TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Pedro Martinez-Julia, Ved P. Kafle, Hitoshi Asaeda	Intelligent Network Service Automation: Improving Accuracy and Efficiency in Network Management	2025	Early Access	Telemetry Accuracy Artificial intelligence	Network virtualization offers enormous flexibility to construct network services tailored to the needs of network tenants and users. Network services can be implemented through composition of virtual network functions, which can be deployed in multiple sites, forming multi-site network services. However, network virtualization further induces the increasing complexity of the network, which, in turn, increases the difficulty of error avoidance in management operations. Thus, network management must be automated to tackle such complexity and eliminate errors. In this paper, we propose the intelligent network service automation system (INSA), and we demonstrate its high efficiency and accuracy in automated network service construction and management. INSA is designed to transparently orchestrate all elements of the management system using a control plane based on publish/subscribe messaging, known as a management service bus. This enables INSA to make highly accurate decisions using vast telemetry information without overloading the control plane. Additionally, INSA integrates key enablers such as efficient and scalable control plane networking, telemetry compression, distributed processing, hidden fault detection, and autonomic resource control. We evaluated INSA both theoretically and through a prototype implementation. The former proves our claims regarding the efficiency improvement provided by compression and distribution. The latter showcases INSA management accuracy and performance, and it also demonstrates how INSA manages multi-site network services. The evaluation results show that, in comparison with the state-of-the-art alternatives, INSA uses 15–36% less CPU time, possesses 80% higher scalability, and improves accuracy by 33%.	10.1109/TNSM.2025.3541225
Eiji Oki, Ryotaro Taniguchi, Kazuya Anazawa, Takeru Inoue	Design of Multiple-Plane Twisted and Folded Clos Network Guaranteeing Admissible Blocking Probability	2025	Early Access	Optical switches Switching circuits Optical fiber networks	Future advancements in data centers are anticipated to incorporate advanced circuit switching technologies, especially optical switching, which achieve high transmission capacity and energy efficiency. Previous studies addressed a Clos-network design problem to guarantee an admissible blocking probability to maximize the switching capacity, which is defined by the number of terminals connected to the network. However, as the number of available N×N switches increases, the switching capacity no longer increases due to the switch port limitation. This paper proposes a design of a multiple-plane twisted-folded (TF) Clos network, named MP-TF, to enhance the switching capacity, which is limited by the original TF-Clos, by guaranteeing an admissible blocking probability. MP-TF consists of identical M TF-Clos planes and pairs of a 1×M selector and an M×1 selector, each pair of which is associated with a transmitter and receiver pair. We formulate a design model of MP-TF as an optimization problem to maximize the switching capacity. We introduce connection admission control in MP-TF, named MP-CAC. We derive the theorem that the MP-TF design model using MP-CAC guarantees the admissible blocking probability. Numerical results observe that MP-TF increases the switching capacity as the number of TF-Clos planes when available N×N switches are sufficient; for example, with seven planes, the switching capacity is 1.97 times larger than that of one plane, given a request active probability of 0.6 and an admissible blocking probability of 0.01. We find that the computation time for MP-TF diminishes with an increase in the number of TF-Clos planes. Designing MP-TF is similar to designing a single TF-Clos plane, differing mainly in the handling of connection admission control. With a larger number of N×N switches, MP-TF enables the design of a smaller TF-Clos plane. We provide the analyses of optical power management and network cost of MP-TF.	10.1109/TNSM.2025.3539907
Jie Zhang, De-Gan Zhang, Meng Qiao, Hong-Lin E, Ting Zhang, Ping Zhang	New Offloading Method of Computing Task Based on Gray Wolf Hunting Optimization Mechanism for the IOV	2025	Early Access	Heuristic algorithms Optimization Servers	Task offloading, as an effective solution, provides low latency and sufficient computing resources for mobile users in the network. However, how to reasonably offload to reduce system overhead is a challenging issue today. This article takes user terminals, edge servers, and idle vehicles with resources as the network structure, and is inspired by the highly social nature of the gray wolf pack. It proposes a new offloading method of edge computing task based on hunting optimizing mechanism of gray wolf for the Internet of Vehicle (IOV). Firstly, an adaptive weight factor is proposed to balance the weight ratio of delay and energy