TNSM Searchable Index

Author(s)	Title	Year	Publication	Keywords
José Santos, Bibin V. Ninan, Bruno Volckaert, Filip De Turck, Mays Al-Naday	A Comprehensive Benchmark of Flannel CNI in SDN/non-SDN Enabled Cloud-Native Environments	2025	Early Access	Containers Benchmark testing IP networks	The emergence of cloud computing has driven advancements in software virtualization, particularly microservice containerization. This in turn led to the development of Container Network Interfaces (CNIs) such as Flannel to connect microservices over a network. Despite their objective to provide connectivity, CNIs have not been adequately benchmarked when containers are connected over an external network. This creates uncertainty about the operation reliability of CNIs in distributed edge-cloud ecosystems. Given the multitude of available CNIs and the complexity of comparing different ones, this paper focuses on the widely adopted CNI, Flannel. It proposes the design of novel benchmarks of Flannel across external networks, Software Defined Networking (SDN)-based and non-SDN, characterizing two of the key backend types of Flannel: User Datagram Protocol (UDP) and Virtual Extensible LAN (VXLAN). Unlike existing benchmarks, this study analysis the overhead introduced by the external network and the impact of network disruptions. The paper outlines the systematic approach to benchmarking a set of Key Performance Indicators (KPIs), including: speed, latency and throughput. A variety of network disruptions have been induced to analyse their impact on these KPIs, including: delay, packet loss, and packet corruption. The results show that VXLAN consistently outperforms UDP, offering superior bandwidth with efficient resource consumption, making it more suitable for production environments. In contrast, the UDP backend is suitable for real-time video streaming applications due to its higher data rate and lower jitter, though it requires higher resource utilization. Moreover, the results show less variation in KPIs over SDN, compared to non-SDN. The benchmark data are made publicly available in an open-source repository, enabling researchers to replicate the experiments, and potentially extend the study to other CNIs. This work contributes to the network management domain by providing an extensive benchmark study on container networking highlighting the main advantages and disadvantages of current technologies.	10.1109/TNSM.2025.3602607
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
Junyu Li, Fei Zhou, Qi Xie, Nankun Mu, Yining Liu	Efficient Conditional Privacy-Preserving Heterogeneous Broadcast Signcryption for Collision Warning in VANETs	2025	Early Access	Security Privacy Encryption	Real-time performance is of utmost significance for communication in certain specific scenarios of vehicle-to-infrastructure (V2I) like collision warning systems. Vehicle-to-Everything (V2X) Broadcast signcryption is very suitable for these scenarios. However, current solutions prioritize generality, but may not be suitable for specialized communication situations, and many broadcast signcryption schemes suffer from low communication verification efficiency due to the sequence of decryption before verification. Moreover, most of existing broadcast signcryption schemes with single cryptosystem are not applicable for the heterogeneous networks of different Internet of Vehicles. To address these challenges, an efficient conditional privacy-preserving heterogeneous broadcast signcryption scheme(ECPHBS) is proposed, improving roadside unit verification to support batch verification of ciphertexts through a pre-authentication mechanism, and allowing vehicles to conduct secure communication with roadside units under the certificateless cryptosystem and the identity-based cryptosystem. Meanwhile, a tracking and revocation mechanism was introduced to achieve conditional privacy protection. Our formal security analysis demonstrates that the ECPHBS scheme formally achieves IND-CCA2 security under the CDH assumption and EUF-CMA security under the ECDL problem. Experimental results confirm its superior verification efficiency, especially with an increasing number of receiving RSUs, and a constant communication overhead. Furthermore, the RSU service capability analysis shows that our scheme enables RSUs to fully handle communication requests from approximately 500 vehicles within a 150-meter range, outperforming comparative schemes.	10.1109/TNSM.2025.3619109
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
