Ad Hoc Networks最新文献_第2页

System energy efficiency maximization-oriented clustering protocol design for active IRS-aided EH-CRSNs 面向系统能效最大化的主动irs辅助EH-CRSNs聚类协议设计

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-15 DOI: 10.1016/j.adhoc.2026.104148

Jihong Wang, Yongxin Fan, Yanan Zhu, Miao Yu, Yang Li

In clustered energy harvesting-cognitive radio sensor networks (EH-CRSNs), reliance on direct links for both EH and data transmission causes distant nodes to deplete energy faster, thereby shortening network lifetime. To address the above issues, this paper integrates an active intelligent reflecting surface (IRS) into EH-CRSNs and proposes a system energy efficiency (EE) maximization-oriented clustering protocol (EEMCP) to achieve a trade-off between network lifetime and monitoring capability. Specifically, by optimizing the reflection coefficient matrix of the active IRS during uplink transmission, the transmission range of cluster heads (CHs) is extended, enabling direct communication with the sink and mitigating data delivery failures caused by the absence of suitable relay nodes in conventional clustered EH-CRSNs. Furthermore, the optimal cluster radius is theoretically derived with the objective of maximizing system EE, thereby constraining local control signaling and intra-cluster communication ranges to reduce energy consumption. High-quality CHs are then selected to form clusters through joint evaluation of node-level communication capacity and channel quality to enhance data transmission performance. Simulations indicate that the EEMCP protocol enables superior system EE, exceeding the peak EE of existing clustering protocols by at least 1.98 times.

在集群能量收集-认知无线电传感器网络（EH- crsns）中，EH和数据传输都依赖于直接链路，这会导致远程节点更快地消耗能量，从而缩短网络寿命。为了解决上述问题，本文将主动智能反射面（IRS）集成到EH-CRSNs中，并提出了一种面向系统能效（EE）最大化的聚类协议（EEMCP），以实现网络寿命和监控能力之间的权衡。具体而言，通过优化上行传输过程中主动IRS的反射系数矩阵，扩展了簇头（CHs）的传输范围，实现了与sink的直接通信，减轻了传统集群EH-CRSNs中由于缺乏合适中继节点而导致的数据传输失败。此外，从理论上推导出最优集群半径，以最大化系统EE为目标，从而约束本地控制信令和集群内通信范围以降低能耗。通过对节点级通信容量和信道质量的联合评价，选择高质量的CHs组成集群，提高数据传输性能。仿真结果表明，EEMCP协议具有较好的系统EE，比现有集群协议的峰值EE至少高出1.98倍。

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引用次数: 0

Distributed video analytics for IoT intelligent systems 物联网智能系统的分布式视频分析

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-14 DOI: 10.1016/j.adhoc.2026.104149

Rodolfo W.L. Coutinho , Azzedine Boukerche

Computer vision embedded in Internet of Things (IoT) systems will enable a new era of smart applications where video inference provides contextual awareness for the system. The limited resource capabilities of IoT devices and edge computing servers, often used to support computation-intensive IoT tasks, might not be enough to process video content produced by IoT devices in a computer vision-based smart system. In contrast to state-of-the-art where IoT video inference is performed locally at IoT devices or at edge and cloud servers, we propose a novel collaborative IoT paradigm where IoT devices share their idle resources for the processing of video frames in video analytics systems. We proposed a novel stochastic framework for modeling scenarios of collaborative IoT and edge/cloud continuum for video analytics systems. The proposed mathematical framework considers the unique characteristics of video analytics systems, IoT devices, and edge and cloud servers used to process video flows from IoT cameras in a collaborative manner. The obtained results show that the collaborative processing at neighboring IoT devices, i.e., IoT helpers, contributes to reduce the overall latency for video inference. However, high offloading costs might becoming a limiting factor which would request the design of more efficient offloading strategies.