consumption in the system cost under the constraints of delay and energy consumption. With delay and computing resources of vehicles and servers as constraints, a multi constraint minimization system cost problem is proposed. Secondly, the hunting process of the gray Wolf optimization algorithm is used to find the optimal solution of the unloading scheme, The Levy flight strategy was added to enhance the global search ability of the algorithm, and a dynamic weight strategy was introduced to improve the convergence performance of the algorithm. Finally, the improved gray Wolf optimization algorithm was used to solve the optimal unloading plan and minimum cost.The simulation results show that compared with traditional gray Wolf optimization algorithm offloading schemes, the proposed scheme in this paper requires lower system costs.	10.1109/TNSM.2025.3539865
Vikash Kumar Bhardwaj, Gagan Mundada, Omm Prakash Sahoo, Mahendra K. Shukla, Om Jee Pandey	SINR-Delay Constrained Node Localization in RIS-Assisted Time-Varying IoT Networks Using ML Frameworks	2025	Early Access	Reconfigurable intelligent surfaces Location awareness Interference	Node localization in time-varying Internet of Things (IoT) networks is an essential problem due to increased delay and poor Signal-to-Interference plus Noise Ratio (SINR) at the Base Station (BS). To improve the received signal strength at the BS, Reconfigurable Intelligent Surface (RIS) has recently been used between transmitter and receiver. Additionally, novel phase prediction methods and optimal weight assignment frameworks have been proposed over RIS and BSs, respectively. Nevertheless, these methods suffer from poor performance due to their heuristic approach, resulting in more time consumption and poor SINR. Motivated by the aforementioned challenges, we propose a novel node localization method over a RIS-assisted time-varying IoT network using Machine Learning (ML) frameworks in this work. Firstly, the method computes the optimal phase configuration over the RIS corresponding to each element using coeff2phaseNN, which has been trained on channel coefficients among the transmitter, receiver, and RIS. Subsequently, the weight of the individual antenna element at the BS is optimized using the proposed VectorSync model. The results confirm that the coeff2phaseNN method demonstrates a reduction of 89.79% in total MSE loss compared to the Artificial Neural Network-RIS (ANN-RIS) method. Additionally, it demonstrates a 71.04% reduction in the absolute RIS phase prediction deviation from the optimal phase compared to the ANN-RIS method. Moreover, the proposed VectorSync method attains a 79.28% and 92.29% reduction in time required for optimal weight assignment compared to the Bartlett and Capon methods, respectively. Finally, the Localization Error(LR) using the proposed method is compared to conventional methods in a time-varying experimental scenario and found to be the minimum, i.e., 6.156%.	10.1109/TNSM.2025.3539711
Abderrafi Abdeddine, Youssef Iraqi, Loubna Mekouar	An Efficient Task Allocation in Mobile Crowdsensing Environments	2025	Early Access	Sensors Resource management Wireless sensor networks	Mobile Crowdsensing (MCS) is gaining attention for large-scale sensing that involves three types of entities: task requesters, workers equipped with sensing devices, and the platform that assigns tasks to workers considering their objectives and constraints. However, finding an allocation solution that satisfies the conditions above is NP-hard. A few studies suggested approximate solutions to this problem, focusing on one of the task’s objectives: coverage maximization. Yet, they implement it in a single-task environment or with weak objective consideration, i.e., they consider other objectives, reducing the utility the task will receive. This study proposes a task allocation that focuses only on maximizing the task coverage, where we improved the solution to consider future task coverage possibilities. We consider an opportunistic MCS environment in which sensing has no impact on user trajectories. We assume a one-to-many matching where a task can be assigned to several workers, while a worker can be matched to at most one task. We first formulate the problem mathematically and prove it to be NP-hard. Then, we design three heuristic-based solutions that are more efficient and perform extensive performance evaluations based on a real-world dataset. Each solution improves the data quality and has a maximum execution time of milliseconds.	10.1109/TNSM.2025.3540293
