TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
S A Harish, S Vignesh, Divya Pathak, Anil Kumar Sharma, Praveen Tammana	Anomaly Detection in In-Network Fast ReRoute Systems	2026	Early Access	Fluid flow Planing Delays	High-speed programmable data planes provide opportunities to implement data-driven fast reroute systems that quickly adapt to varying network conditions (e.g., congestion, failures) and improve network performance. The core of these systems has packet-processing algorithms running in the data plane that continuously look for traffic patterns (e.g., too many retransmissions) specific to a network condition (e.g., link failure) and take appropriate action (e.g., reroute). Despite their benefits, they also increase the potential attack surface. Adversaries can generate malicious traffic patterns resembling those anticipated by a fast reroute system and trick the system. Doing so would lead to poor network performance due to incorrect reroute decisions. In this paper, we propose a mechanism to detect whether the fast reroute systems are under the influence of malicious traffic patterns. Our key idea is to model the expected behavior using benign traffic features and use the model as a reference to determine whether the system is under the influence of adversaries. Using realistic attack traces, we demonstrate attacks on two fast reroute systems and successfully detect those attacks using the proposed detection mechanism.	10.1109/TNSM.2026.3715353
Alessandro Buratto, Marco Levorato, Leonardo Badia	DCP: a TCP-Inspired Domain Adaptation in Dynamic Data Drift	2026	Early Access	Modeling Timing Costing	Mobile devices are affected by computing limitations, battery life, and connectivity issues, making it difficult to execute complex machine learning models or frequently transmit data. These challenges hinder real-time adaptability in dynamic environments. To tackle these issues, we introduce a framework between mobile devices and edge servers, where the edge server assists mobile devices by continuously fine-tuning a lightweight classifier to keep up with changes in data patterns. Our approach, called the Drift Control Protocol (DCP), is inspired by how TCP manages network congestion. Just as TCP interprets packet loss as network congestion and throttles transmission, DCP treats spikes in classification error as congestion and implements an additive increase multiplicative decrease mechanism to dynamically control the frequency of model retraining, optimizing the trade-off between the classifier update rate and the communication overhead with the edge server. It dynamically adjusts the update frequency based on how the data distribution shifts and controls how many samples the mobile device sends to the edge server, while improving overall accuracy. We test different versions of DCP on both synthetic and real-world datasets. Our results show that DCP procedures obtain better tradeoffs in mean error and communication costs when compared with constant interval updates policies.We demonstrate this capability across real-world data and parametrized synthetic datasets explicitly designed to simulate both bursty and gradual drift scenarios.	10.1109/TNSM.2026.3719234
Jing Mei, Jinglei Xu, Zhao Tong, Keqin Li	Energy-Aware Multi-UAV Collaboration for Data Collection and Trajectory Planning with MADDPG	2026	Early Access		Unmanned Aerial Vehicles (UAVs) are pivotal for facilitating data collection in emergency scenarios. Despite the potential of Multi-Agent Deep Reinforcement Learning (MADRL) in coordinating such systems, existing researches struggle to resolve the high-dimensional coupling of data collection, trajectory planning, and energy scheduling under strict collision avoidance and Return-To-Base (RTB) constraints. This paper proposes a energy-aware cooperative MADRL framework designed to maximize data collection utility under energy constraints. Specifically, we employ a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) approach featuring a Centralized Training with Decentralized Execution (CTDE) design and a multi-objective reward mechanism to balance conflicting optimization goals. Extensive simulations validate the advantages of the proposed framework over leading baselines. Notably, the algorithm exhibits significant quantitative advantages in complex high-load scenarios. These outcomes prove that our method achieves higher task completion rates while strictly adhering to RTB and safety protocols.	10.1109/TNSM.2026.3721502