嵌入在物联网（IoT）系统中的计算机视觉将开启智能应用的新时代，其中视频推理为系统提供上下文感知。物联网设备和边缘计算服务器的资源能力有限，通常用于支持计算密集型物联网任务，可能不足以处理基于计算机视觉的智能系统中物联网设备产生的视频内容。与在物联网设备或边缘和云服务器上本地执行物联网视频推理的最新技术相比，我们提出了一种新的协作物联网范式，其中物联网设备共享其空闲资源，用于视频分析系统中的视频帧处理。我们提出了一个新的随机框架，用于视频分析系统的协作物联网和边缘/云连续体的建模场景。提出的数学框架考虑了视频分析系统、物联网设备以及用于以协作方式处理来自物联网摄像机的视频流的边缘和云服务器的独特特征。获得的结果表明，相邻物联网设备（即物联网助手）的协同处理有助于减少视频推理的整体延迟。然而，高卸载成本可能成为一个限制因素，这将要求设计更有效的卸载策略。

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引用次数: 0

Energy-efficient trajectory planning for UAV-assisted communication recovery using multi-agent graph reinforcement learning 基于多智能体图强化学习的无人机辅助通信恢复节能轨迹规划

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-09 DOI: 10.1016/j.adhoc.2026.104145

Cong Wang , Menglong Dong , Ying Yuan , Guorui Li

Unmanned aerial vehicle base stations (UAV-BSs) are effective and rapid to provide recovery of emergency communication after disasters due to their maneuverability. However, the throughput of mobile terminals (MTs) is prone to be limited by the trajectory and energy constraints of UAV-BSs. To improve throughput for MTs while guaranteeing energy efficiency of UAV-BSs, we propose an energy-efficient trajectory planning framework based on multi-agent heterogeneous graph reinforcement learning. We formulate the joint optimization problem as a partially observable Markov decision process. Then, we propose a heterogeneous graph-based method to represent relationships between UAV-BSs and network entities. Subsequently, we design a multi-agent graph attention recurrent actor-critic framework (MA-GAR) to efficiently learn over the heterogeneous graphs. Finally, we introduce a digital twin empowered centralized training and decentralized execution mechanism in MA-GAR to reduce energy consumption of UAV-BSs. Experimental results show that the proposed MA-GAR outperforms the benchmark algorithms in convergence speed, system throughput, energy consumption, and service fairness.

无人机基站由于其机动性，在灾后应急通信恢复中发挥了高效、快速的作用。然而，移动终端（MTs）的吞吐量容易受到UAV-BSs的轨迹和能量限制。为了提高MTs的吞吐量，同时保证无人机- bss的能源效率，我们提出了一种基于多智能体异构图强化学习的节能轨迹规划框架。我们将联合优化问题表述为一个部分可观察的马尔可夫决策过程。然后，我们提出了一种基于异构图的方法来表示UAV-BSs与网络实体之间的关系。随后，我们设计了一个多智能体图注意循环行为者批评框架（MA-GAR）来有效地学习异构图。最后，我们在MA-GAR中引入了数字孪生授权的集中训练和分散执行机制，以降低无人机- bss的能耗。实验结果表明，该算法在收敛速度、系统吞吐量、能耗和服务公平性等方面均优于基准算法。

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引用次数: 0

Explainable energy-efficient UAV-assisted cluster-based data collection in WSNs 可解释的节能无人机辅助的基于簇的无线传感器网络数据收集

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-06 DOI: 10.1016/j.adhoc.2026.104137