Liang Feng, Cunqing Hua, Jianan Hong	Zero-Determinant Incentive Strategy for Transaction Trading in Blockchain System	2025	Early Access	Consensus protocol Buildings Costs	Blockchain has been widely applied in many industries to provide secure and reliable services, in which the activities of the participating nodes are recorded as transactions. Although the original design assumes nodes disseminate the transactions voluntarily, they may be reluctant to provide transactions for others due to the lack of cooperative incentives. To fill the gap, we study the transaction collecting process in the blockchain system under the leader-based consensus protocol. Specifically, we design an incentive scheme to reward the followers if they provide unique transactions to the leader. Considering the selfish nature of different nodes, we model the transaction trading process between nodes as an Iterated Prisoner’s Dilemma (IPD), and a modified zero-determinant (ZD) strategy is proposed such that the follower could correlate the leader’s payoff with the leader’s cooperation probability. We theoretically prove the effectiveness of our proposed algorithm. Simulation results show the leader’s payoff changes under the follower’s different control functions. The proposed scheme can regulate the behavior of blockchain nodes during the transaction trading process.	10.1109/TNSM.2025.3540036
Zhiwei Wang	A Decentralized Oracle Network Constructed From Weighted Schnorr Multisignature	2025	Early Access	Blockchains Contracts Interoperability	A decentralized oracle network is a good solution for blockchain interoperability, and a multisignature is a proper cryptographic primitive for off-chain aggregation where each participating signer’s public key can be identified during verification. An important requirement for the decentralized oracle network is that some important data requests may require high-reputation nodes to validate the external data, while some common data requests may need only low-cost nodes to execute the validation. Thus, we present a weighted Schnorr multisignature to meet this requirement, which is proven to be unforgeable. However, purely relying on the cryptographic scheme cannot fully identify each participating node’s reputation; thus, we design three on-chain contracts for recording and identifying the oracle nodes’ reputation and realizing the reword mechanism. The on-chain components (e.g., smart contracts) and the off-chain components (e.g., oracle nodes) constitute a whole blockchain interoperability system. We implement our system over the Ethereum platform and analyze its on-chain and off-chain costs.	10.1109/TNSM.2025.3539615
Mani Shekhar Gupta, Akanksha Srivastava, Krishan Kumar	PORA: A Proactive Optimal Resource Allocation Framework for Spectrum Management in Cognitive Radio Networks	2025	Early Access	Resource management Switches Sensors	Cognitive radio network with proactive resource allocation to identify unused spectrum bands and utilize them opportunistically is observed as an evolving technology to handle spectrum scarcity problem. However, it is a challenging problem to predict the accurate information about availability of unused resources due to randomness in licenced user appearance and high mobility at the cost of minimizing sensing time. To address this issue, we mathematically model the metrics like resource availability probability, resource allocation time, throughput, connection continuance probability, and the expected number of networks switching to propose a proactive optimal resource allocation (PORA) technique. Furthermore, the performance of proposed PORA technique is analysed under different traffic environments such as low, moderate, and high traffic. The results show that the proposed PORA technique addresses challenges related to providing resource allocation in proactive manner over the traditional techniques.	10.1109/TNSM.2025.3540717
Saihua Cai, Han Tang, Jinfu Chen, Tianxiang Lv, Wenjun Zhao, Chunlei Huang	GSA-DT: A Malicious Traffic Detection Model Based on Graph Self-Attention Network and Decision Tree	2025	Early Access	Feature extraction Telecommunication traffic Decision trees	Malicious attack has shown a rapid growth in recent years, it is very important to accurately detect malicious traffic to defend against malicious attacks. Compared with machine learning and deep learning technologies, graph convolutional neural network (GCN) achieves better detection results of malicious traffic due to additional consideration of the correlation between network traffic features. However, existing GCN-based detection models suffer from fixed weight assignment, only focusing on local features, lack the ability to model graph structure and relationships as well as having gradient disappearance. To solve these problems, this paper proposes the GSA-DT model based on graph self-attention network and decision tree. GSA-DT first preprocesses the original network traffic to obtain better traffic features and labels, and then uses GCN to extract the topological structure of network traffic as well as capture the correlation relationships among traffic features, where the ReLU activation function is replaced by LeakyReLU to overcome the problems of neuron “death” and gradient disappearance during the training process; It also introduces the self-attention mechanism into GCN to assign larger weights to the key features to reduce the interference of redundant features. Finally, GSA-DT uses decision tree to perform the detection of malicious traffic. Experimental results on five network traffic datasets show that GSA-DT model improves the detection accuracy over 1% on average than seven advanced malicious traffic detection models, and it also performs better in F1-measure, TPR, FPR as well as stability.	10.1109/TNSM.2025.3531885
