TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Yingpu Nian, Xiang Jia, Baishun Zhou, Zhi Wang, Bo Yi, Xinhao Zhou, Haodong Li, Yuan Yang, Xingwei Wang, Geyong Min, Keqin Li	XAForward: Accelerating Distributed Large-scale Language Model Training Through Fast eXpress Data Path	2026	Early Access	Payloads Military aircraft Space technology	With the rapid development of Artificial Intelligence Generated Content (AIGC), single data centers are increasingly unable to meet the growing demands for data and computational resources in distributed large-scale language model (LLM) training. In this context, distributed training across heterogeneous data centers has become a necessary choice to enhance computational power and flexibility. However, the networks in heterogeneous data centers are polymorphic, with diverse communication protocols and network architectures. This heterogeneity renders traditional routing devices ineffective in recognizing and processing gradient data. Moreover, frequent copying and excessive parsing of gradient data by routing devices across heterogeneous data centers significantly increase model training time. To address these challenges, we propose XAForward, a method for accelerating distributed LLM in heterogeneous data centers using eXpress Data Path (XDP). Specifically, XAForward introduces a polymorphic-compatible protocol that reconstructs the header of gradient data packets to enable efficient data forwarding across different communication protocols in heterogeneous data centers. Additionally, to accelerate distributed LLM computing and reduce gradient data copying and excessive parsing during training, XAForward leverages kernel-bypass techniques based on XDP for packet processing and kernel-level data forwarding using network index identifiers. Experimental results show that, compared to state-of-the-art methods, XAForward reduces the distributed LLM training time by approximately 35% to 40%.	10.1109/TNSM.2026.3684024
Sifeddine Salmi, Messaoud Ahmed Ouameur, Miloud Bagaa, George C. Alexandropoulos, Abdellah Tahenni, Daniel Massicotte, Adlen Ksentini	AI-Native O-RAN Architectures for 6G: Towards Real-Time Adaptation, Conflict Resolution, and Efficient Resource Management	2026	Early Access	Telemetry Aerospace and electronic systems Antennas	Open Radio Access Network (O-RAN) enables modular and intelligent control of radio resources through open interfaces and programmable RAN components. As networks evolve toward sixth-generation (6G) systems, the proliferation of autonomous xApps and rApps introduces a critical challenge: Coordinating concurrent AI-driven control actions under tight near-real-time constraints while avoiding instability and conflicting decisions. This paper focuses on two tightly coupled enablers for AI-native O-RAN orchestration: Conflict-aware control and intent-driven automation. We propose an AI-native orchestration framework centered on a CME integrated into the Near-RT RIC, and a complementary LLM-based intent orchestration module deployed in the Non-RT RIC. The CME is designed to autonomously arbitrate conflicting xApp actions by learning adaptive mitigation policies from structured conflict signals, system context, and performance feedback, rather than relying on static priorities or predefined conflict classes. The LLM module translates high-level operator intents into policy constraints and control objectives that guide conflict resolution and xApp behavior. Overall, this work advances AI-native O-RAN orchestration by grounding conflict-aware control and LLM-assisted intent translation in practical measurements, and by outlining a clear path toward scalable, adaptive, and resilient control mechanisms required for future 6G RIC deployments.	10.1109/TNSM.2026.3684880
Killian Cressant, Federico Larroca, Stefano Secci, Pedro B. Velloso	On Graph Design for GNN-based Network Anomaly Detection	2026	Early Access	Telemetry Aerospace and electronic systems Radio broadcasting	Graph Neural Networks (GNNs) have gained significant attention for multivariate time series analysis in recent years. However, applying them to real-world networking data introduces several key challenges, particularly in designing meaningful and effective graph structures. In this paper, we propose a novel method for constructing initial graphs tailored for time series anomaly detection in complex network environments. Our approach, called COSI (COrrelation SImilarity), leverages two fundamental properties of real-world data: feature name semantics and statistical correlation. By combining natural language processing (NLP) with correlation analysis, COSI produces graph structures that significantly enhance GNN model performance for anomaly detection tasks, outperforming conventional graph construction methods in almost all evaluation scores across all datasets. We extensively evaluate COSI on three datasets, including two real-world networking datasets and one widely used benchmark dataset, and we open source the implementation to encourage reproducibility, further research, and practical adoption by the community.	10.1109/TNSM.2026.3684653