Nadine Abbas

The use of unmanned aerial vehicles (UAVs) is becoming an integral element in modern wireless sensor networks (WSNs), due to their flexibility and cost-effectiveness, especially for data collection in challenging hard-to-reach environments. Cluster-based solutions further enhance data collection efficiency by allowing sensor nodes (SNs) to act as cluster heads (CHs) aggregating and relaying data to UAVs. Traditional approaches often rely on static clustering and lack transparency in decision-making regarding CH selection and UAV deployment. This work proposes an explainable energy-efficient UAV-assisted cluster-based data collection framework that integrates optimal and sub-optimal solutions as well as adopts machine learning-based CH prediction augmented with explainable AI techniques. First, we formulate a joint multi-objective optimization problem to minimize UAV usage, ensure energy-efficient CH selection, and guarantee data collection within deadline constraints. Second, we propose a sequential solving approach and then a scalable iterative cluster-based approach to provide real-time solutions for large-scale networks. Moreover, we develop machine learning (ML) models to predict CH selection using a customized dataset generated from extensive simulations of our proposed approach, capturing features like location, neighborhood density, data size, and deadlines. Furthermore, we use Explainable AI (XAI) techniques, particularly SHAP, to interpret the CH prediction model, providing insights into feature importance and decision rationale. This transparency enables network operators to validate CH assignments and strategically plan UAV deployment. Overall, the proposed framework achieves near-optimal trade-offs between UAV deployment, energy consumption, and execution time, leveraging flexible communication, emphasizing spatial and connectivity features and enhancing model interpretability for real-world applications.

由于其灵活性和成本效益，无人机的使用正成为现代无线传感器网络（wsn）的一个组成部分，特别是在具有挑战性的难以到达的环境中进行数据收集。基于集群的解决方案通过允许传感器节点（SNs）作为集群头（CHs）聚合和中继数据到无人机，进一步提高了数据收集效率。传统的方法通常依赖于静态聚类，在CH选择和无人机部署的决策中缺乏透明度。这项工作提出了一个可解释的节能无人机辅助基于集群的数据收集框架，该框架集成了最优和次最优解决方案，并采用了基于机器学习的CH预测和可解释的人工智能技术。首先，我们制定了一个联合多目标优化问题，以最大限度地减少无人机的使用，确保节能的CH选择，并保证在期限内收集数据。其次，我们提出了一种顺序求解方法，然后是基于可扩展迭代簇的方法，为大规模网络提供实时解决方案。此外，我们开发了机器学习（ML）模型，使用从我们提出的方法的广泛模拟生成的自定义数据集来预测CH选择，捕获位置，邻居密度，数据大小和截止日期等特征。此外，我们使用可解释的人工智能（XAI）技术，特别是SHAP，来解释CH预测模型，提供对特征重要性和决策原理的见解。这种透明度使网络运营商能够验证CH分配并战略性地规划无人机部署。总体而言，所提出的框架在无人机部署、能耗和执行时间之间实现了近乎最佳的权衡，利用了灵活的通信，强调了空间和连通性特征，并增强了模型对现实世界应用的可解释性。

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引用次数: 0

DRUID: Coordinating drone movements for compromised node identification 德鲁伊：协调无人机移动以识别受损节点

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-05 DOI: 10.1016/j.adhoc.2026.104135

Mauro Farina, Erica Salvato, Martino Trevisan, Alberto Bartoli

In recent years, Unmanned Aerial Vehicles (UAVs) (also called drones) networks have become increasingly popular in scenarios where rapid deployment, flexible mobility, and real-time data acquisition are crucial, such as disaster relief, environmental monitoring, military operations, and smart city infrastructure. However, due to their dynamic nature and dependence on wireless communication, they are intrinsically vulnerable to a variety of cyberattacks. In this work, we present DRUID, a decentralized scheme for silently identifying a compromised drone that selectively alters the messages it forwards. The scheme uses a combination of secret sharing and multipath routing to allow a pair of communicating drones, namely

A

and

B

, to detect the presence of a compromised drone along any route between them, thereby categorizing each route as either safe or compromised. The scheme operates iteratively and consists of three key modules: (i) an Information Retrieval Procedure that allows

A

to learn more about the topology, (ii) a binary search-like Identification Procedure, and (iii) if the previous module fails to identify the compromised drone, a Node Repositioning Procedure that relocates nodes closer to the compromised path. We validate DRUID on a large and diverse set of 178 731 graphs representing realistic UAV networks with different communication ranges. Comparing our scheme to previous work, experiments show that DRUID achieves a 97 % identification rate—up from the 54 % of the most recent alternative approach. We analyze the cost associated with the node repositioning procedure in terms of computation time and drone movement, and show that it generally takes a few seconds.

