: An Efficient Task Priority-based Reliable Provisioning of Resources in Fog-Enabled IoT Networks	2026	Early Access		As the demand for real-time data processing grows, fog computing emerges as an alternative to cloud computing, which brings computation and storage closer to IoT devices. In Fog-enabled IoT networks, provisioning of fog nodes for task processing must consider factors, such as latency, energy consumption, cost, and reliability. This paper presents PR-Fog, a scheme for optimizing the provisioning of heterogeneous fog nodes in fog-enabled IoT networks, considering parameters such as task priority, energy efficiency, cost efficiency, and reliability. At first, we create an analytical framework using M/M/1/C priority queuing system to assess the reliability of these heterogeneous fog nodes. Building on this analysis, we propose an algorithm that determines the optimal number of reliable fog nodes while satisfying latency, energy, and cost constraints. Extensive simulations show significant enhancements in key performance metrics when comparing PR-Fog to existing schemes, including a 36% decrease in response time and an 8% improvement in satisfaction ratio, resulting in minimized 23% fog node provisioning costs. Additionally, PR-Fog’s effectiveness is validated through real testbed experiments.	10.1109/TNSM.2026.3661745
Xiaoshan Yu, Huaxi Gu, Qian Zhang	RCC: Rate-based Congestion Control for the Lossless Network	2026	Early Access		It has been widely accepted that hop-by-hop flow control is applied to High Performance Computing (HPC) interconnect network to ensure lossless transmission. However, hop-by-hop flow control directly interferes with existing congestion control because of inaccurate congestion detection. The aim of this study is to eliminate the interference of hop-by-hop flow control on congestion detection and the interference of flow rate changes on rate adjustment in lossless networks. We designed Rate-based Congestion Control (RCC), which includes a new congestion detection mechanism based on the source sending rate. Combined with congestion detection, we designed an individual rate control mechanism that slows down congested flows and accelerating victim flows. The extensive simulation results based on general traffic patterns and benchmark for HPC systems show that compared with the existing congestion control strategies of the lossless networks, RCC improves 99th percentile FCT performance by 12.55∼29.63%, and the maximum reduction in congestion impact reaches 40.34%.	10.1109/TNSM.2026.3661289
Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Mrityunjoy Gain, Zhu Han, Choong Seon Hong	Age of Sensing Empowered Holographic ISAC Framework for NextG Wireless Networks: A VAE and DRL Approach	2026	Early Access	Array signal processing Resource management Integrated sensing and communication	This paper proposes an AI framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-assisted base station (BS)-enabled wireless network. The AI-driven framework aims to achieve optimized power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO BS for serving the users. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the communication signal-to-interference-plus-noise ratio (SINRc) of the received signals and beam-pattern gains to improve the sensing SINR of reflected echo signals, which in turn maximizes the achievable rate of users. A novel AI-driven framework is presented to tackle the formulated NP-hard problem that divides it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the sensing information achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINRc improvement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively. Therefore, our proposed AI-driven framework guarantees efficient power allocation for holographic beamforming through ISAC schemes leveraging AoS.	10.1109/TNSM.2026.3654889
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
Alparslan Çay, Müge Erel-Özçevik, Bilal Karaman, İlhan Baştürk, Engin Zeydan, Sezai Taşkin	Proof of Genesis-Supported Blockchain and Resilience Networks for In-Disaster Scenarios	2026	Early Access		The increasing intensity and frequency of disasters worldwide necessitate the development of more resilient, efficient, and adaptable disaster management systems. Conventional centralized systems often fail to meet the complex requirements of disaster scenarios and are inefficient in terms of communication, energy, and decision-making processes. This paper proposes a novel blockchain-based Proof of Genesis (PoG) method to strengthen systems’ resilience in disaster scenarios. Unlike traditional blockchain mechanisms, which may not be optimized for the high risks and dynamic nature of disaster scenarios, PoG uses meiosis, mutation, recombination and natural selection steps to ensure the robustness, scalability