Ping He, Yu Yao, Xu Li, Yao Hu, Wei Yang	FIGAN: Diversity-Oriented Traffic Generation for Industrial Protocol Format Inference	2026	Early Access	Protocols Modeling Fuzzing	Protocol Format Inference is a pivotal step in the reverse engineering of proprietary protocols, yet its effectiveness is constrained by the scarcity of high-quality training data. In industrial control systems, the rigid and cyclical nature of traffic results in a "long-tail" distribution, where diverse functional scenarios are severely underrepresented. Existing generative approaches, primarily designed for fuzzing or intrusion detection, fail to resolve the intrinsic conflict between syntactic validity and semantic diversity required for protocol format inference. To bridge this gap, we propose FIGAN, a stage-wise decoupled generative framework tailored to synthesize high-fidelity traffic for protocol format inference. By isolating flexible distribution learning from rigid syntax enforcement, FIGAN liberates the generative process to extrapolate novel payload variations from a continuous latent space, effectively surmounting the limitations of sparse seed data. Specifically, the framework integrates three synergistic modules: first, heuristic pre-processing that constructs semantic templates as a prior knowledge base; second, a generative adversarial architecture optimized via discrete relaxation to explore high-dimensional payload patterns independently of syntax rules; and finally, a closed-loop verification mechanism that performs syntactic calibration and functional validation against simulated device responses. Evaluations on four real-world protocols (Modbus TCP, S7Comm, Omron FINS, and DNP3) demonstrate that FIGAN significantly outperforms state-of-the-art baselines. The source code has been open-sourced https://github.com/MissHP111/FIGAN.	10.1109/TNSM.2026.3717268
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
Yang Wu, Xu Cheng, Wenguang Zheng, Yingyuan Xiao	A Geo-Aware Personalized Network for User and Service Representation and Bilinear Interaction Modeling in QoS Prediction	2026	Early Access	Modeling Quality of service Matrices	With the rapid growth of the Internet, the proliferation of functionally similar web services has made Quality of Service (QoS) prediction, which measures service performance, increasingly critical. In QoS prediction, the QoS values observed from user-service invocations are often significantly affected by their geographical location. However, existing QoS prediction methods typically assume static user and service representations, overlooking geographic differences. We argue that even the same user or service should have personalized representations based on different geographic locations. To address this, we propose GeoPerNet, a Geo-Aware Personalized Network for QoS Prediction. Specifically, we design the Geographical Aware Personalization Module, which models the geographical similarity between users and services to select the most relevant top-k neighbors for the target user or service. We then apply geographic similarity-based weighting to highlight key neighbor information. Next, we leverage the designed GeoTransformer to model the complex dependency relationships among neighbors. Finally, the refined neighbor representations are fused with the original embeddings to generate personalized user and service representations. Additionally, we design the Bilinear Interaction Module to capture fine-grained interaction relationships between users and services using a bilinear function. Experiments on the large-scale WS-DREAM dataset demonstrate that GeoPerNet outperforms state-of-the-art approaches.	10.1109/TNSM.2026.3719697
Anselme Ndikumana, Kim Khoa Nguyen, Oscar Delgado, Adel Larabi, Mohamed Cheriet	Empowering Rural Areas with Energy-Efficient 5G IAB-Based Fixed Wireless Access Network	2026	Early Access	Resource management 5G mobile communication Modeling	Fixed Wireless Access (FWA) has recently emerged as a cost-effective alternative to optical fiber in rural areas, particularly where fiber deployment is economically infeasible. To extend coverage and increase capacity, FWA networks have begun to integrate Integrated Access and Backhaul (IAB) with mid- and high-band spectrum. However, the energy consumption of multi-hop IAB networks scales significantly with the number of hops, a challenge that prior research has not adequately addressed. This paper proposes an energy-efficient framework that minimizes network energy consumption by maximizing Resource Block (RB) utilization while avoiding both over- and under-allocation in multi-hop IAB-based FWA deployments. The proposed method jointly allocates RBs and selects modulation and coding schemes across a mixed set of 5G numerologies to satisfy data rate requirements while minimizing energy consumption. The inherent dynamic interactions among IAB stations render the problem highly complex and non-convex; therefore, we design a disciplined multi-convex programming supported by dynamic programming algorithms to obtain tractable solutions. Furthermore, we introduce a transformer-based prediction to forecast RB distribution, thereby mitigating the need for frequent short-timescale coordination among IAB stations. Our simulation results demonstrate that the proposed approach achieves the required data rates while reducing energy consumption by 14%.	10.1109/TNSM.2026.3719631
