TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Wenjing Jing, Quan Zheng, Siwei Peng, Shuangwu Chen, Xiaobin Tan, Jian Yang	Equivalent Characteristic Time Approximation Based Network Planning for Cache-enabled Networks	2026	Early Access	Planning Resource management Costs	The exponential surge in network traffic has imposed significant challenges on traditional Internet architectures, resulting in high latency and redundant transmissions. Cache-enabled networks alleviate these issues by deploying content closer to end-users, making the planning of such networks a research focus. However, regional heterogeneity in user demand and caching interdependencies among hierarchical nodes complicate the planning process. Most existing approaches rely on simplistic even allocation or empirical methods, which fail to simultaneously meet user performance expectations and minimize deployment costs. This paper proposes a network planning framework based on the Equivalent Characteristic Time Approximation (ECTA). The approach begins by establishing a performance–resource mapping. Using ECTA, we decouple the tightly coupled characteristic time relationships across hierarchical nodes, thereby accurately estimating the required cache capacity and bandwidth needed to achieve user performance targets. Building on this foundation, we formulated the network planning as a constrained convex optimization problem that minimizes deployment cost while satisfying user performance constraints. We conducted extensive experiments on a large-scale simulation platform (ndnSIM) and a real-world cache-enabled network testbed (CENI-HeFei). The results demonstrate that, under identical network topologies and total resource constraints, our method significantly improves cache hit probability while reducing deployment costs compared to homogeneous resource allocation schemes. This work provides a practical theoretical foundation and valuable insights for the design, deployment, and optimization of future cache-enabled networks.	10.1109/TNSM.2026.3670399
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
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
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
Junqing Wang, Lejun Zhang, Zhihong Tian, Kejia Zhang, Shen Su, Jing Qiu, Yanbin Sun, Ran Guo	6Global: Dynamic IPv6 Active Address Scanning Assisted by Global Perspective	2026	Early Access	Clustering algorithms Heuristic algorithms 6G mobile communication	Network scanning is crucial for both network management and cybersecurity. However, due to the vast address space of IPv6, brute-force scanning is infeasible. Seed-based target generation algorithms have recently attracted considerable research attention. However, existing target generation algorithms lack a deeper exploration of patterns, leading to poor capture of dense regions and consequently low hitrate. To address this issue, we propose 6Global, a dynamic IPv6 active address scanning method assisted by global perspective. 6Global first performs rapid clustering of seed addresses based on their descriptive attributes. Then, for each cluster, patterns are generated in a bottom-up manner based on entropy, using subranges to represent patterns and resulting in denser patterns. Finally, dynamic scanning is conducted using these patterns. During scanning, the reward of each pattern is dynamically adjusted based on its active density and global statistics, which enhances the capability in capturing dense regions. Experimental results on six seed datasets show that 6Global overall outperforms seven baseline methods and demonstrates significant advantages across multiple datasets.	10.1109/TNSM.2026.3674490
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
Máté Nagy, Tamás Lévai, Felicián Németh, Aurojit Panda, Gianni Antichi, Gábor Rétvári	Elastic Scaling of Real Time Communication Services	2026	Early Access	Media Servers WebRTC	Real-time Communications (RTC) services, including multiparty conferencing, live streaming, and cloud-gaming, rely on a large-scale media plane infrastructure that provides real-time audio/video processing to clients. Unfortunately, offthe- shelf RTC services are not elastically scalable. As a result, operators must provision media servers to meet peak demand, resulting in resource under-utilization and high cost. Given that today microservice orchestrators like Kubernetes allow web-services to scale transparently and econimically, this paper looks at applying the same approach to scale large-scale RTC services. We find that this is challenging for two reasons: (a) the default network dataplane underlying Kubernetes does not meet the compelling traffic management, performance and real-time requirements of RTC; and (b) current autoscaling policies are ill-suited to RTC. We address these challenges by designing a RTC-specific service mesh that pushes media traffic processing into the OS kernel and designing new RTC-specific Kubernetes autoscaling policies. Our evaluation on a functional VoIP test-bed shows that this combination allows to deploy elatically scalable RTC services with 100× lower-jitter and 700× lower RTT than the current state-of-the art.	10.1109/TNSM.2026.3674598
