TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Raffaele Carillo, Francesco Cerasuolo, Giampaolo Bovenzi, Domenico Ciuonzo, Antonio Pescapé	A Federated and Incremental Network Intrusion Detection System for IoT Emerging Threats	2026	Early Access	Training Incremental learning Adaptation models	Ensuring network security is increasingly challenging, especially in the Internet of Things (IoT) domain, where threats are diverse, rapidly evolving, and often device-specific. Hence, Network Intrusion Detection Systems (NIDSs) require (i) being trained on network traffic gathered in different collection points to cover the attack traffic heterogeneity, (ii) continuously learning emerging threats (viz., 0-day attacks), and (iii) be able to take attack countermeasures as soon as possible. In this work, we aim to improve Artificial Intelligence (AI)-based NIDS design & maintenance by integrating Federated Learning (FL) and Class Incremental Learning (CIL). Specifically, we devise a Federated Class Incremental Learning (FCIL) framework–suited for early-detection settings—that supports decentralized and continual model updates, investigating the non-trivial intersection of FL algorithms with state-of-the-art CIL techniques to enable scalable, privacy-preserving training in highly non-IID environments. We evaluate FCIL on three IoT datasets across different client scenarios to assess its ability to learn new threats and retain prior knowledge. The experiments assess potential key challenges in generalization and few-sample training, and compare NIDS performance to monolithic and centralized baselines.	10.1109/TNSM.2026.3675031
Ei Theingi, Lokman Sboui, Diala Naboulsi	Adaptive and Energy-Efficient Deployment of Robotic Airborne Base Stations: A Deep Reinforcement Learning Approach	2026	Early Access		The increasing energy demands of future wireless networks drive the need for intelligent and adaptive deployment strategies. Traditional methods often lack the flexibility required to handle the spatio-temporal fluctuations inherent in modern communication environments. To address this challenge, we investigate the energy-efficient deployment of Robotic Airborne Base Stations (RABSs) in practical scenarios, such as managing sudden traffic surges during large-scale public events and providing emergency coverage in disaster-stricken areas where terrestrial infrastructure is compromised. We propose a novel Deep Reinforcement Learning (DRL)-based framework for an energy-efficient deployment of multiple RABSs. Unlike existing approaches, our framework features both centralized and decentralized Actor-Critic DRL, enabling scalable and adaptive decision-making. The centralized model leverages global network information to optimize the collective deployment of RABSs, while the multi-agent decentralized approach allows RABSs to make independent yet coordinated decisions based on local observations, ensuring scalability in large-scale networks. In addition, we introduce a state-action representation that captures spatio-temporal traffic variations and energy consumption dynamics. Our simulations validate the effectiveness of the proposed framework, demonstrating significant improvements in energy efficiency and adaptability compared to heuristic, Gauss-Markov, and Q-Learning models. Furthermore, comparison with an exhaustive search benchmark confirms that our approach achieves an optimal energy efficiency with significantly lower computational complexity.	10.1109/TNSM.2026.3678488
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
Archana Ojha, Om Jee Pandey, Prasenjit Chanak	Energy-Efficient Network Cut Detection and Recovery Mechanism for Cluster-Based IoT Networks	2026	Early Access	Wireless sensor networks Data collection Energy consumption	Recently, the Internet of Things (IoT) has found widespread applications in diverse fields, including environmental monitoring, Industry 4.0, smart cities, and smart agriculture. In these applications, sensor nodes form Wireless Sensor Networks (WSNs) and collect data from the monitoring environment. Sensor nodes are vulnerable to various faults, including battery depletion and hardware malfunctions. These faulty nodes cut/partition the network into several isolated segments. Therefore, several non-faulty nodes become disconnected from the Base Station (BS)/Sink and are unable to transmit their data to the BS. It is subject to the early demise of the network. Network cuts also significantly degrade overall network performance. Once the network is divided into isolated segments, it is very difficult to detect and collect data from them. Therefore, this paper proposes a Mobile Data Collector (MDC)-based data-gathering approach for WSNs to collect data from isolated segments. This paper proposes a novel MDC-based network cut detection algorithm that identifies the formation of network cuts in WSNs. A network recovery algorithm is also proposed to enable data collection from the isolated segment. Furthermore, this paper proposes a Reinforcement learning Brain Storm Optimization (RLBSO) algorithm for optimal selection of Rendezvous Points (RPs) and optimal MDC path design. It significantly reduces data-gathering time across isolated network segments. The simulation and testbed results show that the proposed approach outperforms existing state-of-the-art approaches in terms of network lifetime, data collection ratio, energy consumption, and latency.	10.1109/TNSM.2026.3677868