Neha Sharma, Mayank Swarnkar, Divyanshu	DLAZE: Detecting DNS Tunnels Using Lightweight and Accurate Method for Zero-Day Exploits	2025	Early Access	Tunneling Feature extraction Computer crime	Domain Name System (DNS) protocol is highly targeted nowadays for creating tunnels and extracting information from the intended machines. The reason for such exploitation is that DNS is passed unchecked by most firewalls and Intrusion Detection Systems (IDSs) to maintain the network’s quality of service. Most detection methods utilize the signatures of tunneled queries and tools for DNS tunnel detection. However, the new or updated tool versions bypass these signature-based methods. Moreover, DNS generally comprises a significant portion of total network traffic with a skewed distribution of legitimate DNS traffic against DNS tunnels. Thus, checking each DNS packet against signatures is a bottleneck to the efficiency of the network. To resolve this problem, we propose DLAZE, which can efficiently detect known and unknown DNS tunnels in the network traffic without compromising the efficiency of the network. DLAZE consists of a three-layer system. The first layer utilizes our already proposed work OptiTuneD, which filters out nearly all legitimate DNS packets with linear time complexity and solves the problem of the skewed distribution of legitimate vs tunneled DNS. The remaining packets are passed to the second layer, which uses the Bidirectional Encoder Representations from Transformers (BERT) model to identify legitimate DNS packets that remained unidentified at the first layer with the quadratic time complexity. The third layer obtains only unknown or zero-day DNS packets that can be legitimate or tunnels, which are differentiated using the Probing method with constant time complexity. We tested DLAZE using three publicly available datasets. The experimental results show that the average recall, precision, and F1-score obtained on all three datasets are 98.74%, 97.46%, and 97.95%, respectively, with the average processing time for each DNS packet as 473.25 milliseconds.	10.1109/TNSM.2025.3541234
Luis Velasco, Gianluca Graziadei, Sima Barzegar, Marc Ruiz	Provisioning of Time-Sensitive and non-Time-Sensitive Flows With Assured Performance	2025	Early Access	Quality of service Delays Resource management	Time-Sensitive Networking (TSN) standards provide scheduling and traffic shaping mechanisms to ensure the coexistence of Time-Sensitive (TS) and non-TS traffic classes on the same network infrastructure. Nonetheless, much effort is still needed on the operation of such TSN capable network infrastructure to ensure that the required performance of the different flows, defined in terms of key performance indicators, can be met once the flows are deployed in the network. In this paper, we focus on such aspects and propose a solution involving network-wide scheduling for TS flows, as well as performance estimation for non-TS flows. Specifically, a control plane architecture especially designed for provisioning TS and non-TS flows is proposed. The architecture integrates: i) a TS Flow Scheduler Planner for defining the scheduling of requested TS flows along a path so as to meet their required performance; and ii) a Network Digital Twin to estimate the performance of requested and already established non-TS flows. Differently from standardized time-aware schedulers, per-TS flow queues are assumed so as to guarantee minimal jitter. Efficient algorithms are proposed so the provisioning of flows can be carried out with high accuracy and short time. Simulation results for heterogeneous scenarios demonstrate the feasibility and efficiency of the proposed control plane architecture, as well as point out the limitations of current time-synchronization mechanisms when high-speed interfaces are considered.	10.1109/TNSM.2025.3539697
Jialin Zhang, Wei Liang, Bo Yang, Huaguang Shi, Ying-Chang Liang	Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks With Tree Topologies	2025	Early Access	Topology Network topology Resource management	Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot.	10.1109/TNSM.2025.3541245
Mohamed Elloumi, Md. Zoheb Hassan, Georges Kaddoum	Spectrum Sharing in Internet-of-Vehicles Networks: Digital Twin-Empowered Proactive Interference Management Approach	2025	Early Access	Resource management Vehicle dynamics Interchannel interference	Internet-of-Vehicles (IoV) is envisioned to connect vehicles with each other, the surrounding environment, and central control centers. Spectrum sharing among active vehicular links is imperative to enhance the utilization of the spectrum licensed to IoV networks. However, co-channel interference among neighboring vehicular communication links poses a fundamental challenge when enabling spectrum sharing in IoV networks. This paper introduces a resource optimization framework, entitled PRISM (Proactive Resource optimization for Interference and Spectrum Management), to mitigate co-channel interference in