Xing Li, Ge Gao, Zhaoyu Chen, Xin Li, Qian Huang	MD-PCSN: Meta-motion Decoupling Point Cloud Sequence Network for Privacy-Preserving Human Action Recognition in AI machines	2026	Early Access	Point cloud compression Convolution Three-dimensional displays	In next-generation communication networks and Industry 5.0 based applications, ensuring robust security and reliability in human-computer interaction (HCI) constitutes a fundamental prerequisite for safety-critical AI machine systems. Point cloud sequence-based human action recognition demonstrates intrinsic advantages in privacy-preserving HCI, leveraging its non-intrusive sensing modality to mitigate data vulnerability while maintaining high-precision action interpretation in industrial environments. Existing spatio-temporal encoding methods for point cloud sequence-based action recognition suffer from two fundamental limitations: (1) rigid neighborhood constraints impair multi-scale feature extraction for heterogeneous body parts, and (2) independent spatial-temporal decomposition introduces motion representation distortion. We propose a Meta-motion Decoupling Point Cloud Sequence Network (MD-PCSN) that addresses these challenges through: (1) logarithmic spatio-temporal point convolution for hierarchical meta-motion construction at variable granularities, and (2) a novel Gated-KANsformer architecture with differential motion encoding to explicitly model both short-term displacements and long-term spatio-temporal dependencies. The proposed meta-motion decoupling mechanism significantly enhances robustness against sensor perturbations, making the framework particularly suitable for security-critical applications. Extensive experiments on three benchmark datasets demonstrate MD-PCSN’s superior performance. It outperforms classic PST-Transformer by 1.5% on MSR Action3D and 4.14% on UTD-MHAD. Under the NTU RGB+D 60, it achieves 2.9% cross-view gain over the latest PointActionCLIP.	10.1109/TNSM.2026.3671357
Woojin Jeon, Donghyun Yu, Ruei-Hau Hsu, Jemin Lee	Secure Data Sharing Framework with Fine-grained Access Control and Privacy Protection for IoT Data Marketplace	2026	Early Access	Internet of Things Encryption Access control	The proliferation of IoT devices has led to an exponential increase in data generation, creating new opportunities for data marketplaces. However, due to the security and privacy issues arising from the sensitive nature of IoT data, as well as the need for efficient management of vast amounts of IoT data, a robust solution is necessary. Therefore, this paper proposes a secure data sharing framework with fine-grained access control and privacy protection for the internet of things (IoT) data marketplace. For fine-grained access control of the data in the proposed protocol, we develop the hidden attributes and encryption outsourced key-policy attribute-based encryption (HAEO-KP-ABE) that outsources high-complex operations to peripheral devices with high capability to reduce the computation burden of IoT device. It achieves data privacy by hiding attributes in the ciphertext and by preventing entities that do not hold the data consumer’s secret key material (including SA/CS) from running the match test on stored ciphertexts before decryption. It also has an efficient match test algorithm which can verify that the hidden attributes of the ciphertext match the access policy of the data consumer’s private key without revealing those attributes. We demonstrate the proposed protocol satisfies the security features required for the data sharing process in an IoT data marketplace environment. Furthermore, we evaluate the execution time of the proposed protocol according to the number of attributes and show the practicality and efficiency of the proposed protocol compared to the related works.	10.1109/TNSM.2026.3670207
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
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		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
Wenxuan Li, Yu Yao, Ni Zhang, Chuan Sheng, Ziyong Ran, Wei Yang	IMADP: Imputation-based Anomaly Detection in SCADA Systems via Adversarial Diffusion Process	2026	Early Access	Anomaly detection Adaptation models Data models	As the confrontation of the industrial cybersecurity upgrades, multi-dimensional variables measured by the SCADA multi-sensor are critical for assessing security risks in industrial field devices. While Deep Learning (DL) methods based on generative models have demonstrated effectiveness, the impact of missing features in samples and temporal window size on modeling and detection processes has been consistently overlooked. To address these challenges, this work proposes an IMADP framework that integratively solves two tasks of missingness patching and anomaly detection. Firstly, the Window-based Adaptive Selection Strategy (WASS) is also designed to intelligently window samples, reducing reliance on prior settings. Secondly, an imputer is constructed under WASS to restore sample integrity, which is implemented by a fully-connected network centered on Neural Controlled Differential Equations (NCDEs). Thirdly, a adversarial diffusion detection model with the variant Transformer as the inverse solver is proposed. Additionally, the Adaptive Dynamic Mask Mechanism (ADMM) is built upon to bolster the model’s comprehension of inter-dependencies between time and sensor nodes. Simultaneously, adversarial training is introduced to optimize training and detection latency caused by the excessive diffusion step size during the native Conditional Diffusion process. The experimental results validate that the proposed framework has the capability to build detectors using missing training samples, and its overall detection performance, tested across six datasets, is superior to existing methods.	10.1109/TNSM.2026.3670062
