TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
Saif Eddine Khelifa, Miloud Bagaa, Oussama Bekkouche, Messaoud Ahmed Ouameur, Adlen Ksentini	Extending WebAssembly for Deep-Learning Inference Across the Cloud Continuum	2025	Early Access	Webassembly Runtime Hardware	Recent advancements in serverless computing and the cloud-edge continuum have increased interest in WebAssembly (WASM). This technology enables portability and interoperability across diverse computing environments while achieving near-native execution speeds. Currently, WASM supports Single Instruction Multiple Data (SIMD), which allows for data-level parallelism that is particularly beneficial for vectorizable operations such as general matrix-matrix multiplication (GEMM) and convolutional layers. However, WASM lacks native integration with specialized hardware accelerators like GPUs, TPUs, and NPUs, as well as the ability to benefit from multi-core processing capabilities, which are critical for efficiently running Deep-Learning (DL) workloads. In contrast, despite these gains, WASM still lacks native support for heterogeneous accelerators such as GPUs, TPUs, and NPUs, as well as full multi-core parallelism capabilities that are critical for meeting the latency and throughput requirements of modern DL inference services. To bridge this gap, WASI-NN was developed, enabling WASM to integrate with external runtimes such as OpenVINO and ONNX Runtime, which leverage hardware acceleration. However, these current integrations often introduce performance overhead on certain devices, restricting their usability across the CECC. To address these challenges, we propose a new integration focusing on TVM as an external runtime for WASI-NN to enhance WASM’s performance and expand support to a broader range of devices. Additionally, we integrate this solution into Knative, a serverless framework, to provide a scalable and flexible platform for DL deployment. Using WASM technology, we evaluate our TVM-based solution through comparative studies. Results on AMD CPUs demonstrate the effectiveness of our approach, achieving 58% overall gain over other WASI-NN integrations (e.g., ONNX Runtime and OpenVINO) for CNN-based models while also achieving optimal performance on different platforms, such as Intel GPUs. These findings highlight the effectiveness of our solution.	10.1109/TNSM.2025.3606343
Meihui Liu, Fangmin Xu, Shihui Duan, Jinyu Zhu, Wenlong Ma, Ruoyu Ji, Chenglin Zhao	Efficient SRv6 Based Multi-Path Transmission Strategy for Resilient Communication in Deterministic Computing Power Network	2025	Early Access	Reliability Processor scheduling Computer architecture	The computing power network (CPN) serves as a key infrastructure for future networks, facilitating the connection of ubiquitous computing resources distributed across various locations. The continuous emergence of computation-intensive and delay-sensitive applications highlights the crucial need to fully utilize limited computing resources and the importance of building a resilient communication network. Primary-backup (PB) based transmission is a commonly used technique to enhance network reliability. However, implementing this approach in CPN with a consideration of load balancing introduces significant complexity and has received limited research attention. In this paper, we designed a deterministic computing power network (Det-CPN) architecture based on segment routing over IPv6 (SRv6). On top of the above architecture, we proposed a best computing node selection method based on a comprehensive index calculation and ranking (CICR) algorithm to determine the optimal computing node for task transmission. Subsequently, we developed a bandwidth sharing-based multi-path transmission (BSMT) algorithm to realize the maximization of the system efficiency. Simulation results demonstrate that in adverse network conditions (overloaded with a failure rate of 0.02), compared to the traditional dual-path redundant forwarding mechanism, the proposed solution achieves an average reduction of 22.3% in transmission latency, an average improvement of 39.94% in task success rate, a decrease of 19.4% in bandwidth occupation rate, and an increase of 31.05% in computing resource utilization rate.	10.1109/TNSM.2025.3608200
