TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Xingyu He, Nianci Li, Panxing Huang, Chunhua Gu, Guisong Yang, Yunhuai Liu	Dynamic Spatiotemporal Dual-Encoder Transformer for Long-Term Traffic Prediction in LEO Satellite Networks	2026	Early Access	Satellites Modeling Low earth orbit satellites	Accurate long-term traffic prediction in Low Earth Orbit (LEO) satellite networks is essential for proactive resource allocation and congestion avoidance, yet remains challenging due to highly dynamic topologies, intermittent connectivity, and scarce real traffic data. Existing approaches are largely limited to short-term prediction or assume static spatial dependencies, making them inadequate for non-stationary LEO environments. To address these challenges, this paper proposes DST-DEformer, a dynamic spatial–temporal Transformer framework that jointly models evolving inter-satellite topology and multi-scale temporal dependencies. Specifically, a topology-adaptive graph convolution module captures time-varying spatial correlations, while a dual temporal encoder decouples long-term global trend modeling from short-term local fluctuation learning. In addition, a hybrid simulation–calibration framework is developed to generate realistic satellite traffic by incorporating orbital dynamics, demographic information, and real-world traffic trends. Extensive experiments on simulated LEO satellite traffic and the PEMS08 benchmark show that DST-DEformer consistently outperforms state-of-the-art methods in long-term prediction, achieving 4%-13% reductions in MSE and MAE and significantly slower error accumulation as the prediction horizon increases. These results demonstrate the effectiveness and robustness of DST-DEformer for long-term traffic prediction under dynamic network topologies.	10.1109/TNSM.2026.3693648
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
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
Yuxiang Wang, Jiao Zhang, Leixin Cai, Tao Huang	Mercury: Multipath Spraying for Joint Congestion and Reordering Control in RDMA	2026	Early Access		Due to the low entropy traffic characteristics of LLM (Large Language Model) training, existing load balancing mechanisms such as Equal-Cost Multi-Path (ECMP) fail to fully utilize the redundant bandwidth between computing nodes in RDMA over Converged Ethernet (RoCE). Packet spraying mechanism has become a typical solution to the load balancing problem in RoCEs. However, it has a negative effect on congestion control mechanisms and suffers severe out-of-order problems. In this paper, we propose Mercury, a host-driven spraying scheme that synergizes congestion feedback and reordering control. Mercury selects paths by leveraging ECN, RTT, and reordering metrics, adjusts rates via multi-metric window. It also employs receiver-side buffers with priority-based dropping to mitigate out-of-order penalties. Evaluations in ns-3 under AllReduce and All-to-All traffic show that Mercury consistently outperforms the ECMP-based baselines, including DCQCN, TIMELY, HPCC, SWIFT, and BOLT, with the largest reduction in Max FCT reaching 63%. Under multi-path load balancing, Mercury delivers the lowest Max FCT for large messages in AllReduce and for most message sizes in All-to-All. It outperforms STRACK and MP-RDMA by up to 28% and 35% in AllReduce, and by up to 25% and 30% in All-to-All.	10.1109/TNSM.2026.3692452
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
Shaimaa Alkaabi, Mark A Gregory, Shuo Li	A Stateless Orchestrated Handover Protocol for Multi-Access Edge Computing	2026	Early Access		In Multi-access Edge Computing (MEC) environments, session continuity during user mobility remains a pressing challenge due to decentralized infrastructure and high-throughput, latency-sensitive applications. Existing mobility protocols often rely on stateful mechanisms or centralized control, leading to increased signaling overhead, limited scalability, and vulnerability to performance degradation in dynamic networks. This paper introduces the Server Search and Select Algorithm Protocol (SSSAP), a lightweight, UDP-based handover protocol tailored for MEC deployments. The protocol is an extension of our previous work on a handover Server Search and Selection Algorithm (SSSA). SSSAP enables seamless session redirection through a three-phase signaling scheme (pre-handover, handover initiation, and handover termination), preserving service continuity without coupling session state to transport layers. The protocol’s design features extensible headers for multi-metric evaluation and future security adaptation while maintaining minimal dependency on intermediary control nodes. Through extensive simulation and testing, we have validated the SS-SAP efficiency across user equipment nodes and MEC servers. Results demonstrate high handover success rates, low-session setup delays, and balanced server load distribution. SSSAP achieves superior performance in mobility robustness, packet loss mitigation, and integration simplicity. The research outcomes position SSSAP as a scalable and application-agnostic mobility protocol for MEC systems, especially in vehicular and high-mobility scenarios.	10.1109/TNSM.2026.3692555