and sustainability of the incapacitated system during a disaster. A comprehensive architecture that integrates PoG with strategic energy and communications frameworks to create a resilient, decentralized system that can withstand and quickly recover from the effects of disasters, is proposed. Through comparative analyses and extensive simulations, we show the superiority of the PoG method over conventional blockchain approaches by offering high security as Proof of Work (PoW), and less energy consumption as Proof of Stake (PoS).Moreover, it is more scalable than Bitcoin and Ethereum and can be scaled as nearly as Polygon. Our results show that the proposed approach offers a promising way to revolutionize disaster management systems.	10.1109/TNSM.2026.3657902
Xinshuo Wang, Lei Liu, Baihua Chen, Yifei Li	ENCC: Explicit Notification Congestion Control in RDMA	2026	Early Access	Bandwidth Data centers Heuristic algorithms	Congestion control (CC) is essential for achieving ultra-low latency, high bandwidth, and network stability in high-speed networks. However, modern high-performance RDMA networks, crucial for distributed applications, face significant performance degradation due to limitations of existing CC schemes. Most conventional approaches rely on congestion notification signals that must traverse the queuing data path before congestion signals can be sent back to the sender, causing delayed responses and severe performance collapse. This study proposes Explicit Notification Congestion Control (ENCC), a novel high-speed CC mechanism that achieves low latency, high throughput, and strong network stability. ENCC employs switches to directly notify the sender of precise link load information and avoid notification signal queuing. This allows precise sender-side rate control and queue regulation. ENCC also ensures fairness and easy deployment in hardware. We implement ENCC based on FPGA network interface cards and programmable switches. Evaluation results show that ENCC achieves substantial through-put improvements over representative baseline algorithms, with gains of up to 16.6× in representative scenarios, while incurring minimal additional latency.	10.1109/TNSM.2026.3656015
Awaneesh Kumar Yadav, An Braeken, Madhusanka Liyanage	A Provably Secure Lightweight Three-factor 5G-AKA Authentication Protocol relying on an Extendable Output Function	2026	Early Access	Authentication Protocols Security	Compared to 4G, the designed authentication and key agreement protocol for 5G communication (5G-AKA) offers better security. State-of-the-art shows that various protocols indicate the flaws in the 5G-AKA and suggest solutions primarily for the desynchronization attack, traceability attack, and perfect forward secrecy. However, most authentication protocols fail to facilitate the device stolen attack and are expensive; they also do not consider the prominent security issues such as post-compromise security and non-repudiation. Considering the above demerits of these protocols and the necessity to offer additional security, a provably secure lightweight 5G-AKA multi-factor authentication protocol relying on an extendable output function is proposed. The security of the proposed work has been confirmed informally and formally (ROR logic, GNY logic, and Scyther tool) to ensure that the proposed work handles all types of attacks and offers additional security features, such as post-compromise features and non-repudiation. Furthermore, we compute the performance of the proposed work and compare it with its counterparts to show that our work is less costly and more suitable for lightweight devices than others in terms of computational, communication, storage, and energy consumption cost.	10.1109/TNSM.2026.3656167
Junfeng Tian, Rongyi Fei	A certificateless dynamic anonymous authentication scheme for mobile edge computing	2026	Early Access		In mobile edge computing (MEC), many identity-based authentication schemes rely on an unrealistic prior-knowledge assumption about edge server identities, which limits their applicability in highly dynamic MEC environments. On the other hand, although certificateless schemes alleviate the computational overhead introduced by bilinear pairings in ID-based schemes, they face new challenges regarding secure key storage and the achievement of full user anonymity. To simultaneously address these issues, this paper proposes a new certificateless dynamic anonymous authentication scheme based on physical unclonable functions (PUFs) and elliptic curve cryptography (ECC), tailored for authentication and key agreement between mobile users and edge servers in dynamic MEC environment. By leveraging PUFs, the scheme resolves the key storage issue commonly found in traditional certificateless authentication approaches. Additionally, the scheme supports dynamic anonymity and frequent updates of public–private key pairs, thereby enhancing system security and providing user with full anonymity and unlinkability. The proposed scheme is rigorously evaluated through both informal and formal security analyses, including BAN logic, the Real-Or-Random (ROR) model, and automated verification via ProVerif. Comparative results demonstrate that our scheme achieves stronger computational efficiency, lower energy consumption, lower average message delay, and acceptable communication and storage overhead, while maintaining robust security guarantees compared with recent state-of-the-art approaches in this field.	10.1109/TNSM.2026.3661192