Jianwei Zhang, Bowen Cui	Bandwidth-Delay Optimal Segment Routing: Upper-Bound and Lower-Bound Algorithms	2026	Early Access	Routing Optimization Quality of service	Segment routing (SR) is a novel source routing paradigm that enables network programmability. However, existing research rarely considers multicriteria optimization problems in SR networks. Given the critical role of bandwidth and delay in quality-of-service (QoS) routing, we formally define the bandwidth-delay optimal SR (BDoSR) problem for the first time and prove its NP-hardness. By leveraging the label correcting algorithm schema, we design a suite of polynomial-time algorithms, including an upper-bound algorithm (BDoSR-UB) and a lower-bound algorithm (BDoSR-LB). BDoSR-UB enables rapid estimation of the optimal solution while BDoSR-LB is accuracy-adjustable and delivers (near-)optimal feasible solutions. We rigorously analyze their performance gap through carefully constructed network examples, providing deep insights into the adjustable parameters of BDoSR-LB. Finally, we validate our algorithms on realistic network topologies, demonstrating that both BDoSR-UB and BDoSR-LB frequently converge to the optimal solution in practice while offering superior computational efficiency compared to existing approaches.	10.1109/TNSM.2026.3678190
Basharat Ali, Guihai Chen	MIRAGE-DoH: Metamorphic Intelligence and Resilient AI Grid for Autonomous Governance of Encrypted DNS	2026	Early Access	Cryptography Domain Name System Fingerprint recognition	Existing DNS over HTTPS defenses have demonstrated limited resilience against polymorphic traffic shaping, staged tunneling, and adaptive mimicry, largely because they rely on static learning pipelines and rigid cryptographic configurations. MIRAGE-DoH was designed to examine whether adaptive inference, persistent structural encoding, and calibrated cryptographic agility could be integrated into a deployable and measurable encrypted DNS control architecture. The framework combined flow-level Cognitive MetaAgents capable of internal reconfiguration, Topological Memory Networks that preserved stable geometric irregularities across temporal windows, and Metamorphic Cryptographic Shards that adjusted key encapsulation policies according to empirically calibrated threat severity. A Causal Counterfactual Environment modeled constrained attacker decision pathways, while Spectral Game Intelligence analyzed flow interaction graphs to anticipate structural attack transitions.Evaluation on extended CIC-DoH2023 and Gen-C-DDD-2022 datasets was conducted under fixed flow-level decision intervals with explicit accounting for synchronization overhead, spectral graph construction cost, and cryptographic rotation latency. Cross-dataset experiments yielded a mean detection accuracy of 97.8% with a 0.41% false positive rate, sustaining median inference latency of 62μs and cryptographic morph latency of 3.7 ms under load. Quantum-assisted inference was assessed through bounded simulations, indicating constrained information gain within the adopted lattice-based configuration, without asserting unconditional post-quantum immunity. These results demonstrated that adaptive encrypted DNS governance can be empirically grounded, operationally bounded, and stress-evaluated without reliance on unqualified claims of perfect security.	10.1109/TNSM.2026.3677474
Henghua Zhang, Jue Chen, Haidong Peng, Junru Chen	MAT4PM: Machine Learning-Guided Adaptive Threshold Control for P4-based Monitoring in SDNs	2026	Early Access	Monitoring Switches Accuracy	This paper presents MAT4PM, a P4-based proactive monitoring framework designed for Software-Defined Networking (SDN). This is the first monitoring framework that combines Programmable Data Plane (PDP) capabilities for event-driven data collection with control plane intelligence for real-time threshold optimization. The architecture consists of a lightweight P4-based monitoring module deployed at the switch, a Machine Learning (ML) inference engine running at the controller, and a P4Runtime feedback channel for real-time threshold updates. Traffic features are leveraged to predict optimal monitoring thresholds, which are then synchronized with the data plane. A composite cost function is introduced to jointly consider monitoring error and communication overhead, guiding the model toward a balanced trade-off between accuracy and efficiency. Experimental evaluation on BMv2 software switches demonstrates that, compared to static threshold strategies, MAT4PM reduces monitoring error to 7.0% and achieves a 5.6% reduction in overall cost, while maintaining sub-millisecond inference latency and minimal resource consumption. These results demonstrate the practical viability and scalability of MAT4PM in SDN environments.	10.1109/TNSM.2026.3677416