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
Awaneesh Kumar Yadav, Madhusanka Liyanage, An Braeken	An Improved and Provably Secure EDHOC Protocol Supporting the Extended Canetti–Krawczyk (eCK) Security Model	2026	Early Access	Aerospace and electronic systems Telemetry Central Processing Unit	Transport Layer Security (TLS) is considered to be the most used standard security protocol for the Internet of Things (IoT). However, as TLS was originally designed for computer networks, it is not optimal with respect to efficiency. Therefore, a new protocol called Object Security for Constrained RESTful Environments (OSCORE) has been standardized for securing constrained devices. Currently, the Ephemeral Diffie Hellman Over COSE (EDHOC) protocol, which is a key exchange protocol to define a session key used in OSCORE, is also in the process of being standardized. This paper shows that the four authentication modes of the EDHOC protocol are vulnerable in the extended Canetti–Krawczyk (eCK) security model, which is a common security model used in IoT. In addition, also resistance to Distributed Denial of Service (DDoS) attacks is weak. Taking this into account, we propose two new variants of EDHOC. The first variant, EDHOC2, is able to overcome both issues but has a slightly higher cost for communication, computation, storage, and energy consumption. The second variant, EDHOC3, offers only additional protection in the eCK security model and has, on average, similar, even better performance in one authentication mode, compared to EDHOC. Additionally, the Real-Or-Random (ROR) logic and Scyther validation tool are employed to ensure the security of the designed variants. Furthermore, a prototype implementation is conducted to demonstrate the real-time deployment of the designed versions.	10.1109/TNSM.2026.3690530
Jiale Zhu, Xiaoyao Zheng, Shukai Ye, Ming Zheng, Liping Sun, Liangmin Guo, Qingying Yu, Yonglong Luo	Federated Recommendation Model Based on Personalized Attention and Privacy-Preserving Dynamic Graph	2026	Early Access	Modeling Federated learning Privacy	Graph Neural Networks (GNNs) have been widely adopted in recommendation systems. When integrated into a federated learning framework, GNNs can enhance the model’s expressive capability. However, challenges arise in personalized representation and graph expansion due to the heterogeneity and locality of user data in federated recommendation systems. To address these challenges, we propose a federated recommendation model based on personalized attention and privacy-preserving dynamic graphs. The method first matches neighbor users for each selected client. Subsequently, it counts the interaction frequencies of items for both local and neighbor users to construct personalized weights, which captures the unique characteristics of different users. Additionally, we designs a method for constructing privacy-preserving dynamic graphs. In each round of federated training, the selected client adds pseudo-interaction items to its own interaction subgraph, perturbing the real interactions. After completing local training, the noisy interaction subgraph is incorporated into the global graph to capture higher-order connectivity information among users while safeguarding their interaction privacy. We conduct extensive experiments on three benchmark datasets, and the results demonstrate that the proposed PADG method achieves superior performance while effectively protecting privacy.	10.1109/TNSM.2026.3691659
Atri Mukhopadhyay, Dinesh Korukonda, Goutam Das	Design of Passive Optical Network Based O-RAN X-haul: A Systematic Approach	2026	Early Access	Timing Passive optical networks Optimization	The development of high data rate communication technologies has resulted in cell densification, which in turn has led to the development of centralized radio access networks (C-RANs) followed by open radio access networks (O-RANs). The O-RAN segregates the base station into three logical entities; the central unit (CU), the distributed unit (DU) and the radio unit (RU). The CU, DU and RU require low latency, low jitter and high data rate connections for seamless operation, which is known as X-haul. A passive optical network (PON) is a potential solution for X-haul design. However, conventional PON uplink protocols are not inherently suitable for X-haul requirements. The packetization procedure of PON introduces jitter to the X-haul bit stream. Further, the delay requirements of the X-haul limit the number of sources that can be connected to the X-haul. Advanced features like coordinated multipoint requires synchronization among the different X-haul bit streams as well. Therefore, in this paper, we develop an optimal uplink system that allows PON to be used as an X-haul connection technology. The proposal maximizes the throughput of the PON while conforming to the delay and synchronization requirements. Moreover, the proposal nullifies the jitter introduced by the PON scheduler. We have performed extensive simulations for verifying our results.	10.1109/TNSM.2026.3692242
