TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Shuyun Luo, Dongmiao Ying, Zhiyi Luo, Weiqiang Xu	Edge-based Approximate Caching for Fast and Scalable Text-to-Image Diffusion Models	2026	Early Access	Modeling Clouds Servers	Text-to-image generation applications based on diffusion models face substantial challenges in computational efficiency and latency, particularly in time-sensitive scenarios, due to the inherently iterative denoising process. Although approximate caching techniques can reduce denoising iterations by reusing intermediate states of diffusion models, existing approaches fail to adequately capture user request behaviors. It is observed that users tend to issue a large number of prompt requests within short time intervals (bursty patterns), and that prompts from the same user often exhibit high similarity over short time periods (temporal locality). In this work, we define and formalize the Intermediate State Selection (ISS) problem to minimize denoising iterations. We further prove the NP-hardness of the ISS problem via a polynomial-time reduction from the Dominating Set problem. We then exploit both characteristics of prompt requests and present EdgeDiffusion, a novel edge-cloud cooperative framework in which the cloud retains image generation, while prompts caching and intermediate state selection are offloaded to edge servers. Specifically, we design an ISS algorithm that optimizes state reuse by leveraging temporal locality and an adaptive caching strategy tailored to bursty patterns. Experimental results on real-world datasets demonstrate that EdgeDiffusion achieves 18.3%-77.3% computational savings over baseline strategies (NIRVANA, qLRU-AC, LRU and LFU), while maintaining 98% quality of images.	10.1109/TNSM.2026.3697787
Songshou Dong, Yanqing Yao, Huaxiong Wang, Yining Liu	LCMS: Efficient Lattice-based Conditional Privacy-preserving Multi-receiver Signcryption Scheme for Internet of Vehicles	2026	Early Access	Optical waveguides Optical fibers Broadcasting	Internet of Vehicles (IoV) requires robust security and privacy protection mechanisms to enable trusted traffic information exchange, while also requiring low communication and low computing overhead to meet the real-time requirements of IoV. Existing signcryption schemes suffer from quantum vulnerability, inadequate unlinkability/vehicle anonymity, absence of revocability, poor scalability, inadequate management of malicious entities, and high communication and computational overhead. So we propose an efficient lattice-based conditional privacy-preserving multi-receiver signcryption scheme (LCMS) that systematically addresses these gaps through three core innovations: 1) Privacy preservation is achieved via a pseudonym mechanism integrated with certificateless key generation, which ensures vehicle anonymity and weak unlinkability while preventing malicious key generation center and key escrow; 2) Malicious entity management through dynamic revocability and distributed decryption among roadside units, preventing unilateral message access; and 3) Post-quantum efficiency is achieved by leveraging the Learning With Rounding problem to eliminate expensive Gaussian sampling, combined with ciphertext packing techniques. This reduces time overhead, the size of signcryptexts, and communication overhead, while lowering the overall storage overhead of the scheme through the MP12 trapdoor. Security proofs show LCMS achieves Existential Unforgeability under Adaptive Identity Chosen-Message Attack and Indistinguishability under Adaptive Identity Chosen-Ciphertext Attack in the Random Oracle Model, with rigorously validated resistance against multiple IoV-specific attacks. Experimental results via SageMath implementation demonstrate that our scheme exhibits a smaller signcryptext size and lower signcryption/unsigncryption time compared to existing random lattice-based signcryption schemes. Scalability tests with 300 vehicles and 300 roadside units (RSUs) were completed within 230 seconds. Communication overhead analysis confirms practical feasibility for IEEE 802.11p vehicle communication protocol, and RSU serving capability evaluation under realistic vehicle density (100–200/km2) and speed (40–60 km/h) further validates system practicality. LCMS provides a quantum-resistant, privacy-preserving, and efficient solution for production IoV.	10.1109/TNSM.2026.3688507
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
Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, Ori Rottenstreich	SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges	2026	Early Access	Filtering Application specific integrated circuits Filters	Decentralized Finance (DeFi) lending protocols implement programmable credit markets without intermediaries. This paper systematizes the DeFi lending ecosystem, spanning collateralized lending (including over- and under- collateralized designs, and zero-liquidation loans), uncollateralized primitives (e.g., flashloans), and yield aggregation protocols which allocate capital across underlying lending platforms. Beyond a taxonomy of mechanisms and comparing protocols, we provide empirical on-chain measurements of lending activity and user behavior, using Compound V2 and AAVE V2 as case studies, and connect empirical observations to protocol design choices (e.g., interestrate models and liquidation incentives). We then characterize vulnerabilities that arise due to notable designs, focusing on interestrate setting mechanisms and time-measurement approaches. Finally, we outline open questions at the intersection of mechanism design, empirical measurement and security for future research.	10.1109/TNSM.2026.3682174
Jiang Mo, Ke Zhao, Limei Peng, Hsiao-Chun Wu	PDO-SFCM: Prediction-Driven Orchestration for SFC Migration in SAGIN via Fine-Tuned Large Time-Series Model and DRL	2026	Early Access	Modeling Space-air-ground integrated networks Timing	Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.	10.1109/TNSM.2026.3694203
Lizhuang Tan, Nguyen Van Tu, Xinhang Wang, Peiying Zhang, James Won-Ki Hong	SDNIE: A Software-Defined Approach to High-Performance Network Impairment Emulation using Programmable Switches	2026	Early Access	Emulation Central Processing Unit Testing	Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.	10.1109/TNSM.2026.3694388
Ashely Li, Jeffrey Chang, Steven S. W. Lee	Modeling and Optimization Algorithm for Capacity Planning in Hose Model VPN Networks	2026	Early Access	Joining processes Modeling Algorithms	Hose-based VPNs offer greater bandwidth flexibility, as they allow traffic to and from a hose endpoint to be arbitrarily distributed across other endpoints. Existing studies on hose-based VPNs have primarily focused on VPN provisioning algorithms, while optimal capacity planning for hose-based VPN networks remains largely unexplored. Given budget constraints and forecasts of future bandwidth demands at VPN endpoints, the capacity planning problem requires the joint optimization of routing decisions and link capacity allocation. Although the problem can be formulated as a nonlinear programming model, its nonconvex nature makes direct solution computationally challenging. To address this issue, we reformulate the problem as a sequence of linear programming problems and develop a solution framework based on a water-filling algorithm. For any defined budget and relative tolerance, the proposed algorithm yields a near-optimal solution where the network expansion cost stays within the allowed margin. Numerical results demonstrate that the proposed approach efficiently solves the hose-based VPN capacity planning problem within practical computation time.	10.1109/TNSM.2026.3694390
Arman Sanaei, Massoud Reza Hashemi	Adaptive, Profit-Aware RAN Slicing for Multi-Operator Networks via Spatio-Temporal Prediction and Energy-Aware SAC	2026	Early Access	Cells (biology) Energy Modeling	The proliferation of heterogeneous 5G/6G services and the emergence of multi-tenant deployments have made isolation-preserving RAN slicing a central requirement for shared infrastructures. This paper studies multi-tenant RAN slicing where multiple Mobile Virtual Network Operators (MVNOs) compete for a common pool of radio and edge-compute resources and must maximize long-term economic performance while preserving slice isolation. We propose a two-timescale framework that couples (i) prediction-driven, per-cell Long Time Slot (LTS) reservation with an isolation-aware congestion/pressure cost that coordinates competing MVNO demands, and (ii) Short Time Slot (STS) per-user allocation modeled as a Markov decision process and solved via an energy-aware Soft Actor–Critic (SAC) policy. This design separates strategic capacity planning from fast, stochastic user-level control, while retaining tractability under dynamic traffic, mobility, and channel uncertainty. Extensive simulations across small- and large-scale deployments show that the proposed approach improves MVNO profit per cell per LTS by up to 26% over representative baselines, while maintaining robust isolation under asymmetric demand and rival-tenant growth.	10.1109/TNSM.2026.3694799