Abdeltif Azzizi, Mohamad Al Adraa, Chadi Assi, Michael Y. Frankel, Vladimir Pelekhaty	Experimental Topological Analysis in Next-Generation Data Center Networks: STRAT and Clos Topologies	2026	Early Access	Telemetry Aerospace and electronic systems Payloads	This paper presents an experimental and simulationbased evaluation of two data center network (DCN) topologies: the widely adopted hierarchical Clos architecture and STRAT, a flat, expander-based topology designed around passive optical interconnects. While Clos offers proven scalability and performance, it incurs hardware complexity and suffers from congestion in oversubscribed scenarios. STRAT eliminates aggregation and spine layers entirely—using only Top-of-Rack (ToR) switches interconnected via static optical patch panels—to reduce cost, simplify deployment, and enhance path diversity. Our goal is to assess these topologies based on their inherent architectural properties—namely throughput, congestion resilience, scalability, and cost—without relying on congestion control protocols or centralized traffic engineering. To this end, we adopt simple forwarding schemes based purely on local information: ECMP for Clos, and ECMP with Dynamic Group Multipath (DGM) for STRAT. We evaluate both topologies on a physical testbed built from commercial Ethernet switches and further validate scalability through packet-level simulations of networks with up to 256 switches and 1,024 hosts using OMNeT++. We also introduce DEALER, a lightweight routing algorithm tailored to STRAT’s topology, and evaluate its effectiveness in dynamic conditions. Our results show that STRAT achieves up to 43% higher throughput and requires approximately 40% fewer switches than a comparable Clos topology. These gains are further supported by Load Area Under Curve (LAUC) analysis and congestion hotspot visualizations. Overall, our study highlights STRAT as a compelling and practical alternative to conventional DCN architectures, offering deployable scalability, improved performance under load, and reduced infrastructure cost.	10.1109/TNSM.2026.3685175
Yu Gu, Le Zhang, Yunyi Zhang, Ye Du	SatFedGuard: Semi-Supervised Federated Contrastive Learning with RL-Assisted Bidirectional Distillation for Anomaly Traffic Detection in Satellite Networks	2026	Early Access	Low earth orbit satellites Artificial satellites Payloads	Federated learning-based intrusion detection methods for satellite networks enable model training without sharing local data, thereby ensuring network security while significantly reducing communication overhead. However, due to the difficulty of obtaining large-scale high-quality labeled data in satellite environments, a key challenge lies in how to train intrusion detection models using abundant unlabeled traffic data. We propose SatFedGuard, a semi-supervised federated contrastive learning approach for anomaly traffic detection in satellite networks. SatFedGuard effectively integrates unlabeled in-orbit data with labeled data from ground stations for model training. First, it models the unlabeled satellite traffic data using a contrastive learning framework. To address the challenge of non-IID data distribution, an attention-based dual-path aggregation strategy is designed to generate personalized models for each satellite by leveraging model similarities. Then, a bidirectional multi-granularity distillation method between larger and smaller models is implemented, where reinforcement learning is employed to optimize the weights of different loss terms dynamically. Experiments on two satellite network traffic datasets under non-IID settings demonstrate that the proposed method significantly improves anomaly detection performance while reducing dependence on in-orbit labeled data, achieving F1-Scores of 93.38% (↑11.63%) and 99.80% (↑8.72%), respectively.	10.1109/TNSM.2026.3685416