Xiaoming He, Zijing He, Wenyun Li, Gang Liu, Xuan Wei	Framework for Real-Time Monitoring of Packet Loss Caused by Network Congestion	2025	Early Access	Packet loss Monitoring Real-time systems	Network congestion induces performance degradation and increases the uncertainty of service delivery, so it is essential to monitor it in real time. In this paper, we discuss the requirements of real-time monitoring of packet loss caused by congestion, present the problems and challenges faced by existing measurement techniques in monitoring congestion induced packet loss, and propose a comprehensive packet loss monitoring framework. The proposed framework is described in detail and its realizability is demonstrated. The proposed scheme is capable to not only determine the time and location of packet loss occurrence, make the accurate statistics of discarded packets, parse what traffic flows are contained in discarded packets and identify what traffic flows lead to microburst, but also obtain accurate packet loss ratio results with zero error. More importantly, our proposed scheme can achieve little or even no interference to network, and is applicable to any data plane without modifying the forwarding chip and packet header as existing measurement methods do. Experimental results have verified the effectiveness of our proposed scheme. Furthermore, we present three typical application scenarios to demonstrate the advantages of the proposed framework	10.1109/TNSM.2025.3578056
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
Indukuri Mani Varma, Neetesh Kumar	Blockchain-Based SDN-Enabled Lightweight Authentication Protocol for IoV Using zk-SNARK	2025	Early Access	Authentication Protocols Handover	Software-defined networking (SDN), an emerging networking paradigm, has been realized in the internet of vehicles to effectively manage the dynamic nature of vehicles and provide diverse vehicle-to-everything (V2X) services and security. Nevertheless, the lack of authentication mechanisms and malicious behavior of vehicle users has seriously threatened security and privacy concerns in the SDN-based vehicular network (SDVN). Furthermore, in a distributed SDVN, the SDN controllers (SDNCs) are prone to single point of failure (SPoF) due to various potential attack vectors. Such attacks can result in the loss of periodically exchanged vehicular information among SDNCs. To address the aforementioned issues, a novel privacy-preserving lightweight zk-SNARK-based authentication protocol in blockchain-enabled distributed SDVN is presented. To address the SPoF of SDNCs, the proposed network model removes the dependency of a controller on neighboring SDNCs as the authenticated information is validated and stored in the blockchain. As an integral part of the protocol, the vehicles are authenticated by providing succinct and constant size proofs during authentication across SDVN domains. The security and simulation performance analyses confirm the superior performance of the proposed protocol, surpassing state-of-the-art schemes in terms of authentication latency, computation time, and energy efficiency. The proposed protocol has achieved notable improvements of more than 55% in all the metrics considered during the protocol simulation when compared with other protocols.	10.1109/TNSM.2025.3613415
Menna Helmy, Alaa Awad Abdellatif, Naram Mhaisen, Amr Mohamed, Aiman Erbad	Slicing for AI: An Online Learning Framework for Network Slicing Supporting AI Services	2025	Early Access	Artificial intelligence Training Resource management	The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of Service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users’ behavior and mobile networks. Thus, this paper proposes an online learning framework to determine the allocation of computational and communication resources to AI services, to optimize their accuracy as one of their unique key performance indicators (KPIs), while abiding by resources, learning latency, and cost constraints. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity. Additionally, our solution outperforms State-of-the-Art techniques in adapting to diverse environmental dynamics and excels under varying levels of resource availability.	10.1109/TNSM.2025.3603391