Yanli Liu, Yue Pang, Yidi Wang, Shengnan Li, Jin Li, Min Zhang, Danshi Wang	Developing A Domain-Specific LLM for Optical Networks: A Reinforcement Learning-Based Fine-Tuning Framework	2026	Early Access	Optical fiber networks Cognition Accuracy	Optical networks serve as the backbone of modern communication infrastructure, where efficient operation and maintenance (O&M) are essential for ensuring reliable and high-speed data services. However, traditional network O&M face persistent challenges, including high labor costs, delayed response time, and difficulties in processing massive and complex network data. Although large language models (LLMs) have demonstrated strong capabilities in text understanding, generation, and reasoning, their direct application in optical network O&M is limited by domain-specific knowledge barriers, inherent reasoning biases, and insufficient performance in complex multi-step tasks. To address this issue, this study develops a domain-adaptation and system-implementation framework that applies two established reinforcement learning-based fine-tuning methods (RLHF and ReFT) to construct domain-specialized LLMs for optical network O&M tasks. In the context of log analysis, RLHF achieves improvements of 1.64 points in accuracy, 1.02 points in content richness, and a notable 10-point increase in interactivity over supervised fine-tuning. In alarm localization, ReFT achieves accuracy improvements of 2%–13% across four reasoning tasks. The extensive tests not only demonstrate the practical value of RL-based fine-tuning in enhancing alignment and reasoning for domain-specific applications, but also provides a practical methodology and implementation reference for applying reinforcement learning-based LLM adaptation in optical network O&M environments.	10.1109/TNSM.2026.3676522
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
Junyan Guo, Shuang Yao, Yue Song, Le Zhang, Xu Han, Liyuan Chang	EF-CPPA: Escrow-Free Conditional Privacy-Preserving Authentication Scheme for Real-Time Emergency Messages in Smart Grids	2026	Early Access	Authentication Smart grids Security	Timely and secure emergency message delivery is critical to resilient smart-grid operation and rapid disturbance response. However, existing schemes remain inadequate, leaving smart grids vulnerable to security and privacy threats and causing verification bottlenecks, particularly when nonlinear emergency measurements cannot be homomorphically aggregated, which prevents bandwidth-efficient in-network aggregation and scalable batch verification. We propose EF-CPPA, an escrow-free, conditional privacy-preserving authentication scheme for real-time emergency messaging in smart grids. EF-CPPA enables smart meters to deliver authenticated emergency messages to the CC via power gateways verifiable as legitimate relays, while ensuring the confidentiality, integrity, and unlinkability of embedded nonlinear measurements. EF-CPPA further provides conditional anonymity with accountable tracing, as well as origin authentication, intra-domain verification, and scalable batch verification under bursty multi-meter messaging. An ECDLP-based escrow-free key-generation mechanism reduces reliance on the CC and enables efficient node joining and revocation. Security analysis shows that EF-CPPA achieves existential unforgeability under chosen-message attacks (EUF-CMA) and satisfies the stated security and privacy requirements. Performance evaluation demonstrates low computational, communication, energy, and node-management overhead, making EF-CPPA suitable for security-critical, time-sensitive smart-grid emergency messaging.	10.1109/TNSM.2026.3672754
Amin Mohajer, Abbas Mirzaei, Mostafa Darabi, Xavier Fernando	Joint SLA-Aware Task Offloading and Adaptive Service Orchestration with Graph-Attentive Multi-Agent Reinforcement Learning	2026	Early Access	Quality of service Resource management Observability	Coordinated service offloading is essential to meet Quality-of-Service (QoS) targets under non-stationary edge traffic. Yet conventional schedulers lack dynamic prioritization, causing deadline violations for delay-sensitive, lower-priority flows. We present PRONTO, a multi-agent framework with centralized training and decentralized execution (CTDE) that jointly optimizes SLA-aware offloading and adaptive service orchestration. PRONTO builds on Twin Delayed Deep Deterministic Policy Gradient (TD3) and incorporates spatiotemporal, topology-aware graph attention with top-K masking and temperature scaling to encode neighborhood influence at linear coordination cost. Gated Recurrent Units (GRUs) filter temporal features, while a hybrid reward couples task urgency, SLA satisfaction, and utilization costs. A priority-aware slicing policy divides bandwidth and compute between latency-critical and throughput-oriented flows. To improve robustness, we employ stability regularizers (temporal smoothing and confidence-weighted neighbor alignment), mitigating action jitter under bursts. Extensive evaluations show superior QoS and channel utilization, with up to 27.4% lower service delay and over 18% higher SLA Satisfaction Rate (SSR) compared with strong baselines.	10.1109/TNSM.2026.3673188
