TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Francesco Chiti, Simone Morosi, Laura Pierucci	Multiple SDN Controllers Placement for Integrated Satellite/Terrestrial Network	2026	Early Access	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
Beibei Li	B-TWGA: A Trusted Gateway Architecture Based on Blockchain for Internet of Things	2026	Early Access	Internet of Things Blockchains Security	Internet of Things (IoT) terminals are commonly used for data sensing and edge control. The communication links between these hardware devices are critical points that are vulnerable to security attacks. Moreover, these links are usually composed of resource-constrained nodes that cannot implement strong security protections. To address these security threats, we introduce a Blockchain-based Trustworthy Gateway Architecture (B-TWGA), which does not rely on additional thirdparty management institutions or hardware facilities, nor does it require central control. Our proposal further considers the possibility of Denial of Service (DoS) attacks in blockchain transactions, ensuring secure storage and seamless interaction within the network. The proposed scheme offers advantages such as tamper-proofing, protection against malicious attacks, and reliability while maintaining operational simplicity. Experimental results demonstrate that B-TWGA maintains stable trust levels even when 40% of the network nodes are malicious, effectively mitigates trust degradation caused by vote-stuffing and switch attacks, and ensures high transaction processing performance, achieving an average throughput of 97.55% for storage transactions with practical response times below 0.7s for typical trust file sizes.	10.1109/TNSM.2026.3671208
Shi Dong, Fuxiang Zhao, Longhui Shu, Junjie Huang	Android Zero-Day Guard: Zero-Shot Malware Detection Using Deep Learning and Generative Models	2026	Early Access	Malware Feature extraction Accuracy	This paper proposes an Android-oriented zero-day malware detection method named ”Android Zero-Day Guard.” By integrating deep neural networks with zero-shot learning, this approach is capable of identifying emerging threats without prior exposure to malicious samples. The method converts APK files into images and extracts deep features, enabling effective capture of behavioral malware patterns. Experimental results demonstrate that the proposed method achieves a precision of 94.93%, a recall of 93.75%, and an F1-score of 94.28% across multiple malware families. Without relying on dynamic analysis, it exhibits strong detection capability and generalization performance, making it well-suited for the early identification of emerging threats. While the model performs strongly on benchmark datasets, continuous validation on the latest families is essential for deployment in a rapidly evolving threat landscape.	10.1109/TNSM.2026.3671305
Ebrima Jaw, Moritz Müller, Cristian Hesselman, Lambert Nieuwenhuis	Reproducibility Study and Assessment of the Evolution of Serial BGP Hijacking Events	2026	Early Access	Internet Routing Border Gateway Protocol	The Border Gateway Protocol (BGP) is the Internet’s most crucial protocol for efficient global connectivity and traffic routing. However, BGP is well known to be susceptible to route hijacks and leaks. Route hijacks are the intentional or unintentional illegitimate announcements of network resources that can compromise the confidentiality, integrity, and availability of communication systems. In the past, the so-called “serial hijackers” have hijacked Internet resources multiple times, some lasting for several months or years. So far, only the paper “Profiling BGP Serial Hijackers” has explicitly focused on these repeat offenders, and it dates back to 2019. Back then, they had to process large amounts of BGP announcements to find a few potential serial hijackers. In this paper, we revisit the profiling of serial hijackers. We reproduced the 2019 study and showed that we can identify potential offenders with less data while achieving similar accuracy. Our study confirms that there has been no significant increase in the evolution of serial hijacking activities in the last five years. We then extend their research, further analyze the characteristics of the serial hijackers, and show that most of the alleged serial hijackers are still active on the Internet. We also find that 22.9% of the hijacks violated RPKI objects but were still widely propagated, and that even MANRS participants were among the propagating networks.	10.1109/TNSM.2026.3671613