Erhe Yang, Zhiwen Yu, Yao Zhang, Helei Cui, Zhaoxiang Huang, Hui Wang, Jiaju Ren, Bin Guo	Joint Semantic Extraction and Resource Optimization in Communication-Efficient UAV Crowd Sensing	2025	Early Access	Sensors Autonomous aerial vehicles Optimization	With the integration of IoT and 5G technologies, UAV crowd sensing has emerged as a promising solution to overcome the limitations of traditional Mobile Crowd Sensing (MCS) in terms of sensing coverage. As a result, UAV crowd sensing has been widely adopted across various domains. However, existing UAV crowd sensing methods often overlook the semantic information within sensing data, leading to low transmission efficiency. To address the challenges of semantic extraction and transmission optimization in UAV crowd sensing, this paper decomposes the problem into two sub-problems: semantic feature extraction and task-oriented sensing data transmission optimization. To tackle the semantic feature extraction problem, we propose a semantic communication module based on Multi-Scale Dilated Fusion Attention (MDFA), which aims to balance data compression, classification accuracy, and feature reconstruction under noisy channel conditions. For transmission optimization, we develop a reinforcement learning-based joint optimization strategy that effectively manages UAV mobility, bandwidth allocation, and semantic compression, thereby enhancing transmission efficiency and task performance. Extensive experiments conducted on real-world datasets and simulated environments demonstrate the effectiveness of the proposed method, showing significant improvements in communication efficiency and sensing performance under various conditions.	10.1109/TNSM.2025.3603194
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
Dániel Unyi, Ernő Rigó, Bálint Gyires-Tóth, Róbert Lovas	Explainable GNN-Based Approach to Fault Forecasting in Cloud Service Debugging	2025	Early Access	Debugging Microservice architectures Cloud computing	Debugging cloud services is increasingly challenging due to their distributed, dynamic, and scalable nature. Traditional methods struggle to handle large state spaces and the complex interactions between microservices, making it difficult to diagnose failures and identify critical components. This paper presents a Graph Neural Network (GNN)-based approach that enhances cloud service debugging by predicting system-level fault probabilities and providing interpretable insights into failure propagation. Our method models microservice interactions as graphs, where failures propagate probabilistically. Using Markov Decision Processes (MDPs), we simulate failure behaviors, capturing the probabilistic dependencies that influence system reliability. The trained GNN not only predicts fault probabilities but also identifies the most failure-prone microservices and explains their impact. We evaluate our approach on various service mesh structures, including feature-enriched, tree-structured, and general directed acyclic graph (DAG) architectures. Results indicate that our method is effective in the operational phase of cloud services, enabling proactive debugging and targeted optimization. This work represents a step toward more interpretable, reliable, and maintainable cloud infrastructures.	10.1109/TNSM.2025.3602223
Ahan Kak, Van-Quan Pham, Huu-Trung Thieu, Nakjung Choi	HexRAN: A Programmable Approach to Open RAN Base Station System Design	2025	Early Access	Open RAN Base stations Protocols	In recent years, the radio access network (RAN) domain has seen significant changes with increased virtualization and softwarization, driven by the Open RAN (O-RAN) movement. However, the fundamental building block of the cellular network, i.e., the base station, remains unchanged and ill-equipped to handle this architectural evolution. In particular, there exists a general lack of programmability and composability along with a protocol stack that grapples with the intricacies of the 3GPP and O-RAN specifications. Recognizing the need for an “O-RAN-native” approach to base station design, this paper introduces HexRAN– a novel base station architecture characterized by key features relating to RAN disaggregation and composability, 3GPP and O-RAN protocol integration and programmability, robust controller interactions, and customizable RAN slicing. Furthermore, the paper also includes a concrete systems-level prototype and comprehensive experimental evaluation of HexRAN on an over-the-air testbed. The results demonstrate that HexRAN uses only 8% more computing resources compared to the baseline, while managing twice the user plane traffic, delivering control plane processing latency of under 120μs, and achieving 100% processing reliability. This underscores the scalability and performance advantages of the proposed architecture.	10.1109/TNSM.2025.3600587