Long Chen, Yukang Jiang, Zishang Qiu, Donglin Zhu, Zhiquan Liu, Zhenzhou Tang	Toward Energy-Saving Deployment in Large-Scale Heterogeneous Wireless Sensor Networks for Q-Coverage and C-Connectivity: An Efficient Parallel Framework	2026	Vol. 23, Issue	Wireless sensor networks Metaheuristics Sensors	Efficient deployment of thousands of energy-constrained sensor nodes (SNs) in large-scale wireless sensor networks (WSNs) is critical for reliable data transmission and target sensing. This study addresses the Minimum Energy Q-Coverage and C-Connectivity (MinEQC) problem for heterogeneous SNs in three-dimensional environments. MnPF (Metaheuristic–Neural Network Parallel Framework), a two-phase method that can embed most metaheuristic algorithms (MAs) and neural networks (NNs), is proposed to address the above problem. Phase-I partitions the monitoring region via divide-and-conquer and applies NN-based dimensionality reduction to accelerate parallel optimization of local Q-coverage and C-connectivity. Phase-II employs an MA-based adaptive restoration strategy to restore connectivity among subregions and systematically assess how different partitioning strategies affect the number of restoration steps. Experiments with four NNs and twelve MAs demonstrate efficiency, scalability, and adaptability of MnPF, while ablation studies confirm the necessity of both phases. MnPF bridges scalability and energy efficiency, providing a generalizable approach to SN deployment in large-scale WSNs.	10.1109/TNSM.2025.3640070
Junfeng Tian, Junyi Wang	D-Chain: A Load-Balancing Blockchain Sharding Protocol Based on Account State Partitioning	2026	Vol. 23, Issue	Sharding Blockchains Load modeling	Sharding has become one of the key technologies for improve the performance of blockchain systems. However, the imbalance of transaction load between shards caused by extremely hot accounts leads to an imbalance in the utilization of system resources as well as the increase of cross-shard transactions with the number of shards limits the expansion of sharding systems, and sharding systems do not achieve the desired performance improvement. We propose a new blockchain sharding system called D-Chain. D-Chain splits and distributes the state of extremely hot accounts into multiple shards, allowing transactions for an account can be processed in multiple shards, thus balancing the load between shards and reducing the number of cross-shard transactions. We have implemented a prototype of D-Chain, and evaluated its performance using real-world Ethereum transactions. Experimental results show that the proposed system achieves a more balanced shard load and outperforms other baselines in terms of throughput, transaction latency, and cross-shard transaction ratio.	10.1109/TNSM.2025.3640097
Jiahe Xu, Chao Guo, Moshe Zukerman	Virtual Network Embedding for Data Centers With Composable or Disaggregated Architectures	2026	Vol. 23, Issue	Servers Data centers Greedy algorithms	Virtual Network Embedding (VNE) is an important problem in network virtualization, involving the optimal allocation of resources from substrate networks to service requests in the form of Virtual Networks (VNs). This paper addresses a specific VNE problem in the context of Composable/Disaggregated Data Center (DDC) networks, characterized by the decoupling and reassembly of different resources into resource pools. Existing research on the VNE problem within Data Center (DC) networks primarily focuses on the Server-based DC (SDC) architecture. In the VNE problem within SDCs, a virtual node is typically mapped to a single server to fulfill its requirements for various resources. However, in the case of DDCs, a virtual node needs to be mapped to different resource nodes for different resources. We aim to design an optimization method to achieve the most efficient VNE within DDCs. To this end, we provide an embedding scheme that acts on each arriving VN request to embed the VN with minimized power consumption. Through this scheme, we demonstrate that we also achieve a high long-term acceptance ratio. We provide Mixed Integer Linear Programming (MILP) and scalable greedy algorithms to implement this scheme. We validate the efficiency of our greedy algorithms by comparing their performance against the MILP for small problems and demonstrate their superiority over baseline algorithms through comprehensive evaluations using both synthetic simulations and real-world Google cluster traces.	10.1109/TNSM.2025.3639958