Beom-Su Kim	A Semantic-Aware TSN Framework for Minimizing Age of Informative Data in Real-Time Industrial Monitoring Systems	2026	Early Access		Real-time industrial monitoring systems rely on the timely delivery of semantically important data, such as anomaly-indicating sensor readings, to enable accurate and responsive decision-making. Credit-Based Shaping (CBS), a key mechanism in Time-Sensitive Networking (TSN), is well-suited for such systems due to its ability to dynamically manage aperiodic and bursty traffic without rigid transmission schedules. However, CBS was originally designed for Audio Video Bridging (AVB) traffic, which is periodic and less delay-sensitive, and thus lacks mechanisms to prioritize packets based on their semantic importance or freshness. As a result, critical updates may be delayed by the transmission of redundant or outdated packets. Motivated by these limitations, this paper presents an enhanced CBS mechanism, aiming to ensure the timeliness of semantically informative data in real-time industrial monitoring systems. Specifically, we encapsulate this enhancement within a semantic-aware TSN framework, which integrates three tightly coupled techniques: (1) semantic-based packet prioritization, (2) age-aware traffic shaping, and (3) age-aware packet forwarding and filtering. These mechanisms work in synergy to detect and expedite the transmission of high-priority, semantically meaningful packets, while suppressing redundant updates. Simulation results demonstrate that the proposed approach significantly improves anomaly detection responsiveness while maintaining efficient bandwidth utilization.	10.1109/TNSM.2026.3661050
Zhihao Wen, Weishi An, Chuanhua Wang, Quanbo Ge, Thippa Reddy Gadekallu, Hailin Feng, Kai Fang	EK-IGNN: Defending Meteorological Networks Against Covert Attacks using EMD-Kalman Noise Fingerprinting and Intrinsic Graph Neural Networks	2026	Early Access		The meteorological communication networks provide critical data support for agriculture and environmental monitoring. However, covert gradient-based attacks persistently inject subtle perturbations, threatening data integrity and increasing the operational overhead for network operators. To achieve proactive service assurance and security-aware network management, this paper proposes a data integrity monitoring mechanism as a managed network function, named EK-IGNN. Unlike traditional passive detection, EK-IGNN functions as an active security service. It first employs the Empirical Mode Decomposition Kalman Filter (EMD-KF) to extract high-fidelity attack fingerprints, which are then analyzed by an Intrinsic Graph Neural Network (IGNN). The IGNN model captures complex dependencies and adaptively amplifies weak attack features, enabling closed-loop network security management. Experimental results demonstrate that the proposed algorithm achieving an average improvement of 16.07% in accuracy and 15.27% in F1-score over state-of-the-art benchmarks.	10.1109/TNSM.2026.3659226
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry, Guangjie Han, Yunyang Zhang	A Robust Trust Management System for V2X Networks Integrating ISAC with Blockchain Smart Contracts	2026	Early Access		Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.	10.1109/TNSM.2026.3658589
Xiujun Xu, Qi Wang, Qingshan Wang, Yinlong Xu	Contract-Based Incentive Mechanism for Long-term Participation in Federated Learning	2026	Early Access	Contracts Data models Computational modeling	Federated learning (FL), as a newly-developing technique, brings the advantage of organizing multiple participants to learn together, while avoiding the leakage of their privacy information. Contract theory provides an effective incentive mechanism to encourage participants to participate in FL. Existing contract-based incentive mechanisms consider participants’ types but ignore the different contributions of participants within the same type during the training.This paper first introduces a metric, reputation, to evaluate the contribution of participants in each iteration, and then proposes a hybrid contract mechanism consisting of a short-term contract and