Rifat Al Mamun Rudro, Sultanul Arifieen Hamim, Md. Hamid Uddin, Md Masuduzzaman, Md. Manzurul Hasan	Waris-Chain: The Blockchain Driven Transformation of Inheritance Solutions	2025	Early Access	Blockchains Smart contracts Fraud	In the current digital era, managing inheritance presents a critical challenge, necessitating a balance of effectiveness, security, and transparency. Traditional processes are often complex, time-consuming, and susceptible to fraud and disputes. This paper introduces the successor chain model called Waris-Chain, a blockchain-based solution designed to streamline and secure inheritance management. Waris-Chain integrates smart contracts and Non-Fungible Tokens (NFTs) to automate and verify inheritance processes, ensuring accuracy and reducing manual intervention. Developed using the Ethereum blockchain, ERC-1155 tokens, and MetaMask for authentication, Waris-Chain offers a comprehensive, adaptable, and secure platform. Performance evaluation shows that Waris-Chain achieves a high throughput of 477.36 transactions per hour, a low transaction latency of 7.54 seconds, with a 99.42% accuracy rate and a 0.58% error rate. Despite these advancements, challenges such as blockchain adoption, legal integration, and system scalability remain, suggesting avenues for future research to fully realize blockchain’s potential in inheritance management.	10.1109/TNSM.2025.3608088
Ziwang Wang, Huili Yan, Zhize Wu	Efficient Cross-Shard Blockchain Atomic Submission Scheme Based on Pledge Transactions	2025	Early Access	Blockchains Security Synchronization	To address the substantial coordination overhead and communication latency inherent in cross-shard transaction commits within contemporary multi-shard blockchain architectures, this paper presents an efficient cross-shard atomic commit scheme (PledgeACS), grounded in the use of pledge transactions. The proposed scheme introduces a pledge transaction mechanism that employs a null recipient address and synchronizes these transactions across shards to an auxiliary chain through a global consensus protocol. Additionally, a cross-shard transaction protocol is developed, securing recipient funds via pledge transactions within the global consensus framework. Furthermore, a batch pledge transaction record and settlement protocol tailored for shard blockchains is designed, followed by rigorous feasibility analysis and performance evaluation. Experimental results indicate that the proposed scheme markedly decreases the user-perceived latency in cross-shard transactions and enhances security relative to current solutions, providing an innovative approach for achieving high throughput and scalability in blockchain systems.	10.1109/TNSM.2025.3607349
Sankalp Mittal, Praveen Tammana	Efficient In-Network Traffic Classification Using Programmable Switches With AdaFlow	2025	Early Access	Accuracy Pipelines Memory management	In-network ML-based traffic classification using programmable switches has enabled faster decisions and reduced the cost of the security infrastructure and management overheads. However, due to constraints on per-packet operations and limited stateful memory in the switch data plane, there is a fundamental tradeoff between traffic classification accuracy and switch memory requirements. Existing works fall short of accurately classifying traffic with diverse flow characteristics while keeping the memory footprint low. In this paper, we propose AdaFlow, a system that aims to address this gap by incorporating traffic-specific heuristics while designing the in-network classifier. We evaluate the AdaFlow prototype via simulations and also on a testbed with an Intel Barefoot Tofino switch. Compared to the state-of-the-art, AdaFlow improves accuracy up to 7% for various use-cases while keeping the memory overheads similar to or lower than those of the existing systems.	10.1109/TNSM.2025.3607406
Xinhan Liu, Robert Kooij, Piet Van Mieghem	Node-Reliability: Monte Carlo, Laplace, and Stochastic Approximations and a Greedy Link-Augmentation Strategy	2025	Early Access	Reliability Polynomials Computer network reliability	The node-reliability polynomial nRelG(p) measures the probability that a connected network remains connected given that each node functions independently with probability p. Computing node-reliability polynomials nRelG(p) exactly is NP-hard. Here we propose efficient approximations. First, we develop an accurate Monte Carlo simulation, which is accelerated by incorporating a Laplace approximation that captures the polynomial’s main behavior. We also introduce three degree-based stochastic approximations (Laplace, arithmetic, and geometric), which leverage the degree distribution to estimate nRelG(p) with low complexity. Beyond approximations, our framework addresses the reliability-based Global Robustness Improvement Problem (k-GRIP) by selecting exactly k links to add to a given graph so as to maximize its node reliability. A Greedy Lowest-Degree Pairing Link Addition (Greedy-LD) Algorithm, is proposed which offers a computationally efficient and practically effective heuristic, particularly suitable for large-scale networks.	10.1109/TNSM.2025.3607004