Shaohui Gong, Luohao Tang, Jianjiang Wang, Quan Chen, Cheng Zhu	A Key Node Set Analysis Method For Regional Service Denial In Mega-Constellation Networks	2026	Early Access	Satellites Measurement Analytical models	Mega-constellation networks (MCNs) face the significant threats of regional service denial attacks. To improve the robustness of regional services in MCNs against such attacks, a cost-effective approach is to identify key node sets for targeted protection efforts. This paper formally defines the key node set analysis problem for regional service denial in MCNs and develops a comprehensive solution framework. First, we develop a regional service capability analysis model that considers the dynamic collaboration of multiple satellites within regional communication service scenarios in MCNs, alongside a temporal network model for their collaborative relationships. Next, we design a multi-satellite criticality metric that quantifies the multi-dimensional impacts of satellite node set failures on regional service capabilities. Building on these, we construct a mixed-integer programming-based key node set analysis model to achieve precise identification of key node sets. Finally, simulation experiments are conducted to verify and analyze the proposed methods, providing insights to enhance the robustness of regional services in MCNs.	10.1109/TNSM.2026.3672157
Shankar K. Ghosh, Souvik Deb, Rishi Balamurugan, AB Santhosh	Exploring the conditional effect of RLF on handover failure based on ns-3 under stochastic channel condition	2026	Early Access	Handover Correlation Long Term Evolution	A Key component of Handover failure (HOF) in Fifth generation (5G) cellular network is the underlying radio link failure (RLF) event; existing model based analyses of HOF have not adequately explored this dependency. Moreover, HOF as a function of user mobility necessitates models that incorporate spatio-temporal correlation that has been largely ignored. In this work, based on ns-3 simulation, we characterize the relationship between RLF and HOF considering the effects of handover parameters (i.e., hysteresis (Hys), time-to-trigger (TTT), A2 threshold, A4 threshold) and RLF parameters (i.e., out-of-synch threshold (Qout), out-of-synch indication (N310), insynch indication (N311) and RLF timer (T310)) for correlated RSRP samples. The study has been carried out for different kinds of handovers in Non-Standalone (NSA) deployment of 5G. Our study reveals that optimal settings of handover parameters and RLF parameters to optimize HOF are actually constrained by the correlation characteristics of the prevailing channels. Comparison of simulation results with an existing semi-analytic model based analysis shows the novelty of the proposed ns-3 simulation methodology in capturing the cumulative impact of all the aforementioned factors in causing HOF. This study will help the mobile operators in choosing optimal RLF and handover parameters to minimize HOF under different UE velocities and fading scenarios.	10.1109/TNSM.2026.3672646
Rong Jiang, Yulin Li, Xuetao Pu, Xueke Wang, Yukun Xue	A Contract Data Sharing Model Based on Consortium Blockchain and Local Differential Privacy	2026	Early Access	Differential privacy Blockchains Computational modeling	Privacy-preserving and sharing for contract data are crucial for enterprise collaboration. However, current approaches combining blockchain and differential privacy face challenges including high computational costs, low data processing efficiency, and trust issues in decentralized privacy mechanisms. To address this, we propose a federated blockchain model based on multi-dimensional local differential privacy. A Multi-Dimensional Randomized Response (MDRR) mechanism is designed to protect privacy while retaining internal attribute correlations. Secondly, we construct a hybrid computation mechanism that integrates consortium blockchain and differential privacy, enabling on-chain scheduling with off-chain efficient computation, thereby significantly reducing computational overhead. Furthermore, we introduce a Trust-Utility Synergistic Optimization (TUSO) mechanism to enhance reliability by combining trust scores and utility. Experiments show superior accuracy, reduced error, and improved efficiency.	10.1109/TNSM.2026.3672462
