TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
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
Awaneesh Kumar Yadav, Madhusanka Liyanage, An Braeken	An Improved and Provably Secure EDHOC Protocol Supporting the Extended Canetti–Krawczyk (eCK) Security Model	2026	Early Access	Aerospace and electronic systems Telemetry Central Processing Unit	Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral Diffie Hellman Over COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. This paper shows that the four authentication modes of the EDHOC protocol are vulnerable in the extended Canetti–Krawczyk (eCK) security model, which is a common security model used in IoT. In addition, also resistance to Distributed Denial of Service (DDoS) attacks is weak. Taking this into account, we propose two new variants of EDHOC. The first variant, EDHOC2, is able to overcome both issues but has a slightly higher cost for communication, computation, storage, and energy consumption. The second variant, EDHOC3, offers only additional protection in the eCK security model and has, on average, similar, even better performance in one authentication mode, compared to EDHOC. Additionally, the Real-Or-Random (ROR) logic and Scyther validation tool are employed to ensure the security of the designed variants. Furthermore, a prototype implementation is conducted to demonstrate the real-time deployment of the designed versions.	10.1109/TNSM.2026.3690530
Lal Verda Cakir, Mehmet Ali Erturk, Mehmet Ozdem, Berk Canberk	Digital Twin-assisted Handover Scheme for Mobile Networks using Generative AI	2026	Early Access	Electromagnetic propagation Propagation constant Radio broadcasting	Handover management in mobile networks is challenged by high latency and reduced reliability in dense deployments and under user mobility. Here, existing schemes improve handover initiation by optimising the candidate handover at the decision time. However, these are applied after a non-negligible delay due to the control-plane signalling. Then, when applied, it may become invalid or degrade performance. To address this, we propose a Digital Twin (DT)-assisted handover scheme that performs predictive execution-time validation prior to the preparation of the Next Generation (NG)-based handover. To this end, the DT-What-If Generator (DT-WIG) is used to emulate short-horizon future network states under uncertainty. Here, the DT-WIG is a spatiotemporal graph generative model that uses variational latent sampling to generate counterfactual post-handover trajectories for the candidate handover decision. Then, the AMF estimates the failure and QoS risks associated with the candidate handover and approves/rejects it via standard-compliant signalling. With this, we form a policy-agnostic mechanism that runs on the underlying handover policy. Consequently, we evaluate performance using ns-3/5G-LENA trace generation and replay-based policy analysis, with OpenAirInterface-based signalling evaluation. The results show that the proposed method reduces the handover failure rate and handover interruption time while improving latency, jitter, throughput, and packet loss.	10.1109/TNSM.2026.3690572
Willie Kouam, Yezekael Hayel, Gabriel Deugoué, Charles Kamhoua	Decoy Allocation against Lateral Movement - A Network Centrality Game Approach	2026	Early Access	Circuits Feedback Network topology	Targeted incidents increasingly threaten internal security with a rise in data breaches and service disruptions. Attackers now employ sophisticated approaches, infiltrating systems for ongoing access to critical information through lateral movement. Detecting and defending against such intrusions is challenging due to commonly exploited specific vulnerabilities. Consequently, various deception techniques have emerged over time, aiming to divert attackers’ attention. In our scenario, attackers use lateral movement within the network to reach a specific target, while defenders strategically deploy decoys to counteract them. Such a dynamic and adversarial interaction is modeled as a one-sided partially observable stochastic game (OS-POSG). Several solutions have been proposed to address this challenge, particularly when the attacker possesses complete knowledge of the network’s topology through the recognition stage. Simultaneously, recent years saw the development of approaches to obscure the attackers’ reconnaissance phase, compelling them to operate without a full comprehension of the network’s structure. We therefore introduce an innovative methodology involving intelligent players who take into account the importance of network devices, assessed by centrality measures, during the decision-making process. This approach aims to improve the effectiveness of the defender’s strategy to counter the attacker’s lateral movement in the network, in the context of step-by-step optimization.	10.1109/TNSM.2026.3689344