Leyla Sadighi, Stefan Karlsson, Carlos Natalino, Marija Furdek	ML-Based State of Polarization Analysis to Detect Emerging Threats to Optical Fiber Security	2025	Early Access	Optical fiber networks Optical fiber cables Optical fiber polarization	As the foundation of global communication networks, optical fibers are vulnerable to various disruptive events, including mechanical damage, such as cuts, and malicious physical layer breaches, such as eavesdropping via fiber bending. Traditional monitoring methods often fail to identify subtle or novel anomalies, stimulating the proliferation of ML techniques for detection of threats before they cause significant harm. In this paper, we evaluate the performance of SSL and USL approaches for detecting various abnormal events, such as fiber bending and vibrations, by analyzing polarization signatures with minimal reliance on labeled data. We experimentally collect thirteen polarization signatures on three different types of fiber cable and process them using OCSVM as an SSL, and DBSCAN as a USL algorithm for anomaly detection. We introduce tailored evaluation metrics designed to guide hyper-parameter tuning and capture generalization over different anomaly types, detection consistency, and robustness to false positives, enabling practical deployment of OCSVM and DBSCAN in optical fiber security. Our findings demonstrate DBSCAN as a strong contender to detect previously unseen threats in scenarios where labeled data are not available, despite some variability in performance between different scenarios, with F1 score values between 0.615 and 0.995. In contrast, OCSVM, trained on normal operating conditions, maintains high F1 scores of 0.98 to 0.998, demonstrating accurate detection of complex anomalies in optical networks.	10.1109/TNSM.2025.3607022
Chongxiang Yao, Chen Guo, Weiguang Zhang, Shengbo Chen	Efficient Optimization Algorithm for Virtual Backbone in Wireless Sensor Networks by Removing Redundant Dominators	2025	Early Access	Approximation algorithms Wireless sensor networks Optimization	Wireless sensor networks (WSNs) often utilize virtual backbones (VBs) to optimize routing and reduce energy consumption. The effectiveness of this optimization largely depends on the size of the VB, with smaller VBs offering better performance. In WSNs, VBs are typically modeled as connected dominating sets (CDSs) within unit disk graphs (UDGs). However, existing approximation algorithms for constructing the minimum connected dominating set (MCDS) often introduce redundant dominators, leading to inflated CDSs. To tackle this issue, in this paper, we propose a general CDS optimization algorithm named OP-CDS, designed specifically to minimize redundancies. Theoretical analysis shows that the size of the optimized CDS is bounded by α∙opt+δ-k+1, where α∙opt+δ represents the upper bound of the unoptimized CDS, and k denotes the number of OP-CDS iterations. Additionally, extensive simulations demonstrate that OP-CDS can effectively optimize the CDS generated by state-of-the-art algorithms with minimal time consumption.	10.1109/TNSM.2025.3606864
Ruslan Bondaruc, Nicolas Schnepf, Rémi Badonnel, Claudio A. Ardagna, Marco Anisetti	Vulnerability-Aware Secure Service Deployment in Cloud-Edge Continuum	2025	Early Access	Security Cloud computing Quality of service	Software weaknesses and vulnerabilities are continuously discovered and rapidly evolving. Their direct and indirect interference with the business process workflow execution is neither fully understood nor addressed by the current literature. The strict control of the vulnerability footprint of the landing platform before cloud/web service workflow execution is nowadays largely used as a prevention measure in order to improve execution trustworthiness. The vulnerability footprint governance is exacerbated by the cloud, where a common execution platform hosting (vulnerable) services is shared between different tenants. The paper proposes a service workflow deployment solution tailored for Edge-Cloud Continuum, made of different landing platforms showing different peculiarities. The proposed solution is capable of finding a suitable deployment recipe for a given workflow by i) evaluating the vulnerability footprint of each platform, ii) computing the set of candidate deployment platforms, iii) finding the optimal deployment solution, and iv) migrating already deployed workflows in case the vulnerability requirement is no longer satisfied. Each workflow can be associated with a set of requirements to be satisfied by our deployment solution, like the maximum level of vulnerability footprint accepted. Each workflow deployment contributes to the vulnerability footprint of the landing platform involved.	10.1109/TNSM.2025.3606624