Dongyi Han, Qiang Zhi	DGDPFL: Dynamic Grouping and Privacy Budget Adjustment for Federated Learning in Networked Service Management	2026	Vol. 23, Issue	Privacy Data privacy Adaptation models	In federated learning (FL), effective client and privacy management are crucial for maintaining system efficiency and model performance. However, existing FL frameworks face challenges such as imbalanced client contributions, inefficient resource allocation, and static privacy mechanisms, making scalable client management and adaptive privacy control essential. To address these issues, this paper proposes DGDPFL, a novel FL framework that enhances client selection, resource management, and privacy control through dynamic client grouping and adaptive privacy budgeting. The framework optimizes client management by clustering participants based on device capabilities, bandwidth, and data quality, enabling efficient resource allocation. A contribution-aware selection mechanism ensures fair participation, while a privacy-aware control strategy dynamically adjusts privacy budgets based on model similarity, improving both privacy guarantees and learning performance. We evaluate DGDPFL in real-world and simulated environments. On CIFAR-10 and Fashion-MNIST, DGDPFL achieves 77.83% and 88.35% test accuracy respectively with only 10–20 clients and 40 training rounds, outperforming state-of-the-art baselines by up to 12.36%. On audio datasets FSDD and SAD, the accuracy reaches up to 97%, validating the method’s robustness across modalities. Experimental results demonstrate that DGDPFL outperforms existing approaches by achieving higher model accuracy, improved system efficiency, and better privacy-utility balance. These findings highlight DGDPFL’s effectiveness in managing clients and privacy in FL environments.	10.1109/TNSM.2025.3640713
Eduardo Castilho Rosa, Daniel Nunes Corujo, Flávio de Oliveira Silva	CoFIB: A Memory-Efficient NDN FIB Design for Programmable Edge Switches	2026	Vol. 23, Issue	Data structures Random access memory Pipelines	Designing high-performance and memory-efficient data structures for the Forwarding Information Base (FIB) in Named-Data Networking (NDN) is a challenging task. Since the FIB size is orders of magnitude larger than IP routing tables, scaling it to store millions of prefixes in SRAM/TCAM memory in programmable switches is an open problem. To address this issue, we propose a Compressed FIB data structure called CoFIB, designed to run on edge programmable switches in an SDN-based environment. The CoFIB is a collection of exact-match tables placed in an optimized manner in both ingress and egress pipelines. We propose a LNPM algorithm that carefully recirculates packets in the pipeline. We also introduce the concept of canonical name prefixes to reduce memory footprint and propose an algorithm to extract canonical prefixes from the Routing Information Base (RIB). Experimental results show that CoFIB can compress memory up to $16.58{\times }$ compared to the state-of-the-art, with no significant impact on throughput compared to hardware-based solutions. Additionally, our proposed table placement optimization for LNPM increases the number of packets processed at a line rate by 23.17% compared to the linear table placement approach using a large NDN name dataset.	10.1109/TNSM.2025.3641145
Sajjad Alizadeh, Majid Khabbazian	On Scalability Power of Payment Channel Networks	2026	Vol. 23, Issue	Topology Routing Network topology	Payment channel networks have great potential to scale cryptocurrency payment systems. However, their scalability power is limited as payments occasionally fail in these networks due to various factors. In this work, we study these factors and analyze their imposing limitations. To this end, we propose a model where a payment channel network is viewed as a compression method. In this model, the compression rate is defined as the ratio of the total number of payments entering the network to the total number of transactions that are placed on the blockchain to handle failed payments or (re)open channels. We analyze the compression rate and its upper limit, referred to as compression capacity, for various payment models, channel-reopening strategies, and network topologies. For networks with a tree topology, we show that the compression rate is inversely proportional to the average path length traversed by payments. For general networks, we show that if payment rates are even slightly asymmetric and channels are not reopened regularly, a constant fraction of payments will always fail regardless of the number of channels, the topology of the network, the routing algorithm used and the amount of allocated funds in the network. We also examine the impact of routing and channel rebalancing on the network’s compression rate. We show that rebalancing and strategic routing can enhance the compression rate in payment channel networks where channels may be reopened, differing from the established literature on credit networks, which suggests these factors do not have an effect.	10.1109/TNSM.2025.3640098