Mengyao Li, Noah Ploch, Sebastian Troia, Carlo Spatocco, Wolfgang Kellerer, Guido Maier	On the Optimization of Model Aggregation for Federated Learning at the Network Edge	2025	Early Access	Computational modeling Training Wide area networks	The rapid increase in connected devices has significantly intensified the computational and communication demands on modern telecommunication networks. To address these challenges, integrating advanced Machine Learning (ML) techniques like Federated Learning (FL) with emerging paradigms such as Multi-access Edge Computing (MEC) and Software-Defined Wide Area Networks (SD-WANs) is crucial. This paper introduces online resource management strategies specifically designed for FL model aggregation, utilizing intermediate aggregation at edge nodes. Our analysis highlights the benefits of incorporating edge aggregators to reduce network link congestion and maximize the potential of edge computing nodes. However, the risk of network congestion persists. To mitigate this, we propose a novel aggregation approach that deploys an aggregator overlay network. We present an Integer Linear Programming (ILP) model and a heuristic algorithm to optimize the routing within this overlay network. Our solution demonstrates improved adaptability to network resource utilization, significantly reducing FL training round failure rates by up to 15% while also alleviating cloud link congestion.	10.1109/TNSM.2025.3613772
Maruthi V, Kunwar Singh	Enhancing Security and Privacy of IoMT Data for Unconscious Patient With Blockchain	2025	Early Access	Cryptography Security Polynomials	IoMT enables continuous monitoring through connected medical devices, producing real-time health data that must be protected from unauthorised access and tampering. Blockchain ensures this security with its decentralised, tamper-resistant, and access-controlled ledger. A critical challenge arises when patients are unconscious, making timely access to their IoMT data essential for emergency treatment. To address this, we have created and designed a novel Threshold Proxy Re-Encryption+ (TPRE+) framework that integrates threshold cryptography with unidirectional, non-transitive proxy re-encryption(PRE) with Shamir’s secret sharing to distribute re-encryption capabilities among multiple proxies, reducing single-point failure and collision risks. Our contributions are threefold: (i) We first proposed a semantically secure TPRE+ scheme with Shamir-secret sharing, (ii) Construction of an IND-CCA secure TPRE+ scheme, and (iii) Development of a secure, distributed medical record storage system for unconscious patients, combining blockchain infrastructure, IPFS-based encrypted storage, and our proposed TPRE+ schemes. This integration ensures confidentiality, integrity, and fault-tolerant access to critical patient data, enabling secure and efficient deployment in real-world emergency healthcare scenarios.	10.1109/TNSM.2025.3602117
Yadi He, Zhou Wu, Linfeng Liu	Deep Learning Based Link Prediction Method Against Strong Sparsity for Mobile Social Networks	2025	Early Access	Feature extraction Social networking (online) Predictive models	Link prediction refers to the prediction of the potential relationships between nodes through exploring the evolution of the historical network topologies. In mobile social networks, the topologies change frequently due to the appearance/disappearance of nodes over time, and the links between nodes are typically very sparse (i.e., mobile social networks are with strong sparsity), which could affect the accuracy of link prediction in mobile social networks seriously. Therefore, this paper proposes a deep learning based Link Prediction Method against Strong Sparsity (LPMSS). LPMSS integrates the graph convolutional network output with encounter matrices to mitigate the negative impact of strong sparsity. Additionally, LPMSS employs the random negative sampling to alleviate the impact of imbalanced link distributions. We also adopt a Times module to capture the temporal topological changes in mobile social networks to enhance the prediction accuracy. Based on three datasets with different sparsity, extensive experiment results demonstrate that LPMSS can significantly improve AUC values while reducing MAE values, confirming its effectiveness in handling the link prediction in the mobile social networks with strong sparsity.	10.1109/TNSM.2025.3601389