Wei Sai, Yihui Lu, Xin Guo	A Privacy-Preserving Security Framework for Multi-Party Data Fusion Computing Based on Homomorphic Encryption	2026	Early Access	Security Protocols Information rates	To prevent plaintext exposure in multi-party collaborative computing, this paper proposes a distributed secure multi-party computation protocol based on the Cheon-Kim-Kim-Song (CKKS) homomorphic encryption scheme. Data is encoded and encrypted at the source into CKKS complex polynomial ciphertext, enabling vectorized fusion under shared evaluation keys and threshold decryption in a decentralized architecture without a trusted central authority. Experiments on heterogeneous multi-institution datasets demonstrate low numerical error (9.0×10⁻⁷ at polynomial order 2¹⁶ and depth 12), effective scalability (throughput increasing from 1.12×10⁵ to 1.32×10⁵ ops/s and latency decreasing from 56 ms to 38 ms as nodes scale from 4 to 16), and strong robustness (70% decryption success at a 60% threshold and 95% recovery under malicious interference), showing that the framework achieves efficient computation with strict privacy protection for cross-party data fusion.	10.1109/TNSM.2026.3717343
Jheng-Jia Huang, Guan-Yu Chen, Hiroaki Kikuchi, Po-Yuan Su	Full-duplex Low-latency Handover and Transmission Authentication Protocol for 6G Networks	2026	Early Access	Protocols Authentication Modeling	Sixth-generation (6G) networks demand ultra-low latency, high reliability, and seamless handover. However, existing authentication and handover mechanisms, such as 3GPP 5G-AKA/EAP-AKA’ and Xn-/N2-based procedures, are based on half-duplex request–response signaling and incur sequential waiting delays. We propose a full-duplex–aware authentication and handover framework empowered by Co-frequency Co-time Full Duplex (CCFD) communication. By reorganizing 3GPP-style signaling so that authentication, key updates, and connection setup proceed in parallel between the UE, serving gNB, and target gNB, the central cloud is kept outside the real-time handover path. We formalize a game-based security model and prove mutual authentication under a pseudorandom permutation assumption. At the protocol level, we combine measured cryptographic costs with a link-delay model to compare the proposed scheme with the 3GPP baseline and representative academic protocols. The results show that full-duplex signaling can significantly reduce authentication and handover latency while preserving comparable security guarantees.	10.1109/TNSM.2026.3720523
Franck Messaoudi, Luhan Wang, Abdelkader Mekrache, Adlen Ksentini, Bingxuan Li, Jialei Su, Sofiane Messaoudi, Salim El Ghalbzouri	The Brewing Storm in 5G’s Data Plane: Design and Evaluation of a High-Performance eBPF/XDP-Based User Plane Function	2026	Early Access	Quality of service Fluid flow Kernel	This paper presents the design and implementation of a novel 5G UPF leveraging eBPF technology to meet the stringent performance and programmability requirements of emerging 6G systems. Traditional UPF implementations often struggle to balance performance, flexibility, and resource efficiency-challenges particularly critical in CPU- and I/O-constrained edge environments. The proposed eBPF-based UPF architecture mitigates these limitations by embedding core functionalities, such as packet classification, forwarding, and QoS enforcement, directly within the Linux kernel via eBPF programs attached through XDP and tc hook points. Performance evaluation using TRex demonstrates that the proposed solution achieves competitive throughput, low packet loss, and efficient CPU utilization across traffic profiles. Moreover, it maintains full compliance with 5G Core Network standards. Comparative analysis with well-established open-source UPF implementations further underscores its advantages. This work highlights the potential of eBPF as a foundational technology for building next-generation, programmable UPFs optimized for edge cloud deployments in the 6G era.	10.1109/TNSM.2026.3720812