近年来，在救灾、环境监测、军事行动和智慧城市基础设施等快速部署、灵活机动和实时数据采集至关重要的场景中，无人驾驶飞行器（uav）（也称为无人机）网络越来越受欢迎。然而，由于它们的动态性和对无线通信的依赖性，它们本质上容易受到各种网络攻击。在这项工作中，我们提出了DRUID，这是一种分散的方案，用于无声地识别受损的无人机，并选择性地更改其转发的消息。该方案使用秘密共享和多路径路由的组合，允许一对通信无人机，即a和B，在它们之间的任何路线上检测到受损无人机的存在，从而将每条路线分类为安全或受损。该方案迭代运行，由三个关键模块组成：(i)允许A了解更多拓扑信息的信息检索过程，（ii）类似二进制搜索的识别过程，以及（iii）如果前一个模块无法识别受损无人机，则节点重新定位过程将节点重新定位到更靠近受损路径的地方。我们在具有不同通信范围的实际无人机网络的178 731张图上验证了DRUID。将我们的方案与之前的工作进行比较，实验表明DRUID的识别率达到97%，而最近的替代方法的识别率为54%。我们从计算时间和无人机移动角度分析了节点重新定位过程的相关成本，并表明它通常需要几秒钟。

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引用次数: 0

Kalman filter scheduling for 6TiSCH network with traffic adaptation optimized for bursty traffic 基于突发通信量的6TiSCH网络自适应卡尔曼滤波调度

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2026-01-05 DOI: 10.1016/j.adhoc.2025.104130

Yan Zhang, Shijie Xu, Qingqing Huang, Yan Han

The sensor nodes equipped with IEEE 802.15.4e (6TiSCH) wireless protocol stack and IPv6 time slot channel hopping mode have deterministic network characteristics after networking, providing low-latency and highly reliable communication for industrial scenarios with growing demand for low-power sensor networks. However, existing scheduling algorithms perform poorly under the bursty traffic commonly found in industrial environments. Due to the limitations of their design principles, they are unable to respond quickly to changes in traffic or differentiate between bursty traffic patterns to accurately sense traffic conditions, resulting in high latency, low reliability and additional power consumption. Therefore, we propose a scheduling method called the Kalman Filter Traffic Sensing Prediction Scheduling Function (KSF). KSF utilizes the filtered processing of node Cell usage and per-slot frame queue increment as the primary basis for scheduling decisions, coupled with adaptive filtering parameters, to achieve the ability to ignore transient fluctuation noise and respond quickly after the occurrence of bursts. In addition, we utilize filtering to predict the ratio of the number of received data packets to the number of sent data packets in the next slot frame to distinguish burst patterns and dynamically change KSF’s scheduling strategy. Experiments demonstrate that KSF exhibits more optimal scheduling performance under bursty traffic conditions, reducing latency by 14.82% compared to the well-known OTF while maintaining the lowest power consumption across all traffic rates.

采用IEEE 802.15.4e （6TiSCH）无线协议栈和IPv6时隙信道跳变模式的传感器节点组网后具有确定性的网络特性，为低功耗传感器网络需求日益增长的工业场景提供低时延、高可靠的通信。然而，现有的调度算法在工业环境中常见的突发流量下表现不佳。由于其设计原则的限制，它们无法快速响应流量变化或区分突发流量模式以准确感知交通状况，从而导致高延迟、低可靠性和额外的功耗。因此，我们提出一种调度方法，称为卡尔曼滤波交通感知预测调度函数（KSF）。KSF利用节点Cell使用率和每插槽帧队列增量的滤波处理作为调度决策的主要依据，再加上自适应滤波参数，实现了忽略瞬态波动噪声和在突发发生后快速响应的能力。此外，我们利用过滤来预测下一个槽帧中接收数据包数量与发送数据包数量的比例，以区分突发模式并动态改变KSF的调度策略。实验表明，KSF在突发流量条件下表现出更优的调度性能，与众所周知的OTF相比，延迟降低了14.82%，同时在所有流量速率下保持最低的功耗。