Xin Hu, Xiantao Jiang, F. Richard Yu, Victor C.M. Leung	Enhancing Adaptive Video Streaming through Bandwidth Prediction with Deep Reinforcement Learning	2026	Early Access		With the development of HTTP-based video streaming, Adaptive Bitrate (ABR) algorithms have become crucial for optimizing video quality. These algorithms dynamically select the bitrate of video chunks based on factors such as network throughput and playback buffer occupancy. However, the volatility of network throughput, conflicting Quality of Experience (QoE) objectives, and cascading effects in decision-making pose significant challenges for ABR algorithms to accurately determine bitrate selections, leading to substantial revenue losses for content providers. This paper proposes a bandwidth prediction-based ABR algorithm for video streaming, termed the BPA algorithm, which consists of two components: a Bandwidth Prediction Model (BPM) and a Bitrate Selection Model (BSM). The BPM leverages a Bidirectional Long Short-Term Memory (BiLSTM) network for bandwidth prediction, while the BSM adopts an Actor-Critic reinforcement learning framework. A reward function based on bandwidth prediction accuracy is proposed, and an end-to-end joint optimization loss function is designed to train the model for optimal video bitrate selection. Under various network conditions, the BPA algorithm outperforms existing baseline algorithms, achieving an improvement of nearly 31.9% compared to traditional heuristic methods and a 9% enhancement over other deep reinforcement learning-based approaches. The BPA algorithm demonstrates excellent performance in terms of bitrate smoothness and QoE.	10.1109/TNSM.2026.3696658
S Gangadhar, A Chinmayananda, Animesh Roy	Decentralized Adaptive Initial Congestion Window in 5G Using Handshake-Based Flow Classification and Online Learning	2026	Early Access		The growing prevalence of latency-sensitive applications necessitates increasingly adaptive network congestion control mechanisms. This is particularly critical in Next-Generation Networks, e.g., 5G and beyond 5G networks, which promise low-latency and high-throughput support for diverse traffic. Traditional TCP variants employ a fixed Initial Congestion Window (ICW), resulting in suboptimal performance for short and long flows. This is because a small, fixed ICW unnecessarily prolongs short flows, while also delaying the ramp-up of long flows, leading to under-utilized bandwidth and increased latency. While machine learning based solutions offer improvements, they often rely on centralized architectures, limiting their applicability in decentralized scenarios. This paper introduces a novel decentralized framework that combines lightweight machine learning -based flow classification with online learning for real-time adaptive ICW. We first generate a unique dataset using OMNeT++/Simu5G simulations and select a suitable classifier that leverages TCP handshake features to distinguish short flows from long flows with 97.1% accuracy. This prediction drives an online learning model to dynamically select the efficient ICW before data transmission. Rigorous evaluation across TCP variants such as Reno, NewReno, and Westwood through simulations shows that our proposed framework achieves a 27–36% reduction in flow completion time and a 104-204% increase in throughput compared to baseline implementations. Robustness of our framework is further validated via an ablation study, threshold sensitivity analysis, missing TCP options, statistical significance with 95% confidence intervals (10-seed experiments, p < 0.0001), and multi-UE fairness (Jain index > 0.99). Operating entirely at the UE, our proposed solution eliminates dependence on centralized control, offering a scalable and resilient strategy for Next-Generation Networks.	10.1109/TNSM.2026.3698279
Kunpeng Zheng, Huibin Zhang, Yongli Zhao, Yuan Cao, Wei Wang, Xin Li, Zhuangzhuang Ma, Lihan Zhao, Jie Zhang	Sun-Outage-Aware Topology Modeling and Adaptive Routing for Optical Satellite Networks	2026	Early Access		Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.	10.1109/TNSM.2026.3697856
Ke Yu, Xiaofeng Tao, Shen Wang, Chaojie Guo	Game-Theoretic Defense of SYN Flood Attacks in B5G Cloud-Edge-Terminal Networks	2026	Early Access		The emergence of beyond-fifth-generation (B5G) networks and the increasing demand for Internet of Things (IoT) requires deploying a cloud-edge-terminal computing network with the Software Defined Network as the controller. However, this network is vulnerable to various threats, notably SYN flood attacks. This paper adopts queuing theory and game theory to explore Mobile Edge Computing (MEC) attack and defense interaction in different IoT businesses. Moreover, we propose a utility model of the packet flow in MEC networks featuring the delay and packet loss rate in the SYN flood attacks. For the attacker and defender’s strategy, we use game theory to model the interaction between strategy and resource allocation. A search algorithm analyzing MEC cell impact on strategy selection is developed, and we investigate the impact of the attacker’s possession of prior knowledge versus lack thereof regarding MEC cell characteristics under SYN flood attacks. The proposed game models are solved, and the results show that under the defender’s strategy, the attacker has no chance to launch SYN flood attacks under the defender’s defense cost of four times MEC computing resources; the cost of defense resources is lower than other related schemes.	10.1109/TNSM.2026.3695918