Kang Liu, Yaru Fu, Guangping Xu, Wenguang Zheng, Mingyuan Ding, Yulei Wu, Tony Q.S. Quek	Backhaul Traffic-Aware Edge Caching for Recommended Content With Personalized Privacy	2025	Early Access	Privacy Noise Backhaul networks	Caching recommended contents at the network edge can effectively alleviate the traffic pressure of the backbone network and significantly improve user experience. However, highly personalized and precise recommendations often rely on leveraging more user request records, raising serious privacy concerns. Existing recommendation-aware edge caching mechanisms typically apply a fixed level of privacy protection, without considering the personalized privacy of users. This one-size-fits-all approach often introduces significant noise, adversely impacting cache hit ratio (CHR). In this work, we propose a differential privacy-based edge caching framework supporting personalized privacy-preserving to address these challenges. We formulate a CHR maximization problem under personalized privacy constraints and reveal the NP-completeness of the problem with a rigorous mathematical proof. Subsequently, we mathematically model the relationship between personalized privacy and user preference distortion, analyzing its impact on recommendations and user requests. To solve it, we introduce an efficient heuristic algorithm named the Backhaul Traffic-Aware Caching Algorithm. This algorithm utilizes backhaul traffic as a feedback signal to make accurate caching decisions, enabling adaptive optimization of caching decisions by perceiving the impact of noise and low-quality recommendations. Extensive experiments on two typical real-world datasets validate the effectiveness of our framework, demonstrating its ability to enhance privacy protection while simultaneously improving CHR.	10.1109/TNSM.2025.3606544
Livia Elena Chatzieleftheriou, Jesús Pérez-Valero, Jorge Martín-Pérez, Pablo Serrano	Optimal Scaling and Offloading for Sustainable Provision of Reliable V2N Services in Dynamic and Static Scenarios	2025	Early Access	Ultra reliable low latency communication Delays Servers	The rising popularity of Vehicle-to-Network (V2N) applications is driven by the Ultra-Reliable Low-Latency Communications (URLLC) service offered by 5G. Distributed resources can help manage heavy traffic from these applications, but complicate traffic routing under URLLCfs strict delay requirements. In this paper, we introduce the V2N Computation Offloading and CPU Activation (V2N-COCA) problem, aiming at the monetary/energetic cost minimization via computation offloading and edge/cloud CPU activation decisions, under stringent latency constraints. Some challenges are the proven nonmonotonicity of the objective function and the no-existence of closed-formulas for the sojourn time of tasks. We present a provably tight approximation for the latter, and we design BiQui, a provably asymptotically optimal and computationally efficient algorithm for the V2N-COCA problem. We then study dynamic scenarios, introducing the Swap-Prevention problem, to account for changes in the traffic load and minimize the switching on/off of CPUs without incurring into overcosts.We prove the problemfs structural properties and exploit them to design Min-Swap, a provably correct and computationally effective algorithm for the Swap-Prevention Problem. We assess both BiQui and Min-Swap over real-world vehicular traffic traces, performing a sensitivity analysis and a stress-test. Results show that (i) BiQui is nearoptimal and significantly outperforms existing solutions; and (ii) Min-Swap reduces by a ≥90% the CPU swapping incurring into just ≤0.14% extra cost.	10.1109/TNSM.2025.3605408
Wei-Kun Chen, Ya-Feng Liu, Yu-Hong Dai, Zhi-Quan Luo	QoS-Aware and Routing-Flexible Network Slicing for Service-Oriented Networks	2025	Early Access	Reliability Delays Routing	In this paper, we consider the network slicing () problem which aims to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and manage network resources to meet diverse quality of service (QoS) requirements. We propose a mixed-integer nonlinear programming (MINLP) formulation for the considered NS problem that can flexibly route the traffic flow of the services on multiple paths and provide end-to-end delay and reliability guarantees for all services. To overcome the computational difficulty due to the intrinsic nonlinearity in the MINLP formulation, we transform the formulation into an equivalent mixed-integer linear programming () formulation and further show that their continuous relaxations are equivalent. In sharp contrast to the continuous relaxation of the formulation which is a nonconvex nonlinear programming problem, the continuous relaxation of the formulation is a polynomial-time solvable linear programming problem, which significantly facilitates the algorithmic design. Based on the newly proposed formulation, we develop a customized column generation () algorithm for solving the problem. The proposed algorithm is a decomposition-based algorithm and is particularly suitable for solving large-scale problems. Numerical results demonstrate the efficacy of the proposed formulations and the proposed algorithm.	10.1109/TNSM.2025.3608074