Wenxue Hu, Lei Sun, Zhangchao Ma, Rong Huang, Yushan Pei, Jianquan Wang	A Novel Time-Window Scheduling Algorithm With Network Calculus Model in Time-Sensitive Networking	2026	Early Access	Job shop scheduling Optimization Switches	Traffic scheduling plays a critical role in Time-Sensitive Networking (TSN) for ensuring high reliability and deterministic latency. In this paper, we propose a novel window-based scheduling approach for the Time-Aware Shaper (TAS). By allowing packets to wait in egress queues before forwarding, our approach relaxes the strict timing constraints imposed by existing packet-based schedulers. We employ a generalized Network Calculus (NC) framework built on an End-to-End (E2E) network model, to analyze the upper-bound latency, which is then used to assess the schedulability of Time-Critical (TC) traffic. Inspired by the Proportional–Integral–Derivative (PID) closed-loop control architecture, we introduce an Incremental PID-based Search (IPS) algorithm to optimize schedulability, where the P, I, and D terms are leveraged to scale update steps, maintain search momentum, and dampen the oscillations, respectively. To accommodate various traffic classes, throughput constraints for non-TC traffic are incorporated as bounds on window lengths. Simulation experiments were performed on a multi-node network topology carrying large traffic volumes. Under optimal PID settings, the proposed IPS algorithm was evaluated against the well-validated Simulated Annealing (SA) method under a unified scheduling framework with identical decision variables and constraints to ensure a fair comparison. Results show that IPS consistently achieves higher schedulability and requires fewer iterations for flow counts ranging from 100 to 600. Furthermore, a real-time simulation platform based on OMNeT++ was developed, and the effectiveness of the proposed wait-allowed scheduling model was validated through optimized GCL configurations.	10.1109/TNSM.2026.3673031
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
Ying-Chin Chen, Chit-Jie Chew, Wei-Bin Lee, Iuon-Chang Lin, Jun-San Lee	IROVF:Industrial Role-Oriented Verification Framework for safeguarding manufacture line deployment	2026	Early Access	Security Manufacturing Standards	Traditionally, industrial control systems operate in isolated networks with proprietary solutions. As smart factories and digital twins have become inevitable with AI advancement, the rapid adoption of Industrial Internet of Things (IIoT) devices has significantly increased cybersecurity risks. More precisely, the complexity of industrial environments, which includes production processes and device roles, creates substantial challenges for secure deployment. The authors introduce a bottom-up, industrial role-oriented verification framework (IROVF) for manufacturing line deployment. IROVF incorporates SCADA's MTU and RTU components, which are mapped to distinct device roles. This provides authentication and least-privilege principles that are tailored to factory environments. The proposed framework designs an alarm strategy, which can be helpful to detect and report potential operational disruptions during runtime, thus minimizing impact on system availability. Experimental results demonstrate the superior security coverage of the proposed framework compared to existing research, while a comprehensive application scenario validates its practical applicability. The scalable security parameters of IROVF allow organizations to select appropriate security levels based on their specific requirements. IROVF provides an effective security solution for modern industrial control systems during deployment phases.	10.1109/TNSM.2026.3672975
Pietro Spadaccino, Paolo Di Lorenzo, Sergio Barbarossa, Antonia M. Tulino, Jaime Llorca	SPARQ: An Optimization Framework for the Distribution of AI-Intensive Applications under Non-Linear Delay Constraints	2026	Early Access	Computational modeling Delays Resource management	Next-generation real-time compute-intensive applications, such as extended reality, multi-user gaming, and autonomous transportation, are increasingly composed of heterogeneous AI-intensive functions with diverse resource requirements and stringent latency constraints. While recent advances have enabled very efficient algorithms for joint service placement, routing, and resource allocation for increasingly complex applications, current models fail to capture the non-linear relationship between delay and resource usage that becomes especially relevant in AI-intensive workloads. In this paper, we extend the cloud network flow optimization framework to support queueing-delay-aware orchestration of distributed AI applications over edge-cloud infrastructures. We introduce two execution models, Guaranteed-Resource (GR) and Shared-Resource (SR), that more accurately capture how computation and communication delays emerge from system-level resource constraints. These models incorporate M/M/1 and M/G/1 queue dynamics to represent dedicated and shared resource usage, respectively. The resulting optimization problem is non-convex due to the non-linear delay terms. To overcome this, we develop SPARQ, an iterative approximation algorithm that decomposes the problem into two convex sub-problems, enabling joint optimization of service placement, routing, and resource allocation under nonlinear delay constraints. The modeling approach is validated against real-world data. Simulation results demonstrate that the SPARQ not only offers a more faithful representation of system delays, but also substantially improves resource efficiency and the overall cost-delay tradeoff compared to existing state-of-the-art methods.	10.1109/TNSM.2026.3673194