a long-term contract. Only the participants with reputations higher than a pre-defined threshold can sign the long-term contract. We formulate the solution of the long-term contract mechanism as an optimization problem with constraints. We further simplify the constraints of the long-term contract optimization problem, and theoretically analyze the correctness of the simplification to greatly reduce its computational complexity. We prove that the model owner achieves more profit with the hybrid contract mechanism. Simulations with the MNIST dataset show that the long-term contract improves the model accuracy by at least 5% compared with the existing contracts. Furthermore, compared with the short-term contract, participants signing the long-term contract are granted more rewards.	10.1109/TNSM.2026.3657419
Somchart Fugkeaw, Kittipat Tangtanawirut, Pakapon Rattanasrisuk, Archawit Changtor	MK-WISE: Secure and Efficient Multi-Keyword Wildcard ABSE with Keyword-Level Revocation for Device–Edge–Cloud EHRs Data Sharing	2026	Early Access	Encryption Cryptography Access control	The rapid proliferation of Internet of Things (IoT) in healthcare has transformed the management of Electronic Health Records (EHRs), but also introduced critical challenges in secure retrieval, dynamic revocation, and verifiable integrity over encrypted data. Existing Searchable Encryption (SE) and Attribute-Based Searchable Encryption (ABSE) models remain limited: (i) most support only exact or prefix keyword matching and cannot handle flexible wildcard or substring queries common in medical search; (ii) revocation is coarse-grained, often requiring costly key redistribution or ciphertext re-encryption; and (iii) integrity verification either incurs heavy blockchain overhead or exposes access structures, undermining privacy. To address these gaps, we propose MK-WISE, a secure and efficient multi-keyword wildcard ABSE framework for IoT–EHR systems. MK-WISE integrates an Index–Wildcard Tree (IWT) with Substring Bloom Filters (SBF) to enable expressive wildcard and substring queries, employs a puncturable PRF–based revocation workflow with edge-local enforcement, hierarchical key updates, and optional blockchain anchoring, and incorporates homomorphic MACs for lightweight correctness and completeness verification. Security analysis proves that MK-WISE achieves confidentiality, keyword privacy, unlinkability, and revocability under standard assumptions. Experimental results demonstrate that MK-WISE significantly outperforms state-of-the-art schemes in trapdoor generation, search scalability, and revocation cost, achieving millisecond-level revocation without user disruption. These results highlight MK-WISE as a practical and comprehensive solution for privacy-preserving EHR retrieval in IoT-enabled healthcare.	10.1109/TNSM.2026.3657982
Fengqi Li, Yudong Li, Lingshuang Ma, Kaiyang Zhang, Yan Zhang, Chi Lin, Ning Tong	Integrated Cloud-Edge-SAGIN Framework for Multi-UAV Assisted Traffic Offloading Based On Hierarchical Federated Learning	2026	Early Access	Resource management Autonomous aerial vehicles Heuristic algorithms	The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.	10.1109/TNSM.2026.3658833
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
Liang Kou, Xiaochen Pan, Guozhong Dong, Meiyu Wang, Chunyu Miao, Jilin Zhang, Pingxia Duan	Dynamic Adaptive Aggregation and Feature Pyramid Network Enhanced GraphSAGE for Advanced Persistent Threat Detection in Next-Generation Communication Networks	2026	Early Access	Feature extraction Adaptation models Computational modeling	Advanced Persistent Threats (APTs) pose severe challenges to Next-Generation Communication Networks (NGCNs) due to their stealthiness and NGCNs’ dynamic topology, while conventional GNN-based intrusion detection systems suffer from static aggregation and poor adaptability to unseen nodes. To address these issues, this paper proposes DAA-FPN-SAGE, a lightweight graph-based detection framework integrating Dynamic Adaptive Aggregation (DAA) and Multi-Scale Feature Pyramid Network (MSFPM). Leveraging GraphSAGE’s inductive learning capability, the framework effectively models unseen nodes or subgraphs and adapts to NGCN’s dynamic changes (e.g., elastic network slicing, online AI model updates)—a key advantage for handling NGCN’s real-time topological variations. The DAA module employs multi-hop attention to dynamically assign weights to neighbors at different hop distances, enhancing capture of hierarchical dependencies in multi-stage APT attack chains. The MSFPM module fuses local-global structural information via a gated feature selection mechanism, resolving dimensional inconsistency and enriching attack behavior representation. Extensive experiments on StreamSpot, Unicorn, and DARPA TC#3 datasets demonstrate superior performance, meeting detection requirements of large-scale NGCNs.	10.1109/TNSM.2026.3660650