IoV networks. PRISM proactively allocates resources among a set of Vehicle-to-Infrastructure (V2I) communication links by accurately predicting the links’ positions and multi-path channel gains, thereby preventing outdated resource scheduling in dynamic IoV networks. PRISM is a three-step approach. In the first step, a multi-layer long short-term memory neural network and transfer learning are employed to predict the vehicles’ positions. In the second step, a digital twin network incorporating high-fidelity 3D maps and a ray tracing tool entitled SionnaTM is used to predict the V2I links’ multi-path channel gains. In the third step, a resource allocation algorithm is executed to efficiently determine V2I clusters and their transmit power allocations to maximize the overall system capacity. Simulation results show that PRISM enhances IoV network’s capacity up to 33% compared to non-proactive schemes, as validated through a simulation framework using real-world vehicular mobility traces.	10.1109/TNSM.2025.3541977
Sayed Taheri, Achintha Ihalage, Prateek Mishra, Sean Coaker, Faris Muhammad, Hamed Al-Raweshidy	Domain Tailored Large Language Models for Log Mask Prediction in Cellular Network Diagnostics	2025	Early Access	Software Telecommunications Industries	Software logs generated by dedicated network testing hardware are often complex and bear minimal similarity to natural language, requiring the expertise of engineers to understand and capture defects recorded in these logs. This manual process is inefficient and expensive for both service providers and their clients. In this study, we demonstrate the transformative potential of Artificial Intelligence (AI), specifically through domain-tailoring of Large Language Models (LLMs) like RoBERTa, BigBird, and Flan-T5, to streamline the process of defect diagnostics. Particularly, we pre-train these models ground up on a real industrial telecommunications log corpus, and perform finetuning on a multi-label classification objective. This facilitates identifying a correct set of log points to be enabled for rapid detection of defects that arise during network testing. Despite encountering several challenges such as intricate text structures, heavily skewed label distribution, and inconsistencies in historical data labelling, our tailored LLMs achieve commendable performance on previously unseen defect cases, significantly reducing the turnaround times. This research not only serves as an exemplar for adapting LLMs in telecommunications industry for automated defect diagnostics, but also has wide implications for software log analysis across various industries.	10.1109/TNSM.2025.3541384
Junwei Zhou, Yuyang Gao, Ying Zhu, Xiangtian Yu, Yanchao Yang, Cheng Tan, Jianwen Xiang	DRLLog: Deep Reinforcement Learning for Online Log Anomaly Detection	2025	Early Access	Anomaly detection Adaptation models Data models	System logs record the system’s status and application behavior, providing support for various system management and diagnostic tasks. However, existing methods for log anomaly detection face several challenges, including limitations in recognizing current types of anomalous logs and difficulties in performing online incremental updates to the anomaly detection models. To address these challenges, this paper introduces DRLLog, which applies Deep Reinforcement Learning (DRL) networks to detect anomalous events. DRLLog uses Deep Q Network (DQN) as the agent, with log entries serving as reward signals. By interacting with the environment generated from log data and adopting various action behaviors, it aims to maximize the reward value obtained as feedback. Through this approach, DRLLog achieves learning from historical log data and perception of the current environment, enabling continuous learning and adaptation to different log sequence patterns. Additionally, DRLLog introduces low-rank adaptation by using two low-rank parameter matrices in the fully connected layer of the DQN to represent changes in its weight matrix. During online model learning, only low-rank parameter matrices of the model are updated, effectively reducing the model’s overhead. Furthermore, DRLLog introduces focal loss to focus more on learning the features of anomalous logs, effectively addressing the issue of imbalanced quantities between normal and anomalous logs. We evaluated the performance on widely used log datasets, including HDFS, BGL and ThunderBird, showing an average improvement of 3% in F1-Score compared to baseline methods. During online model learning, DRLLog achieves an average reduction of 90% in parameter count and a significant decrease in training and testing time as well.	10.1109/TNSM.2025.3542595
