TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Suyong Eum, Shin’ichi Arakawa, Masayuki Murata	Deterministic and Probabilistic Scheduling for Latency Guarantees in B5G/6G Network Management	2026	Early Access	Delays Probabilistic logic Ultra reliable low latency communication	In the era of Beyond 5G (B5G) and 6G networks, ensuring efficient resource management and meeting stringent quality of service (QoS) requirements are crucial. This paper proposes the Deterministic and Probabilistic Scheduling for Latency Guarantees (DPSLG) algorithm, which provides Worst-Case Delay (WCD) guarantees, both deterministically and probabilistically, to support Ultra-Reliable Low-Latency Communication (URLLC) applications. Deterministic guarantees ensure strict delay bounds for mission-critical scenarios, while probabilistic guarantees offer flexibility by accommodating dynamic traffic conditions with controlled threshold violations. The proposed algorithm leverages the Lyapunov optimization framework for deterministic delay bounds in dynamic environments and integrates Extended Conformal Quantile Regression (ECQR) to enable probabilistic guarantees. This combination enhances reliability and adaptability under diverse traffic conditions. Furthermore, constraint mechanisms are incorporated to mitigate the impact of misbehaving users and improve overall system performance. This work significantly advances the management of radio resources in B5G and 6G networks by addressing key challenges related to latency and efficiency. It establishes a robust framework for optimizing scheduling mechanisms, paving the way for future innovations in managing next-generation networks to meet stringent performance and reliability demands.	10.1109/TNSM.2026.3657735
Awaneesh Kumar Yadav, An Braeken, Madhusanka Liyanage	A Provably Secure Lightweight Three-factor 5G-AKA Authentication Protocol relying on an Extendable Output Function	2026	Early Access	Authentication Protocols Security	Compared to 4G, the designed authentication and key agreement protocol for 5G communication (5G-AKA) offers better security. State-of-the-art shows that various protocols indicate the flaws in the 5G-AKA and suggest solutions primarily for the desynchronization attack, traceability attack, and perfect forward secrecy. However, most authentication protocols fail to facilitate the device stolen attack and are expensive; they also do not consider the prominent security issues such as post-compromise security and non-repudiation. Considering the above demerits of these protocols and the necessity to offer additional security, a provably secure lightweight 5G-AKA multi-factor authentication protocol relying on an extendable output function is proposed. The security of the proposed work has been confirmed informally and formally (ROR logic, GNY logic, and Scyther tool) to ensure that the proposed work handles all types of attacks and offers additional security features, such as post-compromise features and non-repudiation. Furthermore, we compute the performance of the proposed work and compare it with its counterparts to show that our work is less costly and more suitable for lightweight devices than others in terms of computational, communication, storage, and energy consumption cost.	10.1109/TNSM.2026.3656167
Qian Yang, Suoping Li, Jaafar Gaber, Sa Yang	An Optimal Matching Channel Selection Strategy Based on (K+1)-layer 3-D CTMC for Suppressing Spectrum Fragmentation in 5G/B5G Cognitive Radio Ad Hoc Networks	2026	Early Access	Copper Three-dimensional displays Cognitive radio	Dynamic spectrum access (DSA) is one of the pivotal technologies that is widely recognized to be able to cope with the massive demand for limited spectrum resources by massive data in 5G/B5G networks. To address spectrum fragmentation and sharing in 5G/B5G cognitive radio ad hoc networks (CRAHNs), based on the DSA technique, this paper proposes an optimal matched channel selection strategy with finite buffer (OMCS-FB). In the OMCS-FB, a cognitive user (CU) with the transmission request selects the channel whose idle time optimally matches its transmission time rather than selecting the channel with the longest idle time; if the CU fails to access the channel, the CU enters the buffer and waits for the next transmission opportunity. A (K+1)-layer continuous-time Markov chain (CTMC) with the number of primary users (PUs) and CUs in primary channels and the number of CUs in the buffer as 3-D metrics is established, which can effectively portray the activity behavior of users and the occupancy states of primary channels under the OMCS-FB. The CTMC rate steady-state equations are then solved using the successive over-relaxation (SOR) iterative algorithm to obtain the system steady-state probability distributions and performance metrics. The results show that the OMCS-FB effectively suppresses spectrum fragmentation of the MAC layer in the time dimension and enables efficient spectrum sharing among CUs and PUs, as verified by Monte Carlo simulation.	10.1109/TNSM.2026.3656378