Sheng-Wei Wang, Show-Shiow Tzeng	Accurate Estimation of Selfish Mining Rate by Stale Block Ratio in a Proof-of-Work Blockchain	2026	Vol. 23, Issue	Blockchains Estimation Data mining	Selfish mining presents a significant threat to Proof-of-Work blockchains by compromising fairness among miners and wasting computational resources through the generation of stale blocks. Detecting and mitigating selfish mining attacks is crucial to maintain blockchain security and efficiency. While the presence of stale blocks is commonly used as an indicator of selfish mining, merely identifying the attacker’s existence is insufficient. A more valuable goal is to estimate the extent of the attack, specifically the selfish mining rate. In this paper, we propose two analytical models that can precisely calculate stale block ratios in blockchains with one or two selfish miners. These models provide closed-form functions that compute stale block ratios given selfish mining rates. We then derive inverse functions to estimate the selfish mining rate based on observed stale block ratios. Simulation results demonstrate that our analytical models can effectively calculate stale block ratios, with an average discrepancy of only 3.12% compared to simulation results. Furthermore, our estimation approach accurately predicts selfish mining rates, with a mean error of 2.82%. Estimating the selfish mining rate with high accuracy enables better identification of malicious attacks, enhances fairness, optimizes resource allocation, and supports the development of more robust security mechanisms in blockchain networks.	10.1109/TNSM.2025.3631463
Neco Villegas, Ana Larrañaga, Luis Diez, Katerina Koutlia, Sandra Lagén, Ramón Agüero	Optimizing QoS MAC Scheduling in 5G NR: A Lyapunov Approach Evaluated With XR Traffic	2026	Vol. 23, Issue	Quality of service 5G mobile communication Bit rate	5th Generation (5G) and beyond technologies are designed to convey multiple Quality of Service (QoS) requirements coming from highly heterogeneous services. In this regard, 5G extends the QoS definition of previous technologies. In order to fulfill the requirements of new services, it is necessary to develop resource management solutions that effectively and efficiently translate them to usage of resources. Therefore, MAC schedulers are to be designed to accommodate the demands of emerging applications with stringent QoS requirements, such as eXtended Reality (XR). In this work, we introduce a Lyapunov-based Medium Access Control (MAC) scheduler designed to appropriately tackle coexisting heterogeneous QoS flows. The proposed approach allows the implementation of policies that explicitly consider the QoS requirements, as well as an efficient use of allocated resources. The results obtained through extensive experiments over the ns-3 5G-LENA simulator demonstrate that our scheduler guarantees the required time-average bit rate for every QoS flow at every time window, while ensuring the stability of traffic queues for all users.	10.1109/TNSM.2025.3631520
Kaiyi Zhang, Changgang Zheng, Nancy Samaan, Ahmed Karmouch, Noa Zilberman	Design, Implementation, and Deployment of Multi-Task Neural Networks in Programmable Data-Planes	2026	Vol. 23, Issue	Multitasking Artificial neural networks Pipelines	The increasing demand for real-time inference on high-volume network traffic has led to the rise of in-network machine learning, where programmable switches execute various models directly in the data-plane at line rate. Effective network management often involves multiple prediction tasks, such as predicting bit rate, flow size, or traffic class; however, existing solutions deploy separate models for each task, placing a significant burden on the data-plane and leading to substantial resource consumption when deploying multiple tasks. To address this limitation, we introduce MUTA, a novel in-network multi-task learning framework that enables concurrent inference of multiple tasks in the data-plane, without exhausting available resources. MUTA builds a multi-task neural network to share feature representations across tasks and introduces a data-plane mapping methodology to fit it within network switches. Additionally, MUTA enhances scalability by supporting distributed deployment, where different layers of a multi-task model can be offloaded across multiple switches. An orchestrator employs multi-objective optimization to determine optimal model placement in multi-path networks. MUTA is deployed on P4 hardware switches, and is shown to reduce memory requirements by $\times 10.5$ , while at the same time improving accuracy by up to 9.14% using limited training data, compared with state-of-the-art single-task learning solutions.	10.1109/TNSM.2025.3629642