Kai Cheng, Weidong Tang, Lintao Tan, Jing Yang, Jia Chen	SLNALog: A log Anomaly Detection Scheme Based on Swift Layer Normalization Attention Mechanism for Next-Generation Power Communication Networks	2025	Early Access	Anomaly detection Semantics Smart grids	Log anomaly detection is a critical first line of defense for securing next-generation power communication networks against malicious attacks.serves as the initial line of defense for safeguarding the security of the next-generation power communication networks, which can protect it from attackers invasion damage. However, in industrial settingsin the industrial Internet domain, limitedthe scarcity of computational resources on edge devices result in longin devices leads to prolonged inference times for anomaly detection models, hindering the timely detection of anomalous log activities.impeding the prompt identification of unusual activities logged within these devices. To address these challenges, we propose SLNALog, an anomaly detection workflow centered around a Swift Layer Normalization Attention module.the aforementioned issues, the SLNALog anomaly detection workflow has been proposed. Its core comprises a Swift Layer Normalization Attention module. This module leveragesis based on linear attention to optimizeand optimizes the key-value interactions found in traditional attention mechanisms, thereby reducing the computational complexity of the detection process. This optimization reduces the complexity of log anomaly detection. As a result, the model’s receptive field for log data is expanded, and the efficiency of log anomaly detection is improved. ExperimentalThe experimental results on the HDFS and BGL datasets demonstrate the superiority of our approach.indicate that it achieves higher accuracy. SLNALog achieves higher accuracy, with F1-scores increasing by 0.08 and 0.04, respectively, while reducing detection time by 5.7% and 28.3%. The model provides an effective solution to enhance the cyber security of smart grids. Furthermore, the workflow incorporates an LLM-based log template analysis module and an Adapter-based model tuning module to enhance the model’s generalization in real-world scenarios. The proposed model provides an effective solution for enhancing the cybersecurity of smart grids.	10.1109/TNSM.2025.3605764
Runze Wu, Kai Wang, Haobo Guo, Yuxin Liu, Wenting Wang, Jing Liu	Joint Optimization Algorithm for Multi-Dimensional Wireless Communication Resources Adapt to Deterministic Monitoring of Power Distribution Areas	2025	Early Access	Ultra reliable low latency communication Resource management Wireless communication	With a growing penetration of distributed renewable energy, it is becoming increasingly evident that the monitoring service of power distribution area is characterized by multiple concurrency and time delay sensitivity. Correspondingly, massive amounts of differentiated data need to be collected, which dramatically increases the demand for technologies like ultra-reliable low-latency communication (URLLC) and enhanced mobile bandwidth (eMBB). However, due to limited wireless communication resource in power distribution areas, the major difficulty of improving monitoring certainty under the delay perspective lies in efficient and reasonable joint allocation of the multi-dimensional resources. To this end, a joint optimization algorithm for multi-dimensional wireless communication resources adapt to deterministic monitoring of power distribution areas is proposed. Firstly, the joint optimization problem for spectrum resource blocks (RBs) and power scheduling is constructed, and then it is decoupled in layers consisted of eMBB-RB scheduling layer, URLLC-RB scheduling layer and URLLC-power allocation layer and solved by the greedy strategy, improved plant growth simulation algorithm and rigorous theory analysis combined with block coordinate descent. The simulation results validate that the proposed algorithm swiftly and rationally allocates multi-dimensional wireless communication resources to massive and concurrent services, which satisfies the diverse QoS demands and improves the monitoring certainty of the power distribution areas.	10.1109/TNSM.2025.3605601