Shuai Liu, Xiangxu Meng, Yun Zhong, Wenfeng Li, Kanglian Zhao	Dual-timescale Joint Optimization for Dynamic Edge Service Deployment and Task Scheduling in Space-air-ground Integrated Networks	2026	Early Access	Low earth orbit satellites Artificial satellites Propagation losses	This paper investigates dual-timescale joint optimization for dynamic edge service deployment and task scheduling in space-air-ground integrated networks (SAGINs) with mobile edge computing (MEC). We decompose the original problem into a long-term (LT) service deployment subproblem and a short-term (ST) task scheduling subproblem to address the timescale disparity and coupling constraints. For the LT subproblem, an improved whale optimization algorithm (IWOA) is developed with a cosine-based nonlinear convergence factor, vertical-horizontal crossover, and elite opposition-based learning to enhance exploration and convergence. For the ST subproblem, we model task scheduling as a decentralized partially observable Markov decision process (Dec-POMDP) and adopt multi-agent proximal policy optimization (MAPPO) with KL regularization and clipping for stable policy updates. A hierarchical alternating framework (with a self-attention module for traffic-feature extraction) is designed to coordinate the two timescales iteratively. Simulation results demonstrate that, compared with the baseline WOA-PPO algorithm, the proposed method increases service deployment benefits by 27.2% and reduces task scheduling costs by 38.5%.	10.1109/TNSM.2026.3685292
Alba Jano, Serkut Ayvaşik, Yash Deshpande, Wolfgang Kellerer	QUEST: User-Based Quality of Service Aware Uplink Resource Scheduling	2026	Early Access	Payloads Military aircraft Space technology	Efficient radio resource management (RRM) in 5G networks is increasingly challenged by the diverse quality of service (QoS) requirements of emerging applications and the growing uplink (UL) traffic from resource-constrained devices. Existing scheduling approaches often lack user and service-specific context, limiting their ability to guarantee timely and energy-efficient data transmission, particularly critical for the internet of things (IoT) and mission-critical services. In this work, we introduce QUEST, a QoS-aware UL scheduling framework that exploits the 5G QoS model alongside network and device context to efficiently allocate radio resources. Designed and evaluated in an indoor factory environment, QUEST supports users with various heterogeneous 5QI services under dynamic multi-user conditions. Evaluation results, validated through both real-world measurements and 3GPP-compliant simulations, show that QUEST consistently outperforms traditional channel- and QoS-aware schedulers. It improves QoS compliance, reduces packet drops and serving time, and enhances energy efficiency. For users with stringent QoS demands, measurements show a 13% increase in successfully transmitted packets and a 6.2% reduction in delay for 50% of transmissions, compared to the best-performing baseline. Benchmarking against an optimal scheduler shows that QUEST achieves the closest performance among baselines, while maintaining low complexity, making it a practical and scalable solution for 5G and beyond UL RRM.	10.1109/TNSM.2026.3685537
Zuodong Wu, Dawei Zhang, Mianxiong Dong, Kaoru Ota	PDRAA: An Efficient Privacy Data Retrieval Protocol with Anonymous Authorization Based on Verifiable Credential	2026	Early Access	Payloads Broadcasting Broadcast technology	In the data-driven era, the unchecked collection and processing of personal data has given rise to serious privacy concerns. In response, the General Data Protection Regulation (GDPR) was introduced to grant individuals stronger control over the use of their data. Privacy data retrieval methods show considerable promise in this context, but further improvements are required to balance the principles of lawfulness and data minimization. To address this problem, we propose PDRAA, an efficient privacy data retrieval protocol with anonymous authorization based on the verifiable credential (VC). Specifically, our designed VC achieves anonymous identification of data subjects and facilitates fine-grained access control by supporting selective disclosure of attributes. By combining VC with non-interactive zero-knowledge (NIZK) proofs, PDRAA enables data subjects to anonymously authenticate via VC presentation. This allows the data controller to verify the legitimacy of retrieval requests while ensuring compliance with the principle of data minimization. Besides, PDRAA introduces a re-randomization mechanism to prevent linkability attacks during the authorization process and provides lightweight, flexible authorization revocation. Moreover, we utilize Labeled Private Set Intersection (Labeled PSI) technology to meet the privacy requirements of participants and support batch retrieval. Our protocol takes a comprehensive security analysis within the Universal Composability framework. Experimental results demonstrate that PDRAA outperforms existing methods in terms of performance, which is significant for promoting compliance with GDPR.	10.1109/TNSM.2026.3681957