Soonbeom Kwon, Yusu Noh, Youngwoo Jang, Illyoung Choi, Byungchul Tak, In-geol Chun, Young-Kyoon Suh	Scalable and Robust Resource Provisioning via Adaptive Task Scheduling for Edge Devices	2026	Early Access	Schedules Scheduling Cloning	Edge devices, such as wearables, drones, and CCTV systems, are vital for real-time data collection in urban intelligence. However, their limited computational and storage capacities pose significant challenges. While offloading to public clouds offers scalability, it often incurs high latency and operational costs. Conversely, centralizing workloads on edge servers may result in the underutilization of high-performance edge devices. To address these limitations, we introduce ERPF, a Kubernetes-based Edge Resource Provisioning Framework that augments the capabilities of heterogeneous edge environments. ERPF orchestrates dynamic volume provisioning, GPU-aware resource allocation, execution context migration, and adaptive task distribution to improve system flexibility and efficiency. Building on this, we propose a novel adaptive task scheduling technique, termed eATS, composed of three key mechanisms: (i) Partition Smoothing Scheme for stable task granularity control, (ii) Resilient Edge Reintegration for failure detection and task reassignment, and (iii) Competitive Task Cloning for speculative execution with fastest-result commitment. The proposed eATS scheme reduces task execution time by up to 27.6%, lowers partition size variability by 8.7×, and improves scheduling robustness across heterogeneous edge devices over the baseline.	10.1109/TNSM.2026.3694238
Qing Wu, Xijia Dong, Leyou Zhang, Yue Lei, Zilong Yan	Cloud-Assisted Verifiable and Updatable Private Set Union Protocol for Enhancing Network Intrusion Detection	2026	Early Access	Protocols Clouds Security	As network intrusion detection systems (NIDS) play an increasingly critical role in large-scale network environments, multiple organizations, Internet Service Providers (ISPs), and security service providers often maintain independent IP blacklists. Due to the dynamic nature of malicious IP addresses and their cross-organizational propagation, inter-organizational blacklist sharing is essential for improving network intrusion detection. However, traditional blacklist exchange mechanisms risk exposing participants’ complete blacklist information, and curious organizations may infer another organization’s detection strategies from the shared IP intersection, leading to privacy breaches.To address this issue, this paper proposes a Cloud-Assisted Verifiable and Updatable Private Set Union (CVU-PSU) protocol, which leverages the multi-query Reverse Private Membership Test (mq-RPMT) protocol and Oblivious Transfer (OT) technology to ensure privacy-preserving inter-organizational blacklist sharing. The protocol utilizes cloud computing to reduce the computational and communication overhead of participants in the mq-RPMT protocol while incorporating a verification mechanism to ensure the correctness of the cloud’s returned results. Furthermore, the protocol supports real-time blacklist updates, enabling adaptation to rapidly changing malicious IP addresses.Experimental results demonstrate that the proposed protocol achieves efficient inter-organizational blacklist sharing with low communication and computational costs while preserving privacy, thereby enhancing the real-time performance and accuracy of network intrusion detection systems.	10.1109/TNSM.2026.3716071
Masoumeh Safkhani, Mohammad Reza Servati, Fatemeh Rezaei	HEIoT: A Novel Three-Factor Authentication Protocol for Enhanced Security in IoT and Next-Generation Networks	2026	Early Access	Authentication Internet of Things Protocols	The Internet has a significant impact on contemporary society, enabling a wide range of applications, including advanced cellular networks such as 4G, 5G, and 6G. Since these communications occur over shared or open channels, ensuring secure data exchange is of critical importance, as any weakness in the communication infrastructure may compromise system reliability. Device authentication in the Internet of Things (IoT) and user authentication in smart environments, such as smart homes, remain fundamental security challenges. As the first line of defense, authentication mechanisms must be robust, since vulnerabilities at this stage can expose the entire system to serious threats. To address these challenges, numerous authentication schemes based on cryptographic primitives, including Elliptic Curve Cryptography (ECC), have been proposed. In this paper, we present a comprehensive security analysis of an ECC-based three-factor authentication protocol proposed by Yuan et al. Our analysis shows that the protocol is vulnerable to desynchronization, user impersonation, traceability, and insider attacks, all of which succeed with probability 1 by exploiting at most two protocol phases. To mitigate these weaknesses, we propose an improved authentication scheme, called HEIoT. The proposed scheme is formally analyzed under the Real-or-Random (RoR) model to establish session-key security and is further verified using the Scyther tool. Moreover, a Python-based implementation is provided to demonstrate the practicality of the proposed protocol. Comparative results indicate that HEIoT achieves stronger security while maintaining acceptable communication, computational, and storage overhead.	10.1109/TNSM.2026.3702041