Bo Mi, Hangcheng Zou, Darong Huang	FedPP: Privacy-Enhanced Federated Learning for Parameter Aggregation in Heterogeneous Intelligent Connected Vehicles	2025	Early Access	Federated learning Training Data models	With the popularization of intelligent connected vehicles (ICVs), traffic information sources are becoming ubiquitous and diverse. Given the inherent conflict between data value extraction and privacy protection, federated learning (FL) has emerged as a powerful tool for developing application models with certain generalization capability. Although FL ensures that data remains local, the parameters used for aggregation are still vulnerable to attacks, such as reverse engineering or membership inference. Methods based on homomorphic encryption or differential privacy can alleviate this issue to some extent; however, they also lead to a reduction in training performance. Furthermore, since the data collected by ICVs generally exhibit non-independent and identically distributed (non-IID) characteristics, ensuring model reliability becomes quite challenging. This paper presents a private-parameter-based federated learning method, FedPP, which integrates a Gaussian mechanism with multi-key homomorphic encryption to prevent parameter leakage while eliminating noise disturbance. By sorting and selecting the parameters to be aggregated, this approach not only demonstrates improved generalization capability under heterogeneous conditions but also effectively resists poisoning attacks. To evaluate the model, we constructed two non-IID traffic datasets using the Dirichlet distribution, which comprises a traffic sign dataset and a vehicle image dataset generated through the DALL-E model. Theoretical analysis and experimental results demonstrate that FedPP not only meets provable security under collaborative attacks but also exhibits higher model accuracy in heterogeneous vehicular network environments.	10.1109/TNSM.2025.3605336
Lu Cao, Lin Yao, Weizhe Zhang, Yao Wang	HeavyFinder: A Lightweight Network Measurement Framework for Detecting High-Frequency Elements in Skewed Data Streams	2025	Early Access	Accuracy Memory management Resource management	Skewed data streams are characterized by uneven distributions in which a small fraction of elements occur with much higher frequency than others. The detection of these high-frequency elements presents significant practical challenges, particularly under stringent memory constraints, as existing detection techniques have typically relied on predefined thresholds that require significant memory usage. However, this approach is highly inefficient since not all elements require equal storage space. To address these limitations, we introduce HeavyFinder (HF), a novel lightweight network measurement architecture designed to detect high-frequency elements in skewed data. HF employs a threshold-free update strategy that enables dynamic adaptation to variable data, thereby providing greater flexibility for tracking high-frequency elements without requiring fixed thresholds. Furthermore, an included memory-light strategy enables high accuracy for non-uniform distributions, even with limited memory allocation. Experimental results showed that HF significantly improved performance in four query tasks, producing an accuracy of 99.81% when identifying the top-k elements. The average absolute error (AAE) was also reduced to 10-4 using only 100KB of memory, which was significantly lower than that of conventional methods.	10.1109/TNSM.2025.3604523
Frkei Saleh, Abraham O. Fapojuwo, Diwakar Krishnamurthy	eSlice: Elastic Inter-Slice Resource Allocation for Smart City Applications	2025	Early Access	Resource management Smart cities 5G mobile communication	Network slicing is a fundamental enabler for the advancement of fifth generation (5G) and beyond 5G (B5G) networks, offering customized service-level agreements (SLAs) for distinct slices such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communication (URLLC). However, smart city applications often require multiple slices concurrently, posing significant challenges in resource allocation, service isolation, and maintaining performance guarantees. This paper presents eSlice, an elastic inter-slice resource allocation mechanism specifically designed to address the dynamic requirements of smart city applications. eSlice organizes applications into hierarchical slices, leveraging cloud-native resource scaling to dynamically adapt to real-time demands. It integrates two novel algorithms: the Proactive eSlice Allocation Algorithm (PeSAA), which ensures the fair distribution of resources across the substrate network, and the Reactive eSlice Allocation Algorithm (ReSAA), which employs Multi-Agent Reinforcement Learning (MARL) to dynamically coordinate, reallocate, and recover unused resources as network conditions evolve. Experimental results demonstrate that eSlice significantly outperforms existing methods, achieving 94.3% resource utilization in simulation-based experiments under constrained urban-scale scenarios, providing a robust solution for dynamic resource management in 5G-enabled smart city networks.	10.1109/TNSM.2025.3604352