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	Early Access	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
Fernando Martinez-Lopez, Lesther Santana, Mohamed Rahouti, Abdellah Chehri, Shawqi Al-Maliki, Gwanggil Jeon	Learning in Multiple Spaces: Prototypical Few-Shot Learning with Metric Fusion for Next-Generation Network Security	2026	Early Access	Measurement Prototypes Extraterrestrial measurements	As next-generation communication networks increasingly rely on AI-driven automation, ensuring robust and secure intrusion detection becomes critical, especially under limited labeled data. In this context, we introduce Multi-Space Prototypical Learning (MSPL), a few-shot intrusion detection framework that improves prototype-based classification by fusing complementary metric-induced spaces (Euclidean, Cosine, Chebyshev, and Wasserstein) via a constrained weighting mechanism. MSPL further enhances stability through Polyak-averaged prototype generation and balanced episodic training to mitigate class imbalance across diverse attack categories. In a few-shot setting with as few as 200 training samples, MSPL consistently outperforms single-metric baselines across three benchmarks: on CICEVSE Network2024, AUPRC improves from 0.3719 to 0.7324 and F1 increases from 0.4194 to 0.8502; on CICIDS2017, AUPRC improves from 0.4319 to 0.4799; and on CICIoV2024, AUPRC improves from 0.5881 to 0.6144. These results demonstrate that multi-space metric fusion yields more discriminative and robust representations for detecting rare and emerging attacks in intelligent network environments.	10.1109/TNSM.2026.3665647
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	Early Access	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
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	Early Access	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
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	Early Access	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
Mohammad Rasool Momeni, Abdollah Jabbari, Carol Fung	An Efficient and Secure Smart Parking System with Conditional Preservation of Citizens Privacy for Smart Cities	2026	Early Access	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
Mohammed Mahyoub, Wael Jafar, Sami Muhaidat, Halim Yanikomeroglu	STARS: Stability-Aware SFC Orchestration and Associations in LEO Satellite Networks	2026	Early Access	Satellites Resource management Low earth orbit satellites	Low Earth orbit (LEO) satellite networks present critical challenges for security function chain (SFC) orchestration 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 penalizes 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
Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak	Anomaly Resilient Temporal QoS Prediction using Hypergraph Convoluted Transformer Network	2026	Early Access	Quality of service Accuracy Transformers	Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which hinder accurate QoS predictions and limit the ability to capture diverse user preferences. Additionally, these methods often assume QoS data reliability, neglecting potential credibility issues such as outliers and the presence of greysheep users and services with atypical invocation patterns. Furthermore, traditional approaches fail to leverage diverse features, including domain-specific knowledge and complex higher-order patterns, essential for accurate QoS predictions. In this paper, we introduce a real-time, trust-aware framework for temporal QoS prediction to address the aforementioned challenges, featuring an end-to- end deep architecture called the Hypergraph Convoluted Transformer Network (HCTN). HCTN combines a hypergraph structure with graph convolution over hyper-edges to effectively address high-sparsity issues by capturing complex, high-order correlations. Complementing this, the transformer network utilizes multi-head attention along with parallel 1D convolutional layers and fully connected dense blocks to capture both fine-grained and coarse-grained dynamic patterns. Additionally, our approach includes a sparsity-resilient solution for detecting greysheep users and services, incorporating their unique characteristics to improve prediction accuracy. Trained with a robust loss function resistant to outliers, HCTN demonstrated state-of-the-art performance on the large-scale WSDREAM-2 datasets for response time and throughput.	10.1109/TNSM.2026.3674650