Zhi-Bin Zuo, De-Min Wang, Mi-Mi Ma, Miao-Lei Deng, Chun Wang	An Adaptive Contention Window Backoff Scheme Differentiating Network Conditions Based on Deep Q-Learning Network	2025	Early Access	Throughput Data communication Wireless sensor networks	In IEEE 802.11 networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. In order to mitigate the performance degradation caused by the increasing number of stations in the network, we propose a novel adaptive CW backoff scheme, termed the ACWB-DQN algorithm. This algorithm leverages the Deep Q-Leaning Network (DQN) to explore a CW threshold, which is utilized as a boundary to differentiate the network load circumstances and learn the best configurations for different network conditions. When stations transmit data frames, different CW optimization strategies are employed based on station transmission status and the CW threshold. This approach aims to enhance network performance by adjusting the CW to increase transmission efficiency when there are fewer competing stations, and lower collision probabilities when there are more competing stations. Simulation results indicate that this approach can optimize station CW, reduce network collision rates, maintain constant throughput and significantly enhance the performance of Wi-Fi networks by means of adjusting the CW threshold according to real-time network conditions.	10.1109/TNSM.2025.3600861
Mingyang Yu, Haorui Yang, Shengwei Fu, Desheng Kong, Xiaoxuan Xu, Jun Zhang, Jing Xu	Improved Coverage and Redundancy Management in WSN Using ENMDBO: An Enhanced Metaheuristic Solution	2025	Early Access	Optimization Wireless sensor networks Heuristic algorithms	The widespread deployment of Wireless Sensor Networks (WSN) has made network coverage optimization crucial for improving coverage rates. However, traditional methods struggle with challenges such as energy constraints and environmental uncertainties. Metaheuristic (MH) algorithms offer promising solutions. Dung Beetle Optimization (DBO) algorithm is a well-regarded MH approach, but it suffers from slow convergence and a propensity for local optima entrapment in WSN coverage optimization. To overcome these limitations, this study proposes the Enhanced Dung Beetle Optimization with Neighborhood Mutation (ENMDBO). ENMDBO incorporates three key mechanisms: (1) the Exploring Cosine Similarity Transformation (ECST) strategy, which dynamically adjusts individual similarity to balance global exploration and local exploitation, mitigating the risk of local optima; (2) the Neighborhood Solution Mutation Sharing (NSMS) mechanism, which enhances population diversity by sharing positional information among neighbors, improving search efficiency; and (3) the Tolerance Threshold Detection Mutation (TTDM) mechanism, which detects stagnation in fitness to strengthen the algorithm’s global search capabilities. Experiments on the CEC2017 benchmark suite (Dim = 30, 50, 100) show that ENMDBO achieves superior performance compared to state-of-the-art algorithms, approaching the global optimum. Finally, in WSN coverage optimization, ENMDBO achieves an 86.88% coverage rate, representing an 8.92% improvement over the original DBO, while effectively reducing redundancy. These results underscore ENMDBO’s robustness and effectiveness, establishing it as a practical and reliable solution. (Matlab codes of ENMDBO are available at https://ww2.mathworks.cn/matlabcentral/fileexchange/181820-enhanced-dung-beetle-optimization-with-neighborhood-mutation.	10.1109/TNSM.2025.3600631
Peng Qin, Yang Fu, Zhigang Yu, Jing Zhang, Zhou Lu	Cross-Domain Resource Allocation for Information Retrieving Delay Minimization in UAV-Empowered 6G Network	2025	Early Access	Sensors Autonomous aerial vehicles Resource management	The deep integration of sensing, communication, caching, and computation (SC3) is emerging as a key feature of 6G network, designed to support ubiquitous intelligent applications and enhance human quality of life. Simultaneously, unmanned aerial vehicles (UAVs) have been identified as promising edge nodes to bolster terrestrial wireless networks. To harness the coordination benefits of SC3 and address potential conflicts, we propose a UAV-empowered joint SC3 6G network, in which UAVs are outfitted with edge servers to cache and process sensing data from integrated sensing and communication devices before delivering the results to users. To maintain the freshness of sensing data in such network, we formulate an average information retrieving delay minimization problem, coordinating cross-domain resources while considering performance constraints in sensing, communication, and energy. We then develop a cross-domain resource optimization algorithm to jointly design UAV 3D deployment, subcarrier assignment, power control, caching update, and computational resource allocation. This approach combines block coordinate descent, matching theory, and successive convex approximation to iteratively solve the optimization problem. Experimental evaluations demonstrate that the proposed scheme converges rapidly and outperforms benchmark methods in reducing average information retrieving delay through the coordination of SC3 cross-domain resources.	10.1109/TNSM.2025.3600768