Qin Zeng, Dan Qu, Hao Zhang, Yaqi Chen	Neural Collapse-Based Class-Incremental Learning for Encrypted Traffic Classification	2026	Early Access	Payloads Military aircraft Space technology	The rapid evolution of internet technologies has intensified network traffic dynamics due to the emergence of novel encryption protocols, posing significant challenges to traffic classification. Incremental learning, which enables continuous adaptation to emerging tasks, has emerged as a promising approach to enhance the sustainability of encrypted traffic classification. However, existing methods fail to address the substantial feature representation disparities across incremental tasks, resulting in suboptimal model adaptability. Inspired by the Neural Collapse (NC) phenomenonwhich reveals that deep neural networks’ final-layer features collapse to class-mean vectors forming a Simplex Equiangular Tight Frame (ETF) with classifier weights, thereby constituting an optimal geometric structure for classification taskswe propose NCIL-ETC, a Neural Collapse-based Incremental Learning framework for Encrypted Traffic Classification. Our approach employs a pretrained Mamba as the feature extraction backbone, leveraging its linear-complexity computational properties to significantly reduce resource overhead. Simultaneously, we introduce a preallocated ETF classifier that establishes an optimal classification structure covering observed classes. Through feature-classifier alignment constraints during incremental learning, our method promotes both new and historical class features to converge toward ETF vertices, thereby preserving globally optimal category relationships. Extensive experimental evaluations on four public benchmarks demonstrate that NCIL-ETC achieves state-of-the-art performance, surpassing baseline methods in both classification accuracy and incremental learning capability.	10.1109/TNSM.2026.3688767
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
Songshou Dong, Yanqing Yao, Huaxiong Wang, Yining Liu	LCMS: Efficient Lattice-based Conditional Privacy-preserving Multi-receiver Signcryption Scheme for Internet of Vehicles	2026	Early Access	Optical waveguides Optical fibers Broadcasting	Internet of Vehicles (IoV) requires robust security and privacy protection mechanisms to enable trusted traffic information exchange, while also requiring low communication and low computing overhead to meet the real-time requirements of IoV. Existing signcryption schemes suffer from quantum vulnerability, inadequate unlinkability/vehicle anonymity, absence of revocability, poor scalability, inadequate management of malicious entities, and high communication and computational overhead. So we propose an efficient lattice-based conditional privacy-preserving multi-receiver signcryption scheme (LCMS) that systematically addresses these gaps through three core innovations: 1) Privacy preservation is achieved via a pseudonym mechanism integrated with certificateless key generation, which ensures vehicle anonymity and weak unlinkability while preventing malicious key generation center and key escrow; 2) Malicious entity management through dynamic revocability and distributed decryption among roadside units, preventing unilateral message access; and 3) Post-quantum efficiency is achieved by leveraging the Learning With Rounding problem to eliminate expensive Gaussian sampling, combined with ciphertext packing techniques. This reduces time overhead, the size of signcryptexts, and communication overhead, while lowering the overall storage overhead of the scheme through the MP12 trapdoor. Security proofs show LCMS achieves Existential Unforgeability under Adaptive Identity Chosen-Message Attack and Indistinguishability under Adaptive Identity Chosen-Ciphertext Attack in the Random Oracle Model, with rigorously validated resistance against multiple IoV-specific attacks. Experimental results via SageMath implementation demonstrate that our scheme exhibits a smaller signcryptext size and lower signcryption/unsigncryption time compared to existing random lattice-based signcryption schemes. Scalability tests with 300 vehicles and 300 roadside units (RSUs) were completed within 230 seconds. Communication overhead analysis confirms practical feasibility for IEEE 802.11p vehicle communication protocol, and RSU serving capability evaluation under realistic vehicle density (100–200/k m2) and speed (40–60 km/h) further validates system practicality. LCMS provides a quantum-resistant, privacy-preserving, and efficient solution for production IoV.	10.1109/TNSM.2026.3688507
Xinshuo Wang, Baihua Chen, Lei Liu, Yifei Li	Pisces: Fast Loss Recovery for Multipath Transmission in RDMA	2026	Early Access	Payloads Military aircraft Space technology	Conventional Remote Direct Memory Access (RDMA) relies on Priority Flow Control (PFC) to operate on lossless networks. However, as data centers scale, PFC’s drawbacks, such as head-of-line blocking and congestion spreading become increasingly problematic. This study proposes Pisces, a fast packet loss recovery scheme that leverages terminal–network collaboration. Instead of targeting lossless RDMA networks, Pisces enables high-throughput RDMA by efficiently handling loss recovery. To address the inefficient retransmission problems of PFC+Go-Back-N and the challenges of configuring appropriate timeouts for Selective Repeat (SR) in multipath transmission scenarios, Pisces implements Quick Drop Notification (QDN) of packet loss on switches, avoiding bandwidth waste and timeouts. In addition, Pisces RDMA NICs feature on-chip packet buffers to cache in-flight packets, supporting the scalability demands of RDMA in modern data centers. Upon receiving a QDN, lost packets are quickly retrieved from the buffer for retransmission, significantly improving retransmission efficiency and reducing PCIe bandwidth waste caused by cache replacements. This study overcame numerous challenges to implement Pisces prototype, which demonstrated excellent performance. Testbed experiments show that Pisces improves the 99th-percentile FCT by 130×compared to Mellanox CX-6. Large-scale simulations demonstrate that Pisces achieves a maximum reduction of 82.8% in the 99.9th-percentile FCT compared to SR and other state-of-the-art technologies.	10.1109/TNSM.2026.3688038