Md Facklasur Rahaman, Makhduma F. Saiyed, Irfan Al-Anbagi, Ramakrishna Gokaraju	A Domain-informed Hierarchical Federated Learning Framework for DDoS Detection in WSN for Critical Infrastructure	2026	Early Access	Modeling Internet of Things Signal detection	The deployment of Wireless Sensor Networks (WSN) in critical infrastructure, such as Small Modular Reactors (SMRs), faces cybersecurity threats like Distributed Denial of Service (DDoS) attacks that can overload these networks and disrupt monitoring and control functions. Current DDoS detection systems often suffer from high false positive rates, neglect domain-specific operational constraints, and rely on centralized architectures that pose privacy risks, making them less suitable for distributed Internet of Things (IoT) environments. To address these issues, we propose a novel Domain-informed Hierarchical Federated Learning (DHFL) framework for WSN used in SMR monitoring and control applications. Our framework features a dual-branch bidirectional Long Short-Term Memory (LSTM) architecture comprising of two parallel processing branches with network-specific constraints, facilitating precise detection of DDoS attacks. It includes differentiable penalty functions to enforce domain-aligned behaviour and employs adaptive trust scoring to evaluate the reliability of individual nodes. These elements operate within a hierarchical Federated Learning (FL) structure organized into three tiers: sensor nodes, local aggregators, and a global coordinator, allowing collaborative training that preserves privacy. Unlike earlier approaches, our method not only maintains privacy by ensuring that raw sensor data never leaves the local nodes and only model updates are shared but also considers the operational importance and trustworthiness of each node through tier-weighted aggregation. Tested on the CICIoT2023 dataset, our system achieved 93.4% accuracy, 94.5% precision, 97.5% recall, 95.5% F1-score, and 98.9% AUC, surpassing state-of-the-art FL methods in both performance and efficiency. Furthermore, it converged in fewer communication rounds (30–50) with reduced communication costs (from 45 MB to 30 MB per round). Our framework can differentiate between normal reactor transients and actual attacks, making it suitable for mission-critical SMR cybersecurity.	10.1109/TNSM.2026.3693112
Minh-Thuyen Thi, Mohan Gurusamy	Multi-dimensional Cross-granularity Open-set Network Intrusion Detection	2026	Early Access	Modeling Labeling Distance measurement	Network intrusion detection systems (NIDSs) face critical challenges from continuously evolving cyber-attacks. Traditional machine learning methods, while requiring extensive labeled training data, still often fail against unknown and out-of-distribution (OOD) attacks. Furthermore, new sophisticated adversaries are exploiting the detection blind spots inherent in traditional feature representation approaches that do not provide adequate comprehensive traffic analysis. In this paper, we propose MDCG-IDS, an NIDS framework that introduces multi-dimensional cross-granularity (MDCG) feature representation for open-set detection, in which network traffic is analyzed thoroughly across three complementary dimensions (traffic statistics, temporal, spatial), each at multiple granularity levels. These dimensions and granularities jointly capture the structures of sophisticated attacks that may be invisible from single analytical perspectives. We design a tensor structure that provides a unified encoding for the MDCG features while supporting the use of optimal transport theory to measure the distance between benign traffic and known or unknown attacks. MDCG-IDS uses a semi-supervised learning model that is trained exclusively on benign traffic and validated on a small set of labeled data, significantly reducing the effort of data labeling. Experiments on various datasets achieve AUC-ROC scores of more than 0.948, exceeding the best competing state-of-the-art methods by up to 7%. Regarding the amount of labeled validating data, MDCG-IDS obtains an AUC-ROC score of over 0.94 with only 3% of entire validating samples, outperforming the baseline models.	10.1109/TNSM.2026.3693141