Xinshuo Wang, Lei Liu, Baihua Chen, Yifei Li	ENCC: Explicit Notification Congestion Control in RDMA	2026	Early Access	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 through-put improvements over representative baseline algorithms, with gains of up to 16.6× in representative scenarios, while incurring minimal additional latency.	10.1109/TNSM.2026.3656015
Ze Wei, Rongxi He, Chengzhi Song, Xiaojing Chen	Differentiated Offloading and Resource Allocation with Energy Anxiety Level Consideration in Heterogeneous Maritime Internet of Things	2026	Early Access	Internet of Things Resource management Carbon footprint	The popularity of maritime activities not only exacerbates the carbon footprint (CF) but also places higher demands on Maritime Internet of Things (MIoTs) to support heterogeneous MIoT devices (MIoTDs) with different prioritized tasks. High-priority tasks can be processed cooperatively via local computation, offloading to nearby MIoTDs (helpers), or offloading to edge servers to ensure their timely and successful completion. Due to the differences in energy availability and rechargeability, MIoTDs exhibit distinct energy states, impacting their operational behaviors. We propose the Energy Anxiety Level (EAL) to quantify these states: Higher EAL tends to lead to increased packet dropping and earlier shutdown. Although low-EAL MIoTDs seem preferable as helpers, their scarce residual computational resources after local task completion may cause offloaded high-priority tasks to drop or time out. Therefore, helper selection should jointly consider candidate MIoTDs’ EALs and loads to evaluate their unsuitability. This paper addresses the problem of differentiated task offloading and resource allocation in MIoTs by formulating it as a mixed integer nonlinear programming model. The objective is to minimize system-wide carbon footprint (CF), packet loss, helper unsuitability risk, and high-priority task latency. To solve this complex problem, we decompose it into two subproblems. We then design algorithms to determine optimal offloading patterns, task partitioning factors, MIoTD transmission powers, and computation resource allocation for MIoTDs and edge servers. Simulation results demonstrate that our proposal outperforms benchmarks in reducing CF and EAL, lowering high-priority task latency, and improving task completion ratio.	10.1109/TNSM.2026.3655385
Divya D Kulkarni, Manit Baser, Mohan Gurusamy	ARCANE: Adversarial Resilience and Adaptive Network Slicing for UAV-based MEC	2026	Early Access	Autonomous aerial vehicles Servers Power demand	Network slicing and Multi-access Edge Computing (MEC) are pivotal elements of 5G communication technology, enabling diverse, low-latency services to distributed users. Unmanned Aerial Vehicles (UAVs) are being increasingly explored in delivering these services temporarily to remote locations, supporting surveillance in regions with restricted ground connectivity, monitoring urban traffic, and disaster relief. However, the resource constraints of UAVs demand efficient optimization strategies. While Artificial Intelligence (AI)-driven methods like Deep Reinforcement Learning (DRL) offer promising potential in optimizing service delays and minimizing power consumption with fewer UAVs, they remain vulnerable to adversarial attacks. This study evaluates two adversarial attacks against DRL baselines: a targeted service disruption attack that impacts the DRL environment to degrade decision-making and service quality, and an action bit-flipping attack that alters UAV selection, resulting in suboptimal provisioning. To address these vulnerabilities, we propose ARCANE, a resilient DRL-based multi-slice MEC framework for UAVs. ARCANE introduces the Exploratory-Thompson Deep-Q Network (ET-DQN), which leverages Thompson Sampling to effectively balance exploration and exploitation under adversarial conditions, optimizing UAV selection for MEC provisioning. Extensive experiments demonstrate that ARCANE outperforms baseline approaches, achieving ~ 4× faster mitigation of the environmental attack and ~ 2× quicker recovery from the attack on the actions. Moreover, we illustrate that ARCANE demonstrates strong resilience by effectively limiting the degradation in hovering time caused by the attacks.	10.1109/TNSM.2026.3656271
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
Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Mrityunjoy Gain, Zhu Han, Choong Seon Hong	Age of Sensing Empowered Holographic ISAC Framework for NextG Wireless Networks: A VAE and DRL Approach	2026	Early Access	Array signal processing Resource management Integrated sensing and communication	This paper proposes an AI framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)-assisted base station (BS)-enabled wireless network. The AI-driven framework aims to achieve optimized power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO BS for serving the users. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the communication signal-to-interference-plus-noise ratio (SINRc) of the received signals and beam-pattern gains to improve the sensing SINR of reflected echo signals, which in turn maximizes the achievable rate of users. A novel AI-driven framework is presented to tackle the formulated NP-hard problem that divides it into two problems: a sensing problem and a power allocation problem. The sensing problem is solved by employing a variational autoencoder (VAE)-based mechanism that obtains the sensing information leveraging AoS, which is used for the location update. Subsequently, a deep deterministic policy gradient-based deep reinforcement learning scheme is devised to allocate the desired power by activating the required grids based on the sensing information achieved with the VAE-based mechanism. Simulation results demonstrate the superior performance of the proposed AI framework compared to advantage actor-critic and deep Q-network-based methods, achieving a cumulative average SINRc improvement of 8.5 dB and 10.27 dB, and a cumulative average achievable rate improvement of 21.59 bps/Hz and 4.22 bps/Hz, respectively. Therefore, our proposed AI-driven framework guarantees efficient power allocation for holographic beamforming through ISAC schemes leveraging AoS.	10.1109/TNSM.2026.3654889
Zhenyang Guo, Jin Cao, XiongPeng Ren, Yuchen Zhou, Lifu Cheng, Peijie Yin, Hui Li	LDST-UAVS: A Lightweight Data Secure Transmission Protocol for Unmanned Aerial Vehicle Swarms in Emergency Rescue Scenarios	2026	Early Access	Autonomous aerial vehicles Security Protocols	Currently, Unmanned Aerial Vehicles (UAV) groups can quickly build a multi-hop transmission network, which have been widely utilized in emergency communication scenarios to perform search and rescue, environmental monitoring, personnel positioning, rapid networking, etc. In such emergency rescue situations, strict demands on real-time communication, security, and minimal resource consumption become paramount. Higher requirements for security, bandwidth, and real-time performance necessitate a secure and lightweight data transmission protocol. Additionally, due to the lack of personnel supervision in these scenarios, the probability of malicious nodes increases. Therefore, it is essential to quickly and proximally block malicious nodes’ data to prevent it from affecting subsequent network propagation, and to accurately identify the malicious nodes. To address these issues, in this paper, we propose a traceable, lightweight, and secure data transmission protocol for UAV multi-hop networks in emergency rescue scenarios. The proposed protocol can verify the integrity of data transmitted by a large number of nodes in real time, detect erroneous transmissions, and trace malicious users. Experimental results show that our protocol consistently outperforms the comparison schemes in terms of computational overhead. Moreover, in scenarios involving smaller groups (m=5) and fewer hops (n=4), it exhibits significantly lower communication bandwidth overhead than the reference methods. Security analysis using BAN logic and the formal verification tool Scyther indicates that the proposed scheme meets security requirements. Additionally, comparative analysis results demonstrate that the proposed scheme is highly effective and outperforms other related schemes under the unique constraints of emergency rescue scenarios, where rapid, secure decision-making and data transmission are critical.	10.1109/TNSM.2026.3656973
Xiujun Xu, Qi Wang, Qingshan Wang, Yinlong Xu	Contract-Based Incentive Mechanism for Long-term Participation in Federated Learning	2026	Early Access	Contracts Data models Computational modeling	Federated learning (FL), as a newly-developing technique, brings the advantage of organizing multiple participants to learn together, while avoiding the leakage of their privacy information. Contract theory provides an effective incentive mechanism to encourage participants to participate in FL. Existing contract-based incentive mechanisms consider participants’ types but ignore the different contributions of participants within the same type during the training.This paper first introduces a metric, reputation, to evaluate the contribution of participants in each iteration, and then proposes a hybrid contract mechanism consisting of a short-term contract and a long-term contract. Only the participants with reputations higher than a pre-defined threshold can sign the long-term contract. We formulate the solution of the long-term contract mechanism as an optimization problem with constraints. We further simplify the constraints of the long-term contract optimization problem, and theoretically analyze the correctness of the simplification to greatly reduce its computational complexity. We prove that the model owner achieves more profit with the hybrid contract mechanism. Simulations with the MNIST dataset show that the long-term contract improves the model accuracy by at least 5% compared with the existing contracts. Furthermore, compared with the short-term contract, participants signing the long-term contract are granted more rewards.	10.1109/TNSM.2026.3657419