Shahid Mahmood, Moneeb Gohar, Seok Joo Koh	Globally Integrated Trust Authority (GITA) for Resource-Constrained Edge Devices in IoT and 6G	2026	Early Access	Payloads Filtering Central Processing Unit	The rapid growth of the Internet and the increasing number of edge devices have expanded the cyber-attack surface at the edge layer. Hackers exploit vulnerabilities at various levels of a network by either directly connecting to it or accessing it over the Internet. In both scenarios, edge devices remain a primary target due to their widespread use, limited resources and critical impact. Therefore, securing edge devices is essential to counter both local and global cyber threats. Trust is a key factor in determining the level of protection required for edge devices. It can be used to assess the reliability of other devices before offering or requesting services. Since edge devices are often globally interconnected, trust levels should be verifiable across the Internet and intranet. In this paper, we propose the Globally Integrated Trust Authority (GITA), a framework that distributes verifiable trust values across networks and the Internet while minimizing communication overhead. Experimental results demonstrate that GITA improves the efficiency of trust value distribution and verification among nodes compared to digital certificates, while maintaining the same level of protection.. This approach enables effective identification of malicious and benign nodes, enhancing the precision of malicious node detection locally and globally.	10.1109/TNSM.2026.3687967
Qian Guo, Chunyu Zhang, Xue Xiao, Min Zhang, Zhuo Liu, Danshi Wang	Knowledge-Distilled Time-Series LLM for General Performance Parameter Prediction in Optical Transport Networks	2026	Early Access	Optical fibers Optical waveguides Feeds	In optical transport networks (OTNs), proactive and accurate prediction of key performance parameters plays a crucial role in identifying potential failure of OTN equipment and guiding timely operational interventions, reducing downtime and improving overall system performance. However, the performance parameters in OTNs are complex and diverse. The reliance of existing models structure design on specific configurations limits generalizability across diverse equipment types. Moreover, the high computational resource consumption and memory footprints of these models may lead to inefficiency while hindering practical application and large-scale deployment. To address these challenges, this paper presents a general model, KD-TimeLLM, a cross-application of TimeLLM into OTN failure management, for performance parameter prediction of multiple equipment types in OTNs. By learning from its teacher model TimeLLM via a knowledge distillation strategy, KD-TimeLLM can achieve generalizability in performance parameter prediction while enhancing efficiency. We conducted evaluations across multiple metrics using data sets from different operators and various board types. Results show that KD-TimeLLM outperforms other models in predictive effects including the lowest MSE and MAE across all types of board data along with a scaled_RMSE value below 0.5, the varying number of performance parameters, and zero-shot prediction capability, highlighting its generalizability. Moreover, compared to its teacher model, KD-TimeLLM achieves comparable predictive effects with a significant reduction 99.99% in model parameters and an average reduction of 99.23% in inference time across eight different types of board data. Furthermore, compared to a multiple-model system, total inference time and memory footprint of KD-TimeLLM decreased by 94.79% and 89.65%, highlighting its effectiveness and efficiency.	10.1109/TNSM.2026.3686811
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
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