Li-Chin Siang, Wen-Hsing Kuo, Pei-Chieh Lin, Chih-Wei Huang, De-Nian Yang	FoV Prediction-Based Adaptive Streaming Mechanism for 6DoF Volumetric MR Applications in Multi-Base-Station Networks	2026	Early Access	Payloads Antennas Feeds	The emergence of mixed reality (MR) as a significant application in mobile networks has garnered significant attention. Wireless headsets enable unrestricted user movement within femtocell networks comprising numerous small base stations, offering a promising solution for MR applications. However, the complexity of these systems poses challenges in optimizing resource allocation across base stations. This paper proposes a novel resource allocation method for volumetric MR streaming in multi-base-station environments. The method consists of two phases. Firstly, the method uses neural networks to model and forecast users’ viewing directions. Leveraging these predictions, their confidence levels, and layer characteristics, the algorithm adjusts video quality for each user and allocates transmission resources across base stations to optimize overall performance. Through comprehensive analysis, we prove that this novel problem is NP-hard and show that our approach achieves a performance within a bounded gap from the optimal solution. Simulation results reveal that our proposed algorithm outperforms existing techniques, enhancing aggregate performance across diverse scenarios.	10.1109/TNSM.2026.3685670
Kai Huang, Andrea Sordello, Rodolfo Valentim, Luca Vassio, Idilio Drago, Marco Mellia	FedScope – Federated Host Embeddings from Telescope Traffic: Design and Implementation	2026	Early Access	Payloads Military aircraft Space technology	network telescope is a range of IP addresses that host no services. Millions of bots and scanners contact it to look for vulnerable systems, and the traffic it exposes is fundamental to understanding malicious activities. The visibility a telescope offers depends on its size and geolocation, and merging the information from multiple telescopes could help increase visibility and uncover more malicious activities. However, sharing raw telescope data is complicated, calling for solutions that allow one to directly share the knowledge rather than the data obtained from multiple deployments. In this paper, we explore the application of Federated Learning (FL) to create and share such global knowledge from the malicious activities seen in distributed telescopes. For that, we introduce FedScope, an FL-based solution for generating host embeddings in a distributed way. We compare FedScope to local and distributed alternatives in downstream tasks, such as sender classification or coordinated activities detection. We show that FedScope (i) produces embeddings of equal or higher quality than those of a single telescope; (ii) increases coverage, allowing the global model to monitor more malicious actors; (iii) avoids the sharing of the raw data, limiting exchanged data.	10.1109/TNSM.2026.3685756
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Vibha Jain, Prabal Verma, Mohit Kumar, Aryan Kaushik	Blockchain-enabled Incentive Mechanism for Federated Learning: A Multi-Agent Deep Deterministic Policy Gradient Approach	2026	Early Access	Broadcasting Broadcast technology Central Processing Unit	The expeditious growth of the Internet of Things (IoT) generates massive data, which allows advanced machine learning. However, the traditional approach of centralized model training raises the issue of high bandwidth consumption and privacy. Federated learning (FL) mitigates this by enabling local training on raw data with centralized aggregation to generate the global model. The effectiveness of FL depends upon the active participation of resource-constrained local devices. This article presents a blockchain-enabled incentive mechanism for FL leveraging the Multi-Agent Deep Deterministic Policy Gradient (MA-DDPG) algorithm. Specifically, an incentive scheme is formulated with the MEC (Mobile Edge Computing) server as the leader agent and local devices as learning agents in a cooperative environment. We formalize a two-stage Stackelberg game to establish a Nash equilibrium, which ensures fair and utility-optimized reward distribution for MEC and devices. A Markov Decision Process (MDP) is utilized to solve the equilibrium with incomplete knowledge, and utilities are optimized using the MA-DDPG algorithm. The proposed model considers data quality and device contribution to obtain optimal reward distribution and participation strategies dynamically. The experimental results show an approximate 38% improvement in MEC utility and approx 17% in device utility, with rapid convergence (approximately 300-500 episodes) at a learning rate of 0.0001.	10.1109/TNSM.2026.3682129