Dinghao Zeng, Fagui Liu, Runbin Chen, Jingwei Tan, Dishi Xu, Qingbo Wu, C.L. Philip Chen	CoreScaler: A Resource-Efficient Hybrid Scaling Framework for Dynamic Workloads in Cloud	2026	Early Access	Resource management Central Processing Unit Memory	Containerized microservices face significant challenges in balancing service quality and resource efficiency under dynamic workloads. Existing approaches suffer from horizontal scaling’s cold start latency, vertical scaling’s resource ceilings, and hybrid methods’ limited adaptability. We present CoreScaler, a resource-efficient hybrid scaling framework based on analysis of CPU usage patterns revealing substantial consumption differences between working mode and waiting mode instances. This insight drives our dual-mode instance management model that distinguishes between working instances actively handling requests and waiting instances maintaining hot standby with minimal resource allocation. CoreScaler employs a master-subordinate distributed architecture where the master node performs capacity planning using multi-confidence interval predictions and contextual multi-armed bandit optimization, while subordinate nodes execute mode-aware CPU quota adjustments. Comprehensive evaluation on a Kubernetes cluster with a typical microservice system under four representative production work-loads demonstrates that CoreScaler maintains SLO compliance while reducing CPU and memory allocation by 22.53% and 30.83% respectively compared to state-of-the-art solutions. The framework achieves substantially higher resource utilization than single-dimension scaling approaches, validating the effectiveness of coordinated hybrid scaling for dynamic cloud environments.	10.1109/TNSM.2026.3692955
Jiahang Pu, Hongyu Ye, Jing Cheng, Feng Shan, Runqun Xiong	Balancing Timeliness and Accuracy: A Hybrid Data-Control Plane Framework for Volumetric DDoS Defense in IoT	2026	Early Access	Modeling Internet of Things Planing	Resource-constrained IoT devices in Industrial Internet environments are highly vulnerable to DDoS attacks due to infrequent security updates and insufficient built-in protection mechanisms. Existing defense solutions primarily rely on external filtering servers or programmable switches, but these approaches fail to simultaneously meet the stringent real-time performance and high accuracy requirements of industrial applications. To address these limitations, we propose a novel cross-plane defense framework that exploits the temporal invariance characteristics of attack traffic patterns. In the data plane, an adaptive variance threshold mechanism immediately mitigates high-volume, low-variance traffic flows, while a bidirectional dual-hash table captures low-collision flow features for efficient export to the control plane. The control plane constructs temporally-enhanced flow sequences that enable deep learning models to perform accurate attack detection, subsequently directing the data plane to block identified malicious sources. We implemented and evaluated a prototype of this framework on a software switch platform using both real-world attack datasets and custom-generated traffic patterns. Experimental results demonstrate that our framework successfully mitigates 86% of attack traffic within milliseconds and achieves complete source blocking within 52 seconds. Compared to baseline methods, our framework can effectively counter both DoS and DDoS attacks without generating false positives on benign traffic.	10.1109/TNSM.2026.3693266
Fátima Khan, Óscar Gil, Luis Diez, Elena Serna Santiago, Luis M. Contreras, Ramón Agüero	Evaluating Fronthaul Network Performance Under the O-RAN Paradigm: A Novel Methodology Based on Queuing Theory	2026	Vol. 23, Issue	Delays 5G mobile communication Open RAN	Open Radio Access Network (O-RAN) fosters a new networking paradigm that supports flexible and open architectures, while enabling the functional disaggregation of traditional, monolithic, base stations. Specifically, the Lower-Layer Split (LLS) option 7.2x divides the PHY layer into Low-PHY at Open - Radio Unit (O-RU) and High-PHY at Open - Distributed Unit (O-DU), leveraging the so-called open fronthaul network, which imposes stringent latency requirements. In this work, we introduce and thoroughly examine the time window relationships that define the latency budget allocated to the open fronthaul network. Additionally, we propose a novel methodology to analyze the delay experienced by a realistic traffic pattern over a spine-leaf topology, accounting for various O-DU locations and site pools. To address the increased traffic load introduced by the fronthaul, we present a theoretical model to characterize the average delay across different priority levels when open fronthaul traffic coexists with flows from other Radio Access Technologies (RATs). This model is then validated and broadened through ns-3 simulations. Our findings show the applicability of the proposed methodology, which could be exploited by network operators in the planning phase, evincing its suitability to obtain benchmark delay values, offering a reliable tool for performance evaluation in open fronthaul networks.	10.1109/TNSM.2025.3646845