Mohammad Rasool Momeni, Abdollah Jabbari, Carol Fung	An Efficient and Secure Smart Parking System With Conditional Preservation of Citizens Privacy for Smart Cities	2026	Vol. 23, Issue	Privacy Automated parking Security	The ever-increasing world population and the number of vehicles in use have made it more difficult for drivers to find suitable parking lots in large cities. When public parking is insufficient, private parking space sharing could be a solution to alleviate the problem. In the context of private parking reservation, parking owners and drivers share their parking offers and inquiries that consist of private information, such as identity, parking spot, and desired location. Hence, cyber attacks and data leaks can reveal sensitive information about citizens. Therefore, it could be a major barrier to utilize private parking spots. To address this issue, we propose an efficient, secure, and privacy-preserving smart parking system. We use robust security methods, such as proxy re-encryption and certificateless public-key cryptography, to achieve security. We also employ cutting-edge privacy-enhancing technologies, such as (partially) blind signature and symmetric private information retrieval (SPIR), to preserve citizens’ privacy. Moreover, Shamir’s threshold secret sharing is used to provide conditional privacy. Comprehensive security and privacy analysis using the Random Oracle model and the Scyther tool demonstrates that our design is robust against relevant attacks and effectively protects citizens’ privacy. Ultimately, our performance analysis indicates that the proposed scheme is efficient, lightweight, and feasible. In particular, it achieves an average reduction of approximately 73% in communication overhead.	10.1109/TNSM.2026.3673982
Maolin He, Bin Duo, Ping Huang, Junsong Luo, Dongfen Li, Jun Li	1+1 Protection Transmission for UAV-Enabled Computing Power Networks via Multi-Agent Reinforcement Learning	2026	Vol. 23, Issue	Autonomous aerial vehicles Computer architecture Routing	The rapid proliferation of networked devices and emerging applications has driven the evolution of computing power networks (CPNs) as a key architecture to meet the demands of sixth-generation (6G) communication. However, terrestrial CPNs still face challenges such as limited coverage, vulnerability to wireless impairments, and slow responsiveness in emergency or disaster scenarios. To address these challenges, this paper proposes a UAV-enabled computing power network (UCPN) that leverages the flexible deployment and line-of-sight communication advantages of UAVs to enhance transmission reliability and service continuity. In particular, we design a $1+1$ protection transmission mechanism tailored for UCPNs, in which duplicated task data are forwarded over node-disjoint multi-hop UAV paths and recovered through interval-aware packet scheduling, enabling reliable task delivery under UAV failures and dynamic wireless conditions. Building upon this protection mechanism, we further develop a multi-agent reinforcement learning (MARL)–based node assignment and routing optimization algorithm, referred to as MAPPO-NARO. Unlike existing MARL-based UAV routing or task offloading approaches that primarily focus on single-path transmission or isolated node selection, the proposed algorithm explicitly incorporates $1+1$ protection decisions into the MARL formulation, jointly learning access UAV selection, computing UAV assignment, and fault-tolerant dual-path routing under resource and latency constraints. Simulation results demonstrate that the proposed algorithm achieves lower packet loss, better load balance, and higher reliability compared with the baseline methods. Moreover, when UAV failures occur due to adverse weather conditions, signal interference, or hardware malfunctions, the proposed scheme still maintains high service availability, which indicates that it is well suited for emergency scenarios.	10.1109/TNSM.2026.3672762
Yalan Wu, Zhibing Fang, Jiale Huang, Longkun Guo, Jigang Wu	Vehicle Coalition-Based Incentive Algorithm for Model Deployment and Task Offloading	2026	Vol. 23, Issue	Inference algorithms Quality of service Artificial neural networks	In vehicular edge computing (VEC), efficient strategies for model deployment and task offloading provide tremendous potential to improve quality of services for deep neural network (DNN) inference. However, existing works fail to co-consider selfishness and cooperation of vehicles and characteristic of DNN inference tasks, which results in a bottleneck of performance improvement for DNN inference in VEC. This paper aims to fill this gap by investigating a joint model deployment and task offloading problem for DNN inference in VEC. We formulate a problem with an objective of maximizing social welfare, under constraints of per task accuracy level, per vehicle/roadside unit utility, etc. To solve the problem, an incentive algorithm, called ICA, is proposed based on coalition game and auction mechanism by joint model deployment and task offloading for DNN inference in VEC. Additionally, an incentive algorithm, called IDA, is proposed based on deep reinforcement learning and auction mechanism to maximize the social welfare. Besides, we prove that the proposed algorithms guarantee essential economic properties, i.e., truthfulness and individual rationality. We also prove that the proposed algorithms converge, and that the final coalition structure generated by ICA is Nash-stable. Extensive simulation results show that the proposed algorithms outperform the state-of-the-art methods for all cases, in terms of social welfare.	10.1109/TNSM.2026.3674158