Krati Mittal, Akash Gupta, Parul Garg	Resource Management in Energy Efficient RSMA based Hybrid Satellite Terrestrial Network	2026	Early Access	Antennas Receiving antennas Transmitting antennas	In this paper, we study resource management in terms of energy and spectrum for a cooperative satellite terrestrial network. We investigate a comprehensive analysis of an energy efficient rate splitting multiple access (RSMA) based cooperative satellite terrestrial network. RSMA being more efficient both in terms of energy and spectrum when combined with practical non linear energy harvesting, results in a highly efficient cooperative network. While analyzing the network, we consider that the energy of the signal transmitted by the satellite is non linearly harvested at decode and forward (DF) relay. Further this harvested energy is used to transmit signal from relay to RSMA users. The satellite to relay link is characterised by shadowed Rician fading while the link between relay and RSMA users is characterised by α-μ fading. We derive the closed form expression of outage probability and ergodic capacity considering the impact of key parameters both numerically and asymptotically. Simulation results validate the numerical results obtained.	10.1109/TNSM.2026.3684366
Cheng Ren, Jinsong Gao, Yu Wang, Yaxin Li, Hongwei Li	GCN-Transformer Assisted Live SFC Migration with Hierarchical Reinforcement Learning in Mobile Edge Computing	2026	Early Access	Feeds Antennas Filtering theory	Empowered by network function virtualization (NFV), mobile edge computing aims to provide low latency and ultra reliable network services to mobile end users, achieved as a service function chain (SFC) consisting of a series of ordered virtual network functions (VNFs). Due to user mobility, live SFC migration is imperative to avoid Quality of Service (QoS) degradation. Recent advances mainly make separate decisions on VNF node remapping and migration path routing in a heuristic manner, or implement both through reinforcement learning within a single agent of ill-defined policy and action space. In this paper, given next access node, we first formulate the live SFC migration problem as an integer linear programming (ILP) model to achieve optimal solutions. Then, we present HRL-QC, a hierarchical reinforcement learning framework that jointly optimizes VNF destination node remapping, migration path and post-migration service path selections for QoS-aware and cost-efficient live SFC migration. A GCN-Transformer block is introduced to capture long-range VNF-to-physical node dependencies, while a two-level actor-critic design couples the decision-makings through inter-level reward passing. Extensive evaluations show that HRL-QC outperforms the state-of-the-art in energy consumption, migration time, end-to-end service delay, and migration success rate, while remaining within a small margin of the optimal ILP solution.	10.1109/TNSM.2026.3681690
Qi He, Xiao-Jian Li	Secure state estimation for Cyber-Physical systems under composite attacks	2026	Early Access	Antennas Antenna arrays Kalman filters	This paper is concerned with the secure state estimation problem for Cyber-Physical system under composite attacks, which will simultaneously include adversarial injections and missing sensor measurements. The maximum rank deficiency number of the so-called generalized observability matrix is constructed to formulate the computable correctable conditions, which also improve the existing conclusions developed for the single injection case. Especially, an active defense strategy is established based on the derived necessary and sufficient correctable conditions, thus the success rate of secure estimation for Cyber-Physical system can be effectively improved by about 20% in the experiments. The designed `1-norm decoder outperforms the Robust Kalman filters (RKF) in secure estimation performance against the composite attacks with large-amplitude sparse injections. The estimation errors can be reduced more than 30% in simulations. Compared with the mixed norm decoder, the running time of solving convex optimization problem can be further reduced. The simulations of random control systems and IEEE 14-bus electric power grid are carried out respectively, which verify the feasibility of secure state estimation under composite attacks and the advantages of proposed scheme.	10.1109/TNSM.2026.3683412