Cheng Long, Haoming Zhang, Zixiao Wang, Yiming Zheng, Zonghui Li	FastScheduler: Polynomial-Time Scheduling for Time-Triggered Flows in TSN	2025	Early Access	Job shop scheduling Network topology Dynamic scheduling	Time-Sensitive Networking (TSN) has emerged as a promising network paradigm for time-critical applications, such as industrial control, where flow scheduling is crucial to ensure low latency and determinism. As production flexibility demands increase, network topology and flow requirements may change, necessitating more efficient TSN scheduling algorithms to guarantee real-time and deterministic data transmission. In this work, we present FastScheduler, a polynomial-time, deterministic TSN scheduler, which can schedule thousands of Time-Triggered (TT) flows within arbitrary network topologies. The key innovations of FastScheduler include an Equivalent Reduction Technique to simplify the generic model while preserving the feasible scheduling space, a Deterministic Heuristic Strategy to ensure a consistent and reproducible scheduling process, and a Polynomial-Time Scheduling Algorithm to perform dynamic and real-time scheduling of periodic TT flows. Extensive experiments on various topologies show that FastScheduler can effectively simplify the model, reducing variables/constraints by 35%/62%, and schedule 1,000 TT flows in subsecond time. Furthermore, it runs 2/3 orders of magnitude faster and improves the schedulability by 12%/20% compared to heuristic/deep reinforcement learning-based methods. FastScheduler is well-suited for the dynamic requirements of industrial control networks.	10.1109/TNSM.2025.3603844
Wenjun Fan, Na Fan, Junhui Zhang, Jia Liu, Yifan Dai	Securing VNDN With Multi-Indicator Intrusion Detection Approach Against the IFA Threat	2025	Early Access	Monitoring Prevention and mitigation Electronic mail	On vehicular named data network (VNDN), Interest Flooding Attack (IFA) can exhaust the computing resources by sending a large number of malicious Interest packets, which leads to the failure of satisfying the legitimate requests and seriously hazards the operation of Internet of Vehicles (IoV). To solve this problem, this paper proposes a distributed network traffic monitoring-enabled multi-indicator detection and prevention approach for VNDN to detect and resist the IFA attacks. In order for facilitating this approach, a distributed network traffic monitoring layer based on road side unit (RSU) is constructed. With such a monitoring layer, a multi-indicator detection approach is designed, which consists of three indicators: information entropy, self-similarity, and singularity, whereby the thresholds are tweaked by the real-time density of traffic flow. Apart from the detection, a blacklisting based prevention approach is realized to mitigate the attack impact.We validate the proposed approach via prototyping it on our VNDN experimental platform using realistic parameters setting and leveraging the original NDN packet structure to corroborate the usage of the required Source ID for identifying the source of the Interest packet, which consolidates the practicability of the approach. The experimental results show that our multi-indicator detection approach has a greatly higher detection performance than those of using indicators individually, and the blacklisting-based prevention can effectively mitigate the attack impact as well.	10.1109/TNSM.2025.3603630
Huaide Liu, Fanqin Zhou, Yikun Zhao, Lei Feng, Zhixiang Yang, Yijing Lin, Wenjing Li	Autonomous Deployment of Aerial Base Station without Network-Side Assistance in Emergency Scenarios Based on Multi-Agent Deep Reinforcement Learning	2025	Early Access	Heuristic algorithms Disasters Optimization	Aerial base station (AeBS) is a promising technology for providing wireless coverage to ground user equipment. Traditional methods of optimizing AeBS networks often rely on pre-known distribution models of ground user equipment. However, in practical scenarios such as natural disasters or temporary large-scale public events, the distribution of user clusters is often unknown, posing challenges for the deployment and application of AeBS. To adapt to complex and unknown user environments, this paper studies a method of estimating information from local to global and proposes a multi-agent AeBSs autonomous deployment algorithm based on deep reinforcement learning (DRL). This method attempts to dynamically deploy AeBS to autonomously identify hotspots by sensing user equipment signals without network-side assistance, providing a more comprehensive and intelligent solution for AeBS deployment. Simulation results indicate that our method effectively guides the autonomous deployment of AeBS in emergency scenarios, addressing the challenge of the lack of network-side assistance.	10.1109/TNSM.2025.3603875