Sheng-Wei Wang, Show-Shiow Tzeng	An Accurate and Efficient Analytical Model for Security Evaluation of PoW Blockchains With Multiple Independent Selfish Miners	2026	Vol. 23, Issue	Blockchains Analytical models Accuracy	Selfish mining poses significant security challenges to Proof-of-Work (PoW) blockchains by allowing strategic miners to gain disproportionate rewards through protocol deviation. While the impact of a single selfish miner has been extensively studied, the security implications of multiple independent selfish miners remain insufficiently understood. This paper presents an accurate and efficient analytical model for security evaluation of PoW blockchains under multiple independent selfish mining behaviors. The blockchain dynamics are modeled as a Markov chain with a novel state aggregation approximation, enabling closed-form estimation of miner rewards. Numerical results show that the proposed model achieves high accuracy, with deviations typically less than 5.09% compared to simulations in a blockchain with two selfish miners. In a blockchain with more than two selfish miners, the proposed analytical model yields more accuracy approximation leading to less than 2% error. We also propose a truncation mechanism to reduce the number of states in the proposed Markov chain. Numerical results show that the proposed analytical model with truncation significantly reduce the computation time while the accuracy is still maintained. Two use cases are presented: determining the profitable threshold of total selfish mining power and analyzing reward dis-proportionality between strong and weak selfish miners. The proposed model provides a practical framework for quantifying incentive-driven security risks and evaluating their impact on blockchain fairness and decentralization.	10.1109/TNSM.2025.3637840
Xi Xu, Yang Yang, Wei Huang, Songtao Guo, Guiyan Liu	VNF-FG Placement and Admission Control in SDN and NFV-Enabled IoT Networks: A Hierarchical Deep Reinforcement Learning Method	2026	Vol. 23, Issue	Admission control Feature extraction Virtual links	Software Defined Networking (SDN) and Network Function Virtualization (NFV) are expected to provide greater flexibility and manageability for next-generation IoT networks. In this context, network services should be modeled as Virtual Network Function Forwarding Graphs (VNF-FGs). A key challenge is efficient allocation of resources for sequentially arriving network service requests, a process known as VNF-FG placement. Most existing algorithms either manually or partially extract features from the physical network and VNF-FG or adopt a greedy approach, allocating resources as long as a feasible solution exists, which may over-allocate resources to VNF-FG requests, ultimately harming infrastructure providers’ long-term revenue. In this paper, we propose a VNF-FG placement and admission control algorithm based on hierarchical reinforcement learning, called EAC. It consists two levels of agents: a coarse-level agent that generates placement strategies and rejects requests with no feasible placement strategies, and a refine-level agent that implements admission control and rejects requests that are detrimental to long-term revenue. To fully capture the topological features of both the physical network and the VNF-FG, we employ a customized Graph Attention Network (GAT) that incorporates link feature awareness and enables deeper exploration. To fully explore historical temporal information for admission control, we construct state triples and feed them into a Recurrent Neural Network (RNN). Using Proximal Policy Optimization (PPO) as the foundational training algorithm, the corresponding agents are trained hierarchically. Extensive experimental results demonstrate that the proposed EAC algorithm outperforms existing state-of-the-art solutions in terms of acceptance rate, revenue-to-cost ratio, and long-term average revenue.	10.1109/TNSM.2025.3640927
Weichao Ding, Zhou Zhou, Qi Min, Fei Luo, Wenbo Dong, Hengrun Zhang	VDSV: Client Selection in Federated Learning Based on Value Density and Secondary Verification	2026	Vol. 23, Issue	Convergence Training Data models	Client selection has been widely considered in Federated Learning (FL) to reduce communication overhead while ensuring proper convergence performance. Due to data heterogeneity in FL, a representative subset of participants should take into account both intra- and inter-client diversity. While existing works usually emphasize on one of them, this paper proposes a VDSV (client selection based on Value Density and Secondary Verification) framework, which optimizes the client selection strategy from both sides. Therein, intra- and inter-client diversity are respectively measured based on a designed client data score as well as gradient distance and direction. Afterwards, a client selection model is established based on a proposed metric, called client value density. Besides, a secondary validation method is developed to dynamically tweak the current client selection and model aggregation strategies. The general idea of the above design is based on the theoretical convergence analysis and the observation that the client contribution to the global model can get changed throughout the learning process. The experimental results demonstrate that VDSV can achieve higher convergence rates and ensure comparable model performance. In specific, our method can reduce the communication rounds by an average of 37.88%, which saves noticeable communication overhead.	10.1109/TNSM.2025.3636990