Yonghan Wu, Jin Li, Yi Huang, Weixuan Fan, Qi Zhang, Danshi Wang, Min Zhang	Timeslot-Adaptive and Traffic Load-Aware Routing Computation in Two-Layer LEO Satellite Networks	2026	Vol. 23, Issue	Satellites Routing Low earth orbit satellites	Low Earth orbit (LEO) satellite networks, as a fundamental component of 6G networks, are designed to provide full coverage, low latency, and high quality of service (QoS) for satellite-terrestrial integrated networks (STIN). Topology representations and routing computation in dynamic LEO satellite networks have become key research focuses. However, balancing network dynamics with traffic load remains challenging due to inaccurate topology representation and inefficient routing in existing studies. To address this, we propose a timeslot-adaptive and traffic load-aware routing computation (TA-TLARC) scheme for two-layer LEO satellite networks. The two-layer LEO satellite networks consist of communication layer satellites (CLS) and relay and sensing layer satellites (RSLS). TA-TLARC adaptively adjusts timeslots based on traffic variations and utilizes distributed adjacency matrices for routing computation. Simulation results show that TA-TLARC achieves better performance than existing routing schemes in key QoS metrics such as routing success rate, delay, throughput, and packet loss rate. Although routing hops and power consumption increase within acceptable limits, the routing success rate of TA-TLARC remains 99.6% to 100%. The QoS performance, including delay, throughput, and packet loss rate, is improved by 10% to 40% compared to those of the comparative schemes under different traffic scenarios. The robustness of TA-TLARC is further analyzed and demonstrated to be acceptable under various failure conditions. The results demonstrate that the proposed TA-TLARC effectively addresses routing computation challenges and significantly improves QoS performance in two-layer LEO satellite networks.	10.1109/TNSM.2026.3673268
Jun Li, Yuxuan Chen, Zhiyuan Zhong, Yongcheng Li, Biswanath Mukherjee, Gangxiang Shen	Resource Allocation for Time-Sensitive Services in Centralized Optical and Wi-Fi Access Networks	2026	Vol. 23, Issue	Wireless fidelity Passive optical networks Wireless communication	To satisfy the stringent requirements of emerging broadband services in home networks, a novel Centralized optical and Wi-Fi Access Network (C-WAN) has been proposed within the context of Fiber-to-The-Room (FTTR). In C-WAN, centralized management and control of multiple Wi-Fi access points (APs) deployed in each room are facilitated by relocating portions of Wi-Fi protocols from the APs to a centralized entity. This approach significantly enhances network performance, including throughput and roaming capabilities. However, C-WAN also imposes strict demands on the fronthaul networks, specifically requiring high bandwidth and ultra-low latency. In this context, orthogonal frequency division multiplexing passive optical network (OFDM-PON) emerges as a promising solution to support the C-WAN fronthaul network by allocating dedicated subcarriers to each AP. In C-WAN over OFDM-PON, Wi-Fi stations still contend for access to the wireless channel based on existing Wi-Fi protocols, which may result in prolonged wireless access delays. Consequently, the Quality of Service (QoS) requirements for time-sensitive (TS) services may not be met. Additionally, the variation in maximum Wi-Fi throughput due to the contention-based access mechanism presents a significant challenge for the efficient allocation of optical network resources under stringent delay constraints. To address these issues, we propose a priority-based access mechanism that assigns higher priority to TS services for accessing Wi-Fi channels and obtaining wireless resources. Building on this mechanism, we further develop a Wi-Fi throughput prediction model, which is used to optimize the allocation of optical network resources. Simulation results demonstrate that the proposed scheme can effectively reduce wireless access delay and jitter for TS services, meeting their performance requirements while also improving the utilization of optical network resources.	10.1109/TNSM.2026.3673270
Hyeongjin Kim, Hyunbum Kim, Wooil Kim, Athanasios V. Vasilakos, Paolo Bellavista	Resting Drone-Enabled Enhanced ITS Coverage and V2X Integration Network Management for Urban Mobility Service	2026	Vol. 23, Issue	Drones Roads Vehicle-to-everything	Extending Intelligent Transportation Systems (ITS) toward suburban and peripheral regions is challenging because dense roadside infrastructure is expensive to deploy and underutilized outside peak hours. This paper proposes a V2X-enabled resting drone framework as a dynamic traffic flow management solution for ITS, in which drones equipped with Vehicle-to-Everything (V2X) connectivity are dispatched on demand to congested suburban corridors, provide temporary ITS services, and then land on attachment points to rest in a low-power state when not needed. The framework combines a synthetic multi-city road network, a time-slot–based traffic model, and a load-dependent V2X Quality-of-Service abstraction that maps latency and packet loss into an effective drone availability metric and explicitly captures the impact of non-ideal V2X conditions on control reliability. Within this framework, we develop and evaluate GOLD, a Greedy Overlap-Limited Drone deployment algorithm that prioritizes high-gain, low-overlap locations to maximize effective (overlap-removed) ITS expansion with a limited drone fleet. GOLD is compared against a conventional local threshold-based drone deployment rule that independently scales each road point’s coverage radius with traffic intensity, modeling existing overlap-unaware UAV/ITS extensions. Simulation results over multiple random map and traffic realizations show that GOLD achieves a large fraction of the baseline’s effective coverage with substantially fewer active drones under ideal V2X conditions and maintains its relative advantage when V2X latency and packet loss degrade drone availability, demonstrating that resting drones coordinated by GOLD provide a scalable and robust complement to fixed roadside ITS infrastructure.	