Md Arif Hassan, Bui Duc Manh, Cong T. Nguyen, Chi-Hieu Nguyen, Dinh Thai Hoang, Diep N. Nguyen, Nguyen Van Huynh, Dusit Niyato	SBW 3.0: A Blockchain-Enabled Framework for Secure and Efficient Information Management in Web 3.0	2026	Early Access	Jamming Protocols Semantic Web	In this paper, we propose an effective blockchain-enabled information management framework, named Smart Blockchain-based Web 3.0 (SBW 3.0). Our framework aims to handle information within Web 3.0 efficiently, enhance data security and privacy, create new revenue streams, and encourage users to contribute valuable information to websites. To this end, SBW 3.0 employs blockchain technology and smart contracts to manage the decentralized data collection in Web 3.0. Moreover, we introduce a robust consensus mechanism grounded in Delegated Proof-of-Stake (DPoS) to reward user contributions. Furthermore, we develop a non-cooperative game model to examine user behavior in this context and conduct thorough analysis to prove the uniqueness of the Nash equilibrium in our proposed system. Through simulations, we evaluate the performance of SBW 3.0 and analyze the effects of various critical parameters on information contribution. Our results validate the theoretical analysis, showing that the proposed consensus mechanism successfully encourages nodes and users to provide more information, thus overcoming the current limitations of Web 3.0 regarding data decentralization and management.	10.1109/TNSM.2026.3683881
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
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
Venkatesan C, Jeevanantham S, Rebekka B	Credibility-Aware Hierarchical Blockchain Consensus Protocol for Bandwidth-Limited IoT Edge Networks	2026	Early Access	Payloads Military aircraft Space technology	The Internet of Things (IoT) edge networks are widely accompanied by blockchain for the associated benefits namely, transparency, reliability and security. Especially, the consensus mechanism is the integral part of the blockchain to attain the common agreement among the nodes. The practical impediments associated with this synergy are scalability, network overhead, fault tolerance, and computational resource constraints. Thus, to surmount these issues, we propose a novel Heirarchical Proof of Credibility (HPoC) consensus protocol incorporating the randomization of miner through Physical Layer Key (PLK). Unlike existing works, the orchestration of node responsibilities in hierarchy is dynamic based on the node’s malicious behavior during mining process. Further, the Shulze beatpath method-based penalization of the byzantine voting behavior is introduced. The proposed HPoC is evaluated on the Long Range (LoRa) Wide Area Network (WAN) for validating its performance in the presence of resource constraints. The HPoC shortens the consensus latency by 76.4%, 42.5%, 30.5%, 30.98%, 35.84% and 3.7% with respect to Proof of Work (PoW), Practical Byzantine Fault Tolerance (PBFT), RAFT, Reputation Awareness Randomization Consensus (RARC), Reputation-based Secure HotStuff (RSHS), and Proof-of-Physical-Layer-Authentication (PoPLA) respectively. The HPoC surpasses existing schemes by reducing the communication cost significantly while tolerating half of the nodes being byzantine nodes.	10.1109/TNSM.2026.3683555
Zhenzhen Yan, Lizhi Peng, Peiqiang Liu, Yingshuo Bao, Bo Yang	NT-Transformer: A Non-Pretrained Encrypted Network Traffic Classification Model	2026	Early Access	Payloads Military aircraft Space technology	Network traffic classification plays an indispensable role in network management, Quality of Service (QoS), and cybersecurity. With the widespread encryption techniques applied to network traffic, it has become increasingly challenging to classify network traffic into different management groups accurately. In recent years, pre-training Transformer-based models have been successfully applied to Natural Language Processing (NLP), and researchers have also introduced such models into encrypted network traffic analysis. However, besides the similarities of words in NLP and byte codes in network traffic, there exist essential differences between them, which may cause inefficacy of the pretrained model when being applied to new traffic data. In this paper, we propose a non-pretrained encrypted network traffic classification model based on Transformer called NT-Transformer, which can directly learn labeled network traffic features at two levels of granularity, namely, byte level (uni-gram or bi-gram) and flow level (packet size and packet inter-arrival time), without the relatively expensive pre-training procedure of unlabeled data. This method is validated on three public datasets and three sets of recently collected network traffic data. Experimental results indicate that in some scenarios, pretrained models offer limited performance gains when applied to new encrypted network traffic data not encountered during pretraining, and NT-Transformer with uni-gram byte representation outperforms the state-of-the-art models in terms of pushing the F1 score up by 0.25% - 2.24%.	10.1109/TNSM.2026.3683410