Shuang Zheng, Xing Zhang, Michael Sheng, Haixu Wang, Wenbo Wang	Beam Hopping Low Earth Orbit Satellite Resource Allocation for Differentiated Services and Robustness Analysis under Model Attacks	2026	Early Access	Beams Satellites Resource management	Beam hopping (BH)-enabled Low Earth Orbit (LEO) satellites play a pivotal role in next-generation communication networks, providing global coverage, improving spectrum efficiency, and supporting flexible adaptation to heterogeneous service demands. To fully exploit these capabilities, artificial intelligence (AI) techniques are increasingly employed for dynamic resource allocation and power management. However, limited onboard resources and potential adversarial perturbations pose challenges to both efficiency and robustness. To address these issues, we leverage digital twin technology to accurately capture the spatio-temporal dynamics of user–satellite visibility, providing precise state information for decision-making. Building on this, we formulate a joint optimization framework for BH scheduling and power allocation as a Markov Decision Process and propose the BRIDGE—BH with Reinforcement learning incorporating Integrated Dirichlet and Gumbel-TopK Exploration—which integrates a quality of service (QoS)-driven subchannel scheduling mechanism to ensure efficient and differentiated resource allocation. The model’s robustness is systematically evaluated under three classical adversarial attacks. Simulation results demonstrate that our approach achieves superior energy efficiency, service throughput, and fairness, while the robustness analysis shows stable performance under the considered bounded adversarial perturbations.	10.1109/TNSM.2026.3710750
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
Islam Elgarhy, Mahmoud M. Badr, Ahmed T. Eltoukhy, Mohamed M. E. A. Mahmoud, Tariq Alshawi, Maazen Alsabaan, Mostafa M. Fouda	Interpretable Detector Secure Against Stealthy False Power Consumption Attacks	2026	Vol. 23, Issue	Detectors Power demand Explainable AI	Machine learning (ML) anomaly detectors are commonly used to identify cyber-attacks on smart power grids because they can detect new (i.e., zero-day) attacks by classifying deviations from normal patterns as anomalies. Deep-learning-based anomaly detectors offer superior performance but are highly sensitive to the selection of threshold values for defining anomalies. Conversely, traditional (or shallow-based) detectors avoid this threshold sensitivity but often underperform, particularly when dealing with complex interdependent data. Moreover, like all ML models, these detectors are vulnerable to adversarial evasion attacks, where adversaries make small and subtle manipulations to false data to evade detection. To address these issues, we propose a robust hybrid-based anomaly detector that combines the strengths of both deep and shallow-based and is trained using explanations derived from power consumption readings rather than the raw readings themselves. This hybrid approach not only mitigates threshold sensitivity and improves performance but also enhances robustness against white-box evasion attacks. Additionally, we introduce an interpretability method using occlusion sensitivity, which helps explain how a classification decision is made for an input power consumption sample, thereby increasing trust, reliability, and understanding of various attack patterns.	10.1109/TNSM.2026.3659131
Rajasekhar Dasari, Sanjeet Kumar Nayak	PR-Fog: An Efficient Task Priority-Based Reliable Provisioning of Resources in Fog-Enabled IoT Networks	2026	Vol. 23, Issue	Reliability Internet of Things Costs	As the demand for real-time data processing grows, fog computing emerges as an alternative to cloud computing, which brings computation and storage closer to IoT devices. In Fog-enabled IoT networks, provisioning of fog nodes for task processing must consider factors, such as latency, energy consumption, cost, and reliability. This paper presents PR-Fog, a scheme for optimizing the provisioning of heterogeneous fog nodes in fog-enabled IoT networks, considering parameters such as task priority, energy efficiency, cost efficiency, and reliability. At first, we create an analytical framework using M/M/1/C priority queuing system to assess the reliability of these heterogeneous fog nodes. Building on this analysis, we propose an algorithm that determines the optimal number of reliable fog nodes while satisfying latency, energy, and cost constraints. Extensive simulations show significant enhancements in key performance metrics when comparing PR-Fog to existing schemes, including a 36% decrease in response time and an 8% improvement in satisfaction ratio, resulting in minimized 23% fog node provisioning costs. Additionally, PR-Fog’s effectiveness is validated through real testbed experiments.	10.1109/TNSM.2026.3661745