Somchart Fugkeaw, Kittipat Tangtanawirut, Pakapon Rattanasrisuk, Archawit Changtor	MK-WISE: Secure and Efficient Multi-Keyword Wildcard ABSE with Keyword-Level Revocation for Device–Edge–Cloud EHRs Data Sharing	2026	Early Access	Encryption Cryptography Access control	The rapid proliferation of Internet of Things (IoT) in healthcare has transformed the management of Electronic Health Records (EHRs), but also introduced critical challenges in secure retrieval, dynamic revocation, and verifiable integrity over encrypted data. Existing Searchable Encryption (SE) and Attribute-Based Searchable Encryption (ABSE) models remain limited: (i) most support only exact or prefix keyword matching and cannot handle flexible wildcard or substring queries common in medical search; (ii) revocation is coarse-grained, often requiring costly key redistribution or ciphertext re-encryption; and (iii) integrity verification either incurs heavy blockchain overhead or exposes access structures, undermining privacy. To address these gaps, we propose MK-WISE, a secure and efficient multi-keyword wildcard ABSE framework for IoT–EHR systems. MK-WISE integrates an Index–Wildcard Tree (IWT) with Substring Bloom Filters (SBF) to enable expressive wildcard and substring queries, employs a puncturable PRF–based revocation workflow with edge-local enforcement, hierarchical key updates, and optional blockchain anchoring, and incorporates homomorphic MACs for lightweight correctness and completeness verification. Security analysis proves that MK-WISE achieves confidentiality, keyword privacy, unlinkability, and revocability under standard assumptions. Experimental results demonstrate that MK-WISE significantly outperforms state-of-the-art schemes in trapdoor generation, search scalability, and revocation cost, achieving millisecond-level revocation without user disruption. These results highlight MK-WISE as a practical and comprehensive solution for privacy-preserving EHR retrieval in IoT-enabled healthcare.	10.1109/TNSM.2026.3657982
Fengqi Li, Yudong Li, Lingshuang Ma, Kaiyang Zhang, Yan Zhang, Chi Lin, Ning Tong	Integrated Cloud-Edge-SAGIN Framework for Multi-UAV Assisted Traffic Offloading Based On Hierarchical Federated Learning	2026	Early Access	Resource management Autonomous aerial vehicles Heuristic algorithms	The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.	10.1109/TNSM.2026.3658833
Islam Elgarhy, Mahmoud M. Badr, Ahmed T. Eltoukhy, Mohamed Mahmoud, Tariq Alshawi, Maazen Alsabaan, Mostafa Fouda	Interpretable Detector Secure Against Stealthy False Power Consumption Attacks	2026	Early Access	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. Deeplearning-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
Shagufta Henna, Upaka Rathnayake	Hypergraph Representation Learning-Based xApp for Traffic Steering in 6G O-RAN Closed-Loop Control	2026	Early Access	Open RAN Resource management Ultra reliable low latency communication	This paper addresses the challenges in resource allocation within disaggregated Radio Access Networks (RAN), particularly when dealing with Ultra-Reliable Low-Latency Communications (uRLLC), enhanced Mobile Broadband (eMBB), and Massive Machine-Type Communications (mMTC). Traditional traffic steering methods often overlook individual user demands and dynamic network conditions, while multi-connectivity further complicates resource management. To improve traffic steering, we introduce Tri-GNN-Sketch, a novel graph-based deep learning approach employing Tri-subgraph sampling to enhance link prediction in Open RAN (O-RAN) environments. Link prediction refers to accurately forecasting optimal connections between users and network resources using current and historical measurements. Tri-GNN-Sketch is trained on real-world 4G/5G RAN monitoring data. The model demonstrates robust performance across multiple metrics, including precision, recall, F1 score, and ROC-AUC, effectively modeling interfering nodes for accurate traffic steering. We further propose Tri-HyperGNN-Sketch, which extends the approach to hypergraph modeling, capturing higher-order multi-node relationships. Using link-level simulations based on Channel Quality Indicator (CQI)-to-modulation mappings and LTE transport block size specifications, we evaluate throughput and packet delay for Tri-HyperGNN-Sketch. Tri-HyperGNN-Sketch achieves an exceptional link prediction accuracy of 99.99% and improved network-level performance, including higher effective throughput and lower packet delay compared to Tri-GNN-Sketch (95.1%) and other hypergraph-based models such as HyperSAGE (91.6%) and HyperGCN (92.31%) for traffic steering in complex O-RAN deployments.	10.1109/TNSM.2026.3654534