Imtiaz Ali Soomro, Hamood Ur Rehman Khan, Syed Jawad Hussain, Adeel Iqbal, Waqas Khalid, Heejung Yu	SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks	2026	Vol. 23, Issue	Internet of Things Accuracy Robustness	The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service (DDoS), malware injection, and data exfiltration. Conventional intrusion detection systems (IDS) face critical challenges like privacy, scalability, and robustness when applied in such heterogeneous IoT environments. To address these issues, we propose SecureDyn-FL, a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks. SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS: (1) poisoning detection through dynamic temporal gradient auditing, (2) privacy protection against inference and eavesdropping attacks through secure aggregation, and (3) adaptation to heterogeneous non-independent-and-identically-distributed (non-IID) data via personalized learning. The framework introduces three core contributions: (i) a dynamic temporal gradient auditing mechanism that leverages Gaussian mixture models (GMMs) and Mahalanobis distance (MD) to detect stealthy and adaptive poisoning attacks, (ii) an optimized privacy-preserving aggregation scheme based on transformed additive ElGamal encryption with adaptive pruning and quantization for secure and efficient communication, and (iii) a dual-objective personalized learning strategy that improves model adaptation under non-IID data using logit-adjusted loss. Extensive experiments on the N-BaIoT dataset under both IID and non-IID settings, including scenarios with up to 50% adversarial clients, demonstrate that SecureDyn-FL consistently outperforms state-of-the-art FL-based IDS defenses. It achieves up to 99.01% detection accuracy, a 98.9% F1-score, and significantly reduced attack success rates across diverse poisoning attacks, while maintaining strong privacy guarantees and computational efficiency for resource-constrained IoT devices.	10.1109/TNSM.2025.3647642
Agrippina Mwangi, León Navarro-Hilfiker, Lukasz Brewka, Mikkel Gryning, Elena Fumagalli, Madeleine Gibescu	A Threshold-Triggered Deep Q-Network-Based Framework for Self-Healing in Autonomic Software-Defined IIoT-Edge Networks	2026	Vol. 23, Issue	Switches Routing Quality of service	Stochastic disruptions such as flash events arising from benign traffic bursts and switch thermal fluctuations are major contributors to intermittent service degradation in software-defined industrial networks. These events violate IEC 61850-derived quality of service requirements and user-defined service-level agreements, hindering the reliable and timely delivery of control, monitoring, and best-effort traffic in IEC 61400-25-compliant wind power plants. Failure to maintain these requirements often results in delayed or lost control signals, reduced operational efficiency, and increased risk of wind turbine generator downtime. To address these challenges, this study proposes a threshold-triggered Deep Q-Network self-healing agent that autonomically detects, analyzes, and mitigates network disruptions while adapting routing behavior and resource allocation in real time. The proposed agent was trained, validated, and tested on an emulated tri-clustered switch network deployed in a cloud-based proof-of-concept testbed. Simulation results show that the proposed agent improves disruption recovery performance by 53.84% compared to a baseline shortest-path and load-balanced routing approach, and outperforms state-of-the-art methods, including the Adaptive Network-based Fuzzy Inference System by 13.1% and the Deep Q-Network and Traffic Prediction-based Routing Optimization method by 21.5%, in a super-spine leaf data-plane architecture. Additionally, the agent maintains switch thermal stability by proactively initiating external rack cooling when required. These findings highlight the potential of deep reinforcement learning in building resilience in software-defined industrial networks deployed in mission-critical, time-sensitive application scenarios.	10.1109/TNSM.2025.3647853