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引用次数: 0

UpsFed-IDS: U-shaped split federated intrusion detection system for securing UAV communication in dynamic networks UpsFed-IDS：用于动态网络中无人机通信安全的u形分离联邦入侵检测系统

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-31 DOI: 10.1016/j.adhoc.2025.104133

Zongpu Wei, Jinsong Wang, Zening Zhao, Zhao Zhao, Kai Shi

Integrating an intrusion detection system (IDS) into UAVs is critical for safeguarding their operational reliability and overall security. Centralized IDS deployed in data centers has become impractical, primarily due to concerns over data privacy and computational constraints. Federated learning (FL)-based IDS alleviates the data leakage issue inherent in traditional IDS. Nevertheless, its integration with UAV systems still encounters unavoidable challenges. Firstly, the requirement for local model training on UAVs imposes substantial computational overhead. Secondly, the non-independent and identically distributed (non-IID) data characteristics of UAVs directly impair the performance of the IDS model. Thirdly, the constant dynamic changes in UAV network connectivity undermine the robustness of the federated IDS. To address these challenges, this paper presents a U-shaped split federated intrusion detection system (UpsFed-IDS) for securing UAV communication. Inspired by FL and Split Learning (SL), we offload a portion of the IDS model training to the Ground Control Station (GCS). This approach ensures that raw data and labels remain on the UAVs, which enhances data privacy protection and reduces the computational overhead on the UAV side. Within this system, we propose a split-specific head personalization method to decouple global feature learning from local model personalization under the SL scheme, which strengthens the IDS model performance in heterogeneous data scenarios. Furthermore, a client failover mechanism is designed to tackle disconnections occurring during training in dynamic UAV networks, which effectively improves the overall robustness of the system. Extensive experimental evaluations are conducted on the UAVCAN attack and WSN-DS datasets. The results demonstrate that UpsFed-IDS outperforms existing FL frameworks in both attack recognition performance and local computation overhead.

在无人机中集成入侵检测系统（IDS）对于保障无人机的运行可靠性和整体安全性至关重要。部署在数据中心的集中式IDS已经变得不切实际，这主要是由于对数据隐私和计算限制的担忧。基于联邦学习（FL）的入侵检测缓解了传统入侵检测固有的数据泄漏问题。然而，它与无人机系统的融合仍然面临着不可避免的挑战。首先，对无人机进行局部模型训练的要求带来了大量的计算开销。其次，无人机数据的非独立和同分布（non-IID）特性直接影响了IDS模型的性能。第三，无人机网络连通性的不断动态变化削弱了联邦入侵检测系统的鲁棒性。为了解决这些挑战，本文提出了一种u形分裂联邦入侵检测系统（UpsFed-IDS），用于保护无人机通信。受FL和分裂学习（SL）的启发，我们卸载了一部分IDS模型训练到地面控制站（GCS）。这种方法确保原始数据和标签保留在无人机上，从而增强了数据隐私保护并减少了无人机方面的计算开销。在该系统中，我们提出了一种针对分裂的头部个性化方法，将全局特征学习与局部模型个性化解耦，从而增强了IDS模型在异构数据场景下的性能。此外，设计了一种客户端故障转移机制来解决动态无人机网络在训练过程中出现的断开问题，有效地提高了系统的整体鲁棒性。对UAVCAN攻击和WSN-DS数据集进行了广泛的实验评估。结果表明，UpsFed-IDS在攻击识别性能和局部计算开销方面都优于现有的FL框架。