Xinshuo Wang, Lei Liu, Baihua Chen, Yifei Li	ENCC: Explicit Notification Congestion Control in RDMA	2026	Vol. 23, Issue	Bandwidth Data centers Heuristic algorithms	Congestion control (CC) is essential for achieving ultra-low latency, high bandwidth, and network stability in high-speed networks. However, modern high-performance RDMA networks, crucial for distributed applications, face significant performance degradation due to limitations of existing CC schemes. Most conventional approaches rely on congestion notification signals that must traverse the queuing data path before congestion signals can be sent back to the sender, causing delayed responses and severe performance collapse. This study proposes Explicit Notification Congestion Control (ENCC), a novel high-speed CC mechanism that achieves low latency, high throughput, and strong network stability. ENCC employs switches to directly notify the sender of precise link load information and avoid notification signal queuing. This allows precise sender-side rate control and queue regulation. ENCC also ensures fairness and easy deployment in hardware. We implement ENCC based on FPGA network interface cards and programmable switches. Evaluation results show that ENCC achieves substantial throughput improvements over representative baseline algorithms, with gains of up to $16.6\times $ in representative scenarios, while incurring minimal additional latency.	10.1109/TNSM.2026.3656015
Junfeng Tian, Rongyi Fei	A Certificateless Dynamic Anonymous Authentication Scheme for Mobile Edge Computing	2026	Vol. 23, Issue	Authentication Servers Security	In mobile edge computing (MEC), many identity-based authentication schemes rely on an unrealistic prior-knowledge assumption about edge server identities, which limits their applicability in highly dynamic MEC environments. On the other hand, although certificateless schemes alleviate the computational overhead introduced by bilinear pairings in ID-based schemes, they face new challenges regarding secure key storage and the achievement of full user anonymity. To simultaneously address these issues, this paper proposes a new certificateless dynamic anonymous authentication scheme based on physical unclonable functions (PUFs) and elliptic curve cryptography (ECC), tailored for authentication and key agreement between mobile users and edge servers in dynamic MEC environment. By leveraging PUFs, the scheme resolves the key storage issue commonly found in traditional certificateless authentication approaches. Additionally, the scheme supports dynamic anonymity and frequent updates of public–private key pairs, thereby enhancing system security and providing user with full anonymity and unlinkability. The proposed scheme is rigorously evaluated through both informal and formal security analyses, including BAN logic, the Real-Or-Random (ROR) model, and automated verification via ProVerif. Comparative results demonstrate that our scheme achieves stronger computational efficiency, lower energy consumption, lower average message delay, and acceptable communication and storage overhead, while maintaining robust security guarantees compared with recent state-of-the-art approaches in this field.	10.1109/TNSM.2026.3661192
Beom-Su Kim	A Semantic-Aware TSN Framework for Minimizing Age of Informative Data in Real-Time Industrial Monitoring Systems	2026	Vol. 23, Issue	Job shop scheduling Real-time systems Monitoring	Real-time industrial monitoring systems rely on the timely delivery of semantically important data, such as anomaly-indicating sensor readings, to enable accurate and responsive decision-making. Credit-Based Shaping (CBS), a key mechanism in Time-Sensitive Networking (TSN), is well-suited for such systems due to its ability to dynamically manage aperiodic and bursty traffic without rigid transmission schedules. However, CBS was originally designed for Audio Video Bridging (AVB) traffic, which is periodic and less delay-sensitive, and thus lacks mechanisms to prioritize packets based on their semantic importance or freshness. As a result, critical updates may be delayed by the transmission of redundant or outdated packets. Motivated by these limitations, this paper presents an enhanced CBS mechanism, aiming to ensure the timeliness of semantically informative data in real-time industrial monitoring systems. Specifically, we encapsulate this enhancement within a semantic-aware TSN framework, which integrates three tightly coupled techniques: 1) semantic-based packet prioritization, 2) age-aware traffic shaping, and 3) age-aware packet forwarding and filtering. These mechanisms work in synergy to detect and expedite the transmission of high-priority, semantically meaningful packets, while suppressing redundant updates. Simulation results demonstrate that the proposed approach significantly improves anomaly detection responsiveness while maintaining efficient bandwidth utilization.	10.1109/TNSM.2026.3661050