Li Zhang, Chan Xu, Yuan Huang, Bing Tang, Zijun Peng, Wenhui He, Buqing Cao, Mingdong Tang	Elastic Scaling for Microservices in Cloud-Edge Collaborative Environments: A Workload Prediction-Driven Approach	2026	Early Access		Cloud computing optimizes service quality and resource efficiency via centralized hardware and computational resources. However, the predominantly centralized deployment and operation of cloud data centers increase the physical distance to end-users, leading to degraded service quality. Edge computing addresses this by offloading data processing and analysis tasks directly to devices at the network edge, reducing reliance on backhaul transmission and thus offering a more responsive solution for latency-sensitive applications. Nevertheless, ensuring that applications meet predefined Service Level Agreement (SLA) in resource-constrained edge environments remains challenging. To tackle these issues, this paper investigates elastic scaling strategies in cloud-edge collaborative settings. We propose an attention-enhanced bidirectional LSTM model (A-Bi-LSTM) for microservice workload prediction, and design an adaptive elastic scaling system named XScale. This system incorporates a fall-back scaling mechanism when predictions are unreliable and introduces a proactive load forwarding strategy to enhance overall edge node performance. Experimental results show that, compared to existing elastic scaling methods, XScale reduces SLA violations by 82.3%, increases average resource utilization by 17.4%, decreases average response time by 21.1%, and improves overall edge node performance by 36.3%.	10.1109/TNSM.2026.3696137
Xiaomao Zhou, Zihao Shao, Qingmin Jia, Renchao Xie	ProxyLLM: Augmenting LLMs with Proxy Models for Tool Utilization in Network Service Generation	2026	Early Access	Tools Modeling Large language models	This paper introduces ProxyLLM, a novel framework designed to enhance the tool utilization capabilities of Large Language Models (LLMs) by leveraging an ensemble of smaller, specialized proxy models. Specifically, instead of invoking tools directly, ProxyLLM delegates tasks to these proxy models, each of which is responsible for a distinct domain and equipped with a curated set of relevant tools. Meanwhile, ProxyLLM employs a two-step knowledge transfer mechanism, utilizing data generated by the LLM for knowledge distillation and LLM-guided Deep Reinforcement Learning (DRL) to enhance the decision-making abilities of the proxy models. During the data-driven knowledge distillation process, the introduction of rationales ensures that proxy models maintain a comprehensive understanding of tasks, thereby improving the learning effectiveness. In the DRL learning process, LLM guidance is separately integrated into both the actor and critic learning phases. This ensures consistency in strategy and uniformity in evaluating the action space, which enhances both the efficiency and effectiveness of the learning process. Extensive experiments, including real-world applications such as network service generation in a Computing Power Network (CPN) system, demonstrate that ProxyLLM significantly outperforms existing methods in terms of task accuracy and tool invocation efficiency. The proposed framework offers a promising solution for constructing generalizable, large-scale intelligent agents capable of effectively leveraging diverse tools to solve complex, cross-domain problems.	10.1109/TNSM.2026.3695074
Haoyu Luo, Ming Liu, Shaojian Qiu, Xiao Liu	FaaSAdapter: An Adaptive Resource Configuration Framework for Serverless Workflows at the Edge	2026	Early Access	Optimization Resource management Modeling	Serverless computing has emerged as a promising deployment paradigm for edge scenarios, owing to its efficient resource utilization and flexible provisioning enabled by Function-as-a-Service (FaaS). In Serverless environment, developers are required to configure resources for functions to balance cost efficiency and performance. However, determining appropriate resource allocations for the functions running at the edge is a challenge due to the dynamic nature of the environment. This challenge is further compounded when managing serverless workflows composed of multiple interconnected functions with complex dependencies. To address such an challenge, we present FaaSAdapter, an efficient runtime resource configuration framework for workflow functions, aiming at conserving computational resources at the edge while ensuring timely response to user requests. Different from existing dynamic resource configuration methods that incrementally determine resource schemes for only the immediate subsequent workflow function, FaaSAdapter predicts the execution times of all the unexecuted functions across various resource configurations and determines an optimal configuration schema for the function instances based on the current execution progress. Then, it updates the configuration schema as needed during runtime. Comprehensive experiments demonstrate that FaaSAdapter ensures satisfactory response time of user requests with lowest resource consumption.	10.1109/TNSM.2026.3695591