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引用次数: 0

A deep learning-based approach for heterogeneous hotspot-coverage in UAV deployment 基于深度学习的无人机部署异构热点覆盖方法

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-30 DOI: 10.1016/j.adhoc.2025.104127

Kolichala Rajashekar , Vamsi Krishna Sunkara , Subhajit Sidhanta

The deployment of unmanned aerial vehicles (UAVs) for wireless coverage in dynamic environments, such as public gatherings, road junctions, and urban intersections, presents numerous challenges owing to variations in the size of the hotspots, the mobility patterns of the users, and quality of service (QoS) requirements. Although the iterative or heuristic algorithms used in previous papers can potentially adapt to these changes, they would either incur significant runtime overhead on computationally constrained UAV hardware or require uninterrupted backhaul communication. In this paper, we formalize the above Dynamic UAV Deployment (DUDE) problem, show that it is NP-hard, and propose a hybrid Convolutional Neural Network-based (CNN-based) approach to predict the optimal 3D placement of a single UAV. Our CNN-based model is trained on a custom synthetic dataset that encompasses diverse user distributions and hotspot sizes, allowing it to perform extensive offline training and then infer UAV positions online in real-time, thereby eliminating the need for repeated online iterations. Experimental results demonstrate that our model achieves a mean absolute error of 3.5 and an average

R^{2}

score exceeding 96% in predicting the UAV’s 3D position across heterogeneous hotspot areas and different statistical distributions of position of users. We also provide extensive comparisons with greedy user-assignment schemes and demonstrate improved connectivity under QoS constraints.

由于热点的大小、用户的移动性模式和服务质量（QoS）要求的变化，在公共集会、道路路口和城市十字路口等动态环境中部署无人驾驶飞行器（uav）进行无线覆盖带来了许多挑战。虽然以前论文中使用的迭代或启发式算法可以潜在地适应这些变化，但它们要么会在计算受限的无人机硬件上产生显著的运行时开销，要么需要不间断的回程通信。在本文中，我们形式化了上述动态无人机部署（DUDE）问题，证明了它是np困难的，并提出了一种基于混合卷积神经网络（cnn）的方法来预测单个无人机的最佳3D布局。我们基于cnn的模型是在包含不同用户分布和热点大小的自定义合成数据集上训练的，允许它进行广泛的离线训练，然后实时在线推断无人机位置，从而消除了重复在线迭代的需要。实验结果表明，该模型在异质热点区域和不同用户位置统计分布下预测无人机三维位置的平均绝对误差为3.5，平均R2评分超过96%。我们还提供了与贪婪用户分配方案的广泛比较，并展示了在QoS约束下改进的连通性。

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引用次数: 0

Reliability analysis of multi-state wireless sensor networks with functional dependency based on dynamic Bayesian networks 基于动态贝叶斯网络的功能依赖多状态无线传感器网络可靠性分析

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-30 DOI: 10.1016/j.adhoc.2025.104125

Haozhe Liu , Jinfang Zhao , Qun Zhao , Hongliang Sun

Wireless sensor networks (WSNs) are extensively employed in contemporary practical applications. Consequently, analyzing the reliability of WSNs is a significant research area. Recent research has focused on the impact of multiple operational states and functional dependencies on system reliability. However, current reliability modeling approaches rarely address both the effects of data transmission blocking and component dependency failures. Furthermore, studies on system states frequently neglect the diverse operational modes of WSNs, potentially leading to an inaccurate characterization of system behavior over time. To address these shortcomings, this study conceptualizes multi-state WSNs as modular k-out-of-n systems with FDEP, where each module comprising a cluster head (CH) node and its corresponding sensor nodes. Dynamic Bayesian network (DBN) models are employed to construct the structure function of the multi-state WSN. The parameters encoded in the DBN graphical structure of the multi-state WSN are generated automatically by a customized algorithm. Furthermore, an inferencing α-factor method is introduced in DBN model to integrate prior knowledge with observations for updating system reliability while accounting for common cause failures (CCFs). Finally, taking a multi-state meteorological surveillance system as an example, its traffic model is multi-hop transmission, consisting of 8 modules and 46 sensor nodes. The dynamic reliability was evaluated comprehensively when considering FDEP and CCF to illustrate applicability of the proposed framework.