Jiangang Liu, Hanjiang Luo, Hang Tao, Jiahong Liu, Jiehan Zhou	JLOS: a Cooperative UAV-Based Optical Wireless Communication With Multi-Agent Reinforcement Learning	2025	Early Access	Autonomous aerial vehicles Optical fiber communication Atmospheric modeling	In maritime Internet of Things (IoT) systems, leveraging a swarm of Unmanned Aerial Vehicles (UAVs) and optical communication can achieve a variety of potential maritime missions. However, due to the high directionality of the optical beam and interference from the marine environment, the optical link via UAVs as relays is prone to interruption. To address this challenge, we propose a Joint Link Optimization Scheme (JLOS) that includes Wind Disturbance Resistance (WDR) and Adaptive Beamwidth Adjustment (ABA). In WDR, we first model the problem as a Partially Observed Markov Decision Process (POMDP), and then design a collaborative Multi-Agent Reinforcement Learning (MARL) approach to control a swarm of UAVs in windy conditions, to maintain mechanical stability and prevent link interruption. Furthermore, in ABA, to reduce uncertainties from control activities and environmental factors like sunlight and fog, we design an adaptive algorithm using distributed MARL. It adjusts beamwidth based on historical UAV locations and link Bit Error Ratio (BER) to improve communication reliability. Numerical simulations confirm its effectiveness in enhancing robust data transmission.	10.1109/TNSM.2025.3543160
Xin Yang, Yimin Guo	IAR-AKA: An Efficient Authentication Scheme for Healthcare Tactile Internet Beyond Conventional Security	2025	Early Access	Authentication Security Medical services	With the rapid development of 5G technology, the tactile Internet is emerging as a novel form of interaction. Its application, particularly in fields such as healthcare, is extensive, with stringent requirements for real-time and accurate performance. During the transmission and storage of medical data, malicious adversaries may attempt to compromise sensitive patient information, or even disrupt the normal operation of medical devices, posing a threat to patient safety. We have found that although many existing authentication schemes claim and prove to be able to resist various known attacks, they have been found to have security vulnerabilities in subsequent research. This is because these schemes often overlook the existence of implicit attacks, which are a type of attack derived from different combinations or inferences of known attacks. In such a context, designing a lightweight authentication scheme that is secure against implicit attacks becomes crucial. This paper proposes an authentication scheme for the healthcare tactile Internet environment that goes beyond conventional security, named IAR-AKA. We conducted formal security proofs based on session key security and its corresponding implicit attacks. Additionally, we conducted non-formal security analyses based on the relationship between implicit attacks and security goals and used the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool to perform experimental simulations for IAR-AKA, demonstrating its experimentally provable security. Furthermore, detailed performance analysis results indicate that IAR-AKA not only possesses more security attributes against implicit attacks compared to similar solutions in similar contexts but also has lower communication and computation costs.	10.1109/TNSM.2025.3542796
Na Xia, Lei Chen, Meng Li, Yutao Yin, Ke Zhang	Joint Double Auction-Based Channel Selection in Wireless Monitoring Networks	2025	Early Access	Monitoring Heuristic algorithms Wireless networks	In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller’s marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios.	10.1109/TNSM.2025.3542821
Giampaolo Bovenzi, Francesco Cerasuolo, Domenico Ciuonzo, Davide Di Monda, Idio Guarino, Antonio Montieri, Valerio Persico, Antonio Pescapé	Mapping the Landscape of Generative AI in Network Monitoring and Management	2025	Early Access	Surveys Monitoring Artificial intelligence	Generative Artificial Intelligence (GenAI) models such as LLMs, GPTs, and Diffusion Models have recently gained widespread attention from both the research and the industrial communities. This survey explores their application in network monitoring and management, focusing on prominent use cases, as well as challenges and opportunities. We discuss how network traffic generation and classification, network intrusion detection, networked system log analysis, and network digital assistance can benefit from the use of GenAI models. Additionally, we provide an overview of the available GenAI models, datasets for largescale training phases, and platforms for the development of such models. Finally, we discuss research directions that potentially mitigate the roadblocks to the adoption of GenAI for network monitoring and management. Our investigation aims to map the current landscape and pave the way for future research in leveraging GenAI for network monitoring and management.	10.1109/TNSM.2025.3543022
Abdul Basit, Muddasir Rahim, Tri Nhu Do, Nadir Adam, Georges Kaddoum	DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming	2025	Early Access	Jamming Wireless networks Schedules	In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs’ and the iUE’s communication. The iUE’s learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network’s sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network’s SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming.	10.1109/TNSM.2025.3534028