Min Yuan, Qiangsheng Hu, Yuqing Zhu, Hongbing Wang	Prioritizing Critical Tasks in Microservice Clouds: A Dependency-Aware Container Scheduling Framework via Grouping and Degradation	2026	Early Access	Containers Optimization Algorithms	Containerized deployment serves as a pivotal technology for enhancing service performance and resource efficiency in large-scale distributed systems. However, guaranteeing the execution quality of critical tasks in resource-constrained scenarios remains a pressing challenge. This paper proposes a critical-task-prioritized container resource scheduling framework. Firstly, a dependency quantification model based on the Genetic Algorithm is designed to calculate the dependency strength between containers. Secondly, a container grouping strategy based on the Seagull Optimization Algorithm is proposed. Upon determining the optimal container group size, containers are grouped based on their dependency strengths. Finally, a service degradation mechanism for non-critical tasks is introduced. By predicting criticality rankings based on historical data, resources are released from non-critical task groups and prioritized for allocation to critical task groups, thereby ensuring the Quality of Service (QoS) of core business operations. Extensive experiments utilizing real-world datasets demonstrate that the proposed framework outperforms traditional algorithms, effectively increasing the successful request volume of critical tasks and reducing response latency.	10.1109/TNSM.2026.3697106
Mariusz Głąbowski, Sławomir Hanczewski, Damian Kmiecik, Maciej Stasiak, Joanna Weissenberg	Modeling of multi-service queueing systems with traffic overflow	2026	Early Access	Modeling Probability Servers	This article proposes an analytical model of a multi-service hierarchical system with multi-service overflow traffic. To model the primary and secondary resources of this system, the state-dependent queue service discipline was used. In order to model the secondary resources with the dedicated queue for overflow traffic, the Hayward’s approach was generalized and applied. To evaluate its accuracy, the results of analytical modeling were compared with the data obtained during the simulation experiments carried out in the study. Both the data presented in the article and the results obtained by the present authors in numerous comparative studies clearly indicate that the proposed model makes it possible to evaluate the values of the blocking probability with the accuracy that provides its reliable practical application at the stage of network dimensioning. The overflow mechanism has particular significance in networks with limited resources, such as mobile networks.	10.1109/TNSM.2026.3696894
Awaneesh Kumar Yadav, Ravi Kumar, An Braeken, Madhusanka Liyanage	A Provably Secure Multifactor Authentication and Key Exchange Protocol with Anonymity for Next-Generation IoT	2026	Early Access	Internet of Things Authentication Protocols	With the rapid surge in IoT devices, communication between the IoT devices and the server becomes more frequent. Since IoT devices are considered at the edge of the networks, their communication is completely exposed to the server, making them prone to several attacks. In addition to this, IoT devices have limited energy and computational resources. Therefore, there is an impelling necessity for an authentication mechanism suitable for security and taking into account the resource constraints. This paper shows that a recently proposed protocol by Daojing et al. is prone to serious attacks such as stolen device attacks, suffers from integrity violations, and does not offer perfect forward secrecy. We propose an alternative and more secure authentication mechanism for this type of model and also show that this protocol offers better performance with respect to the state-of-the-art. The proposed protocol achieves reductions of 75%, 40%, 36%, and 71% in computational, communication, storage, and energy consumption costs, respectively. Additionally, the protocol only has two communication phases. Furthermore, prototype implementation and simulation with the NS3 tool are carried out to show the applicability of the proposed work in real-time scenarios.	10.1109/TNSM.2026.3696671
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
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