Claudia Canali, Giuseppe Di Modica, Francesco Faenza, Luca Foschini, Riccardo Lancellotti, Domenico Scotece	OptiFog: A Framework to Optimize the Placement of Microservices in Fog Scenarios	2026	Vol. 23, Issue	Microservice architectures Genetic algorithms Quality of service	The Fog computing paradigm makes use of dispersed, diverse, and resource-limited devices located at the network edge to effectively implement Internet of Things (IoT) application services that demand low latency and substantial bandwidth. At the same time, the adoption of microservice-based architectures in the IoT domain is on the rise due to their ability to align with the swift evolution and deployment demands of highly dynamic IoT applications and to elastically scale to fulfill load demands. In complex environments like Fog federations, characterized by highly heterogeneous computing and networking resources, the effective allocation of microservices to available nodes, while ensuring compliance with required Quality of Service (QoS) constraints, represents a significant challenge. In this paper, we present the design and implementation of OptiFog, a comprehensive framework that enables users to model, simulate, and validate microservice placement solutions within a realistic testbed environment. Compared to state-of-the-art approaches, OptiFog offers developers a controlled environment for experimenting with placement solutions while providing the assurance that the resulting deployments will meet the targeted QoS requirements in real-world scenarios, specifically in terms of service execution time and energy consumption of Fog nodes. To demonstrate the feasibility of the proposed approach, we implemented and evaluated a representative use case, involving both sub-optimal and optimal microservice placement, and utilizing real-world microservices drawn from the IoT domain.	10.1109/TNSM.2025.3648449
Xi Liu, Jun Liu, Weidong Li	Strategy-Proof Cost-Sharing Mechanism for Dynamic Adaptability Service in Vehicle Computing	2026	Vol. 23, Issue	Costs Sensors Vehicle dynamics	Vehicle computing has emerged as a promising paradigm for delivering time-sensitive computing services to Internet of Things applications. Intelligent vehicles (IVs) offer onboard computing and sensing capabilities for delivering a wide range of services. In this paper, we propose a dynamic adaptability service model that leverages the swift mobility of vehicles to adjust the distribution of IVs to users’ dynamically changing locations. There are two types of areas in our model: the user area and the parking area. The former is where services are provided, while the latter serves as the preparation zone for backup IVs. IVs in the parking area are dispatched to service areas, where existing vehicle resources cannot meet user demand, and they return to the parking area after delivering the service. Multiple users share sensing resources, and our model allocates the costs among them. To ensure strategy-proofness, we introduce the concepts of no additional cost and allocation stability. We propose a strategy-proof cost-sharing mechanism for dynamic adaptability service. The proposed mechanism achieves no positive transfers, voluntary participation, individual rationality, consumer sovereignty, budget balance, no additional costs, and allocation stability. Moreover, the proposed mechanism’s approximation performance is analyzed. We further use comprehensive simulations to verify the effectiveness and efficiency of the proposed mechanism.	10.1109/TNSM.2025.3646778
Yeryeong Cho, Sungwon Yi, Soohyun Park	Joint Multi-Agent Reinforcement Learning and Message-Passing for Resilient Multi-UAV Networks	2026	Vol. 23, Issue	Servers Heuristic algorithms Autonomous aerial vehicles	This paper introduces a novel resilient algorithm designed for distributed un-crewed aerial vehicles (UAVs) in dynamic and unreliable network environments. Initially, the UAVs should be trained via multi-agent reinforcement learning (MARL) for autonomous mission-critical operations and are fundamentally grounded by centralized training and decentralized execution (CTDE) using a centralized MARL server. In this situation, it is crucial to consider the case where several UAVs cannot receive CTDE-based MARL learning parameters for resilient operations in unreliable network conditions. To tackle this issue, a communication graph is used where its edges are established when two UAVs/nodes are communicable. Then, the edge-connected UAVs can share their training data if one of the UAVs cannot be connected to the CTDE-based MARL server under unreliable network conditions. Additionally, the edge cost considers power efficiency. Based on this given communication graph, message-passing is used for electing the UAVs that can provide their MARL learning parameters to their edge-connected peers. Lastly, performance evaluations demonstrate the superiority of our proposed algorithm in terms of power efficiency and resilient UAV task management, outperforming existing benchmark algorithms.	10.1109/TNSM.2025.3650697