无线传感器网络在当今的实际应用中得到了广泛的应用。因此，分析无线传感器网络的可靠性是一个重要的研究领域。最近的研究主要集中在多种运行状态和功能依赖对系统可靠性的影响。然而，目前的可靠性建模方法很少同时考虑数据传输阻塞和组件依赖故障的影响。此外，对系统状态的研究往往忽略了WSNs的各种工作模式，这可能导致对系统行为随时间变化的不准确描述。为了解决这些缺点，本研究将多状态wsn概念化为具有FDEP的模块化k-out- n系统，其中每个模块包括一个簇头（CH）节点及其相应的传感器节点。采用动态贝叶斯网络（DBN）模型构建多状态无线传感器网络的结构函数。多状态WSN的DBN图形结构中编码的参数通过自定义算法自动生成。此外，在DBN模型中引入推理α-因子方法，将先验知识与观测结果相结合，在考虑共因故障（CCFs）的同时更新系统可靠性。最后，以某多状态气象监测系统为例，其业务模型为多跳传输，由8个模块和46个传感器节点组成。在考虑FDEP和CCF的情况下，对框架的动态可靠性进行了综合评估，以说明该框架的适用性。

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引用次数: 0

A reinforcement learning–based active interception algorithm for wireless networks topology identification 一种基于强化学习的无线网络拓扑识别主动拦截算法

IF 4.8 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Ad Hoc Networks

Pub Date : 2025-12-30 DOI: 10.1016/j.adhoc.2025.104134

Liping Luo , Zhou Peng , Han Xu , Renhai Feng

Accurate Topology Identification (TI) in non-cooperative networks is critical, particularly during various communication engagements that demand low computational overhead. Active interception has proven effective in such scenarios. Specifically, active interception is performed on full-duplex eavesdroppers which cause frequency hopping, thereby obtaining corresponding received signal strength as indicator. However, its interference power adjustment requires numerous iterations and causes overwhelming frequency hopping. This paper proposes a novel Reinforcement Learning-based Active Interception and Node Localization (RLAI-NL) method. RLAI-NL aims to accurately identify network topology. Four different frequency hopping patterns are designed to evaluate the performance of RLAI-NL. Using Reinforcement Learning (RL), an intelligent agent is trained to dynamically adjust its interference power. Through dynamic learning and policy optimization, the agent avoids unnecessary power consumption associated with specially designed search strategies, while adapting effectively to both small- and large-scale networks as well as various communication modes. Simulation results demonstrate that RLAI significantly outperforms traditional active interception methods, achieving 99% accuracy with fewer frequency hops and iterations, thereby reducing computational complexity and power consumption.

在非合作网络中，精确的拓扑识别（TI）是至关重要的，特别是在需要低计算开销的各种通信约定中。在这种情况下，主动拦截已被证明是有效的。具体来说，对引起跳频的全双工窃听器进行主动拦截，从而获得相应的接收信号强度作为指标。但其干扰功率调整需要多次迭代，且会造成压倒性的跳频。提出了一种基于强化学习的主动拦截与节点定位（RLAI-NL）方法。RLAI-NL旨在准确识别网络拓扑。设计了四种不同的跳频模式来评估RLAI-NL的性能。利用强化学习（RL），训练智能体动态调整其干扰功率。通过动态学习和策略优化，智能体避免了特殊设计的搜索策略所带来的不必要的功耗，同时有效地适应小型和大型网络以及各种通信模式。仿真结果表明，RLAI显著优于传统的主动拦截方法，以更少的跳频和迭代次数达到99%的准确率，从而降低了计算复杂度和功耗。

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引用次数: 0