Pengcheng Guo, Zhi Lin, Haotong Cao, Yifu Sun, Kuljeet Kaur, Sherif Moussa	GAN-Empowered Parasitic Covert Communication: Data Privacy in Next-Generation Networks	2026	Early Access	Interference Generators Generative adversarial networks	The widespread integration of artificial intelligence (AI) in next-generation communication networks poses a serious threat to data privacy while achieving advanced signal processing. Eavesdroppers can use AI-based analysis to detect and reconstruct transmitted signals, leading to serious leakage of confidential information. In order to protect data privacy at the physical layer, we redefine covert communication as an active data protection mechanism. We propose a new parasitic covert communication framework in which communication signals are embedded into dynamically generated interference by generative adversarial networks (GANs). This method is implemented by our CDGUBSS (complex double generator unsupervised blind source separation) system. The system is explicitly designed to prevent unauthorized AI-based strategies from analyzing and compromising signals. For the intended recipient, the pretrained generator acts as a trusted key and can perfectly recover the original data. Extensive experiments have shown that our framework achieves powerful covert communication, and more importantly, it provides strong defense against data reconstruction attacks, ensuring excellent data privacy in next-generation wireless systems.	10.1109/TNSM.2026.3666669
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
Zhaoping Li, Mingshu He, Xiaojuan Wang	HKD-Net: Hierarchical Knowledge Distillation Based on Multi-Domain Feature Fusion for Efficient Network Intrusion Detection	2026	Early Access	Feature extraction Telecommunication traffic Knowledge engineering	We propose HKD-Net1, a hierarchical knowledge distillation network based on multi-domain feature fusion, for efficient network intrusion detection on resource-constrained edge devices. The framework incorporates dedicated feature extraction modules across temporal, frequency, and spatial domains, and introduces a dynamic gating mechanism for adaptive feature fusion, resulting in a more discriminative and comprehensive feature representation. Moreover, a hierarchical distillation mechanism is designed that not only preserves soft labels from the output layer but also aligns intermediate features from spatial, temporal, frequency, and fused domains, enabling efficient knowledge transfer from a large teacher model to a compact student model. Through knowledge distillation, the final lightweight model requires only 278,580 parameters, reducing the number of parameters by approximately 74.68% compared to the teacher, while maintaining high detection accuracy. Extensive experiments on three public datasets (Kitsune, CIRA-CIC-DoHBrw2020, and CICIoT2023) demonstrate that HKD-Net outperforms five state-of-the-art methods, achieving accuracies of 96.72%, 97.19%, and 87.19%, respectively, while reducing parameters by 74.68% and maintaining low computational cost.	10.1109/TNSM.2026.3668812
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
Yanxu Lin, Renzhong Zhong, Jingnan Xie, Yueting Zhu, Byung-Gyu Kim, Saru Kumari, Shakila Basheer, Fatimah Alhayan	Privacy-Preserving Digital Publishing Framework for Next-Generation Communication Networks: A Verifiable Homomorphic Federated Learning Approach	2026	Early Access	Electronic publishing Federated learning Cryptography	Next-generation communication networks are revolutionizing digital publishing through intelligent content distribution and collaborative optimization capabilities. However, existing federated learning approaches face fundamental limitations, including trusted third-party dependencies, excessive communication overhead, and vulnerability to collusion attacks between servers and participants. This paper introduces VHFL-DP, a verifiable homomorphic federated learning framework for digital publishing environments operating within 6G network infrastructures. The framework addresses critical privacy and scalability challenges through four key innovations: a distributed cryptographic key generation protocol that eliminates trusted third-party requirements, Chinese remainder theorem-based dimensionality reduction, auxiliary validation nodes that enable independent verification with constant-time complexity, and an intelligent incentive mechanism that rewards digital publishing platforms based on objective contribution quality metrics. Experimental evaluation on MNIST and Amazon reviews datasets across six baseline methods demonstrates that VHFL-DP achieves superior performance with accuracy improvements of 4.2% over the best baseline method. The framework maintains constant verification time ranging from 2.73 to 2.91 seconds regardless of platform count, increasing from ten to fifty, or dropout rates reaching thirty percent. Security evaluation reveals strong resilience with only 2.4 percentage point accuracy degradation under poisoning attacks compared to 6.7-7.0 points for baseline method, inference attack success near random guessing at 51.3%, and 92.4% successful aggregation under Byzantine adversaries.	10.1109/TNSM.2026.3667167
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
Yalan Wu, Zhibing Fang, Jiale Huang, Longkun Guo, Jigang Wu	Vehicle Coalition Based Incentive Algorithm for Model Deployment and Task Offloading	2026	Early Access	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
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