10.1109/TNSM.2026.3673324
Mohammed Mahyoub, Wael Jaafar, Sami Muhaidat, Halim Yanikomeroglu	STARS: Stability-Aware SFC Orchestration and Associations in LEO Satellite Networks	2026	Vol. 23, Issue	Satellites Resource management Low earth orbit satellites	Low Earth orbit (LEO) satellite networks present critical challenges for security function chain (SFC) orchestra-tion and associations due to rapid topology changes, resource volatility, and heterogeneous service requirements that render conventional SFC optimization approaches ineffective. To tackle this issue, we introduce here STARS, an optimization framework that fundamentally transforms the sequential time-window optimization for SFC orchestration and satellite association through three techniques: 1) Stability-aware regularization that penal-izes configuration changes across time windows, thus reducing handovers by 54% and security function migrations by 33%; 2) Temporal decoupling that leverages solutions from prior time windows as warm-start seeds and dynamic repairing using real-time visibility constraints; and 3) Hierarchical decoupling that separates satellite association and SFC placement into computationally efficient stages, thus reducing time complexity. Through rigorous formulation as a mixed-integer non-linear programming (MINLP) and simulation-based evaluation, STARS achieves a 57% reduction in optimization solution time, a 7% reduction in the load of deployed security function instances, and efficient CPU utilization (9.81% increase) compared to benchmark schemes. STARS delivers these substantial benefits without any degradation in end-to-end delay. Note that the reported performance values are based on our specific system parameter choices and simulation setup and may not be universally representative. The co-design of stability mechanisms and decoupling strategies establishes STARS as a new paradigm for resilient satellite network optimization, balancing optimality, continuity, and computational tractability under high LEO satellite dynamicity.	10.1109/TNSM.2026.3674391
Francesco Chiti, Simone Morosi, Laura Pierucci	Multiple SDN Controllers Placement for Integrated Satellite/Terrestrial Network	2026	Vol. 23, Issue	Satellites Satellite broadcasting Low earth orbit satellites	The integration of Terrestrial Networks (TN) and Non Terrestrial Networks (NTN) has been explored within the 3GPP standardization forum, and it is now being extended toward discussions on the future 6G vision. An integrated T/NTN is highly heterogeneous and requires different communication protocols and links for each layer, resulting in increased network management and control complexity. The Software Defined Networking (SDN) paradigm can enable unified and efficient T/NTN management, allowing full resource optimization of the satellites, radio access and core network. This paper proposes an optimized SDN-based T/NTN architecture, where Low Earth Orbit (LEO) satellites are dynamically selected to act as multiple SDN controllers if the terrestrial network becomes saturated or unavailable, or to jointly operate with the terrestrial controllers under the coordination of a central terrestrial controller. From this perspective, the number of the SDN controllers and their placement are of paramount importance. A multi-controller placement strategy is evaluated for the integrated T/NTN using the Simulated Annealing (SA) plus Tabu Search methods to search for the optimal solution in terms of average latency and SDN controllers load, while accounting for the frequent topology variations inherent to LEO satellites. In addition, the design of the SDN architecture for an integrated T/NTN system, and in particular the definition of a distributed SDN control plane (CP) across both the terrestrial and satellite segments, including the specific mechanisms required to enable LEO satellites to operate as controllers, is also addressed. Extensive simulations based on realistic T/NTN topologies, specifically, the terrestrial Agis network and the Iridium NEXT satellite constellation, show that the use of multiple controllers in optimized placements both in terrestrial and satellite segments decreases the average latency and balances the load of each controller. In addition, the proposed controller switching policy adopted for the LEO segment helps to avoid frequent reassignments and improves the reliability of the overall integrated system.	10.1109/TNSM.2026.3673404