Muhammad Umar Farooq Qaisar, Weijie Yuan, Lin Zhang, Shehzad Ashraf Chaudhry, Guangjie Han, Yunyang Zhang	A Robust Trust Management System for V2X Networks Integrating ISAC with Blockchain Smart Contracts	2026	Early Access		Vehicle-to-everything (V2X) networks face critical security challenges due to their dynamic nature, stringent latency requirements, and susceptibility to malicious attacks. Traditional trust management approaches often rely on centralized authorities or historical data, creating vulnerabilities and scalability limitations. This paper presents a new trust management system that leverages integrated sensing and communication (ISAC) technology and blockchain-based smart contracts to provide secure and decentralized trust evaluation in V2X networks. The proposed framework leverages real-time ISAC signal processing to compute five comprehensive trust metrics: behavior score, reputation score, safety score, uptime score, and response time score. These metrics are derived through advanced Kalman filtering and statistical anomaly detection applied to physical-layer measurements, enabling immediate detection of malicious activities that traditional approaches might miss. Trust records are securely stored and validated through smart contracts deployed on 5G base station blockchains, ensuring tamper-proof storage and automated policy enforcement. Numerical results demonstrate that the proposed protocol achieves faster trust convergence, higher communication reliability, significant reduction in false positive rates, improved detection accuracy, acceptable end-to-end latency, and lower computational overhead compared to state-of-the-art approaches.	10.1109/TNSM.2026.3658589
Zhihao Wen, Weishi An, Chuanhua Wang, Quanbo Ge, Thippa Reddy Gadekallu, Hailin Feng, Kai Fang	EK-IGNN: Defending Meteorological Networks Against Covert Attacks using EMD-Kalman Noise Fingerprinting and Intrinsic Graph Neural Networks	2026	Early Access		The meteorological communication networks provide critical data support for agriculture and environmental monitoring. However, covert gradient-based attacks persistently inject subtle perturbations, threatening data integrity and increasing the operational overhead for network operators. To achieve proactive service assurance and security-aware network management, this paper proposes a data integrity monitoring mechanism as a managed network function, named EK-IGNN. Unlike traditional passive detection, EK-IGNN functions as an active security service. It first employs the Empirical Mode Decomposition Kalman Filter (EMD-KF) to extract high-fidelity attack fingerprints, which are then analyzed by an Intrinsic Graph Neural Network (IGNN). The IGNN model captures complex dependencies and adaptively amplifies weak attack features, enabling closed-loop network security management. Experimental results demonstrate that the proposed algorithm achieving an average improvement of 16.07% in accuracy and 15.27% in F1-score over state-of-the-art benchmarks.	10.1109/TNSM.2026.3659226
Surendra Kumar, Jitendra Kumar Samriya, Rajeev Tiwari, Mohit Kumar, Shilpi Harnal, Neeraj Kumar, Mohsen Guizani	A Dynamic PAPR Reduction Method Using PTS-ESSA for MIMO Generalized FDM Wireless System	2026	Vol. 23, Issue	5G mobile communication Partial transmit sequences Heuristic algorithms	Generalized Frequency Division Multiplexing (GFDM) is considered a strong candidate to replace Orthogonal Frequency Division Multiplexing (OFDM) in 5G MIMO networks because of its enhanced spectral utilization and design flexibility. Despite these advantages, GFDM faces the drawback of producing a relatively high Peak-to-Average Power Ratio (PAPR), which limits the efficiency of power amplifiers. To address this issue, the Partial Transmit Sequence (PTS) method is often employed for PAPR reduction. Nevertheless, the effectiveness of PTS is hindered by the intensive computational effort required for searching multiple phase factors. To overcome this challenge, we propose a method that integrates the Enhanced Squirrel Search Algorithm (ESSA) with an adaptive parameter control mechanism and a Grey Wolf Optimizer (GWO), enabling a dynamic balance between exploration and exploitation during phase factor selection. This improvement reduces the computational overhead, accelerates the convergence, and enhances the robustness of the phase sequence optimization. Simulation results show that the Hybrid PTS-ESSA-GWO-RPSM model achieves superior PAPR reduction compared to conventional ESSA-based approaches, while also providing better BER and SNR performance under varying channel conditions. The proposed method therefore offers an efficient trade-off between complexity and PAPR reduction, making it suitable for practical deployment in MIMO-GFDM-based 5G systems. The proposed scheme is evaluated against related methods by analyzing key performance indicators, including Complementary Cumulative Distribution Function (CCDF), Bit Error Rate (BER), Peak-to-Average Power Ratio (PAPR), and Signal-to-Noise Ratio (SNR).	10.1109/TNSM.2025.3619945
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
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
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