Ad Hoc Networks最新文献

A DEPMU-based network traffic anomaly detection scheme for IoT 基于depmu的物联网网络流量异常检测方案

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-01-29 DOI: 10.1016/j.adhoc.2026.104160

Yueling Liu, Chunhai Li, Changsong Yang, Yong Ding

{"title":"A DEPMU-based network traffic anomaly detection scheme for IoT","authors":"Yueling Liu, Chunhai Li, Changsong Yang, Yong Ding","doi":"10.1016/j.adhoc.2026.104160","DOIUrl":"10.1016/j.adhoc.2026.104160","url":null,"abstract":"<div><div>Thanks to the rapid development and widespread popularity of wireless network technology, Internet of Things (IoT) has been broadly used by the public in the daily life and work due to its convenience, low delay and high-efficiency. Despite plenty of tremendous advantages, IoT also suffers from some serious security problems and technology issues, for instance, dishonest user attack, malicious hacker intrusion, etc. For discovering malicious attacks, network traffic anomaly detection (NTAD) system has been deployed in IoT. However, in IoT, the network traffic data is characterized by massive, irregularity, temporal correlation, multiple feature and high dimensionality. These characteristics will greatly reduce the detection performance of NTAD. In this article, to solve the above issues, we aim to design a new NTAD scheme. Specifically, inspired by the traditional parsimonious memory unit (PMU), we design a new neural network model called deep encoder parsimonious memory unit (DEPMU), which consists of the encoding parsimonious memory unit (EPMU), the decoding parsimonious memory unit (DPMU), the loss compensation parsimonious memory unit (LEPMU), and two loss functions. Compared with the original PMU, DEPMU can better characterize and learn the time-series data, and can reduce the feature loss by adding a loss compensation mechanism. Subsequently, we adopt DEPMU to design a NTAD scheme for IoT, which can greatly improve the anomaly detection performance. Meanwhile, we prove the high efficiency of our scheme through computational complexity analysis. Finally, we also develop a prototype system and implement our scheme to test the overall performance. We can discover from the experimental results that our scheme can achieve better performance compared with some existing schemes.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104160"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-layer joint optimization for semantic communication-driven MEC systems via deep reinforcement learning 基于深度强化学习的语义通信驱动MEC系统跨层联合优化

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-01-29 DOI: 10.1016/j.adhoc.2026.104159

Meiyao Wen, Linyu Huang, Qian Ning

{"title":"Cross-layer joint optimization for semantic communication-driven MEC systems via deep reinforcement learning","authors":"Meiyao Wen, Linyu Huang, Qian Ning","doi":"10.1016/j.adhoc.2026.104159","DOIUrl":"10.1016/j.adhoc.2026.104159","url":null,"abstract":"<div><div>The integration of semantic communication (SemCom) with mobile edge computing (MEC) has opened new avenues to improve task execution efficiency in intelligent networks. This paper proposes a cross-layer joint optimization framework for SemCom-driven MEC systems, aiming to minimize the weighted sum of task completion time and user energy consumption. Specifically, the framework jointly optimizes the semantic extraction factor at the application layer, task offloading decisions at the control layer, and communication and computational resource allocation at the network and physical layers. To address the non-convex and mixed-integer nature of the problem, a Deep Deterministic Policy Gradient (DDPG)-based algorithm was employed to efficiently search for solutions. The simulation results validate the effectiveness of the proposed approach and demonstrate that the integration of SemCom into MEC significantly improves the system performance. The findings offer practical insights for system engineers to design efficient MEC systems, reducing transmission overhead and energy consumption, especially in latency-sensitive applications such as autonomous driving and industrial Internet of Things.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104159"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving object selection for Collective Perception Messages under congestion 在拥塞条件下改进集体感知信息的对象选择

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-02-05 DOI: 10.1016/j.adhoc.2026.104175

Andreia Figueiredo , João Amaral , Pedro Rito , Miguel Luís , Susana Sargento

{"title":"Improving object selection for Collective Perception Messages under congestion","authors":"Andreia Figueiredo , João Amaral , Pedro Rito , Miguel Luís , Susana Sargento","doi":"10.1016/j.adhoc.2026.104175","DOIUrl":"10.1016/j.adhoc.2026.104175","url":null,"abstract":"<div><div>Collective Perception Messages (CPMs), defined by European Telecommunications Standards Institute (ETSI), enable vehicles and roadside infrastructure to exchange information about detected objects, enhancing situational awareness in cooperative environments. However, as the size of CPMs increases — particularly in dense traffic scenarios — the wireless channel can become saturated, leading to delays in transmission and reduced packet delivery ratios. This paper starts by assessing how the number of objects included per CPM impacts communication performance, highlighting the necessity for effective object selection strategies during periods of congestion. To address this issue, we propose a lightweight, real-time object prioritization algorithm based on deviations from the predicted path. Our method estimates each object’s expected state based on its last transmission, and prioritizes those whose current state deviates most from this prediction, as these are likely to be more informative. The evaluation uses a real-world dataset and demonstrates that our strategy significantly improves predictive accuracy by at least 7%. Moreover, the algorithm does not increase CPU or memory usage, demonstrating similar resource consumption compared to the method described in the Collective Perception Service (CPS) standard, making it well-suited for embedded platforms. These results confirm that Prediction–Deviation selection can enhance the efficiency and informativeness of CPMs, especially when the message size must be constrained due to network congestion.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104175"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HetTraffic: Multi-link traffic prediction and allocation for 6G heterogeneous networks HetTraffic：针对6G异构网络的多链路流量预测与分配

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-01-31 DOI: 10.1016/j.adhoc.2026.104153

Yali Lv , Jian Huang , Jingpu Duan , Yaping Sun , Xiong Li

{"title":"HetTraffic: Multi-link traffic prediction and allocation for 6G heterogeneous networks","authors":"Yali Lv , Jian Huang , Jingpu Duan , Yaping Sun , Xiong Li","doi":"10.1016/j.adhoc.2026.104153","DOIUrl":"10.1016/j.adhoc.2026.104153","url":null,"abstract":"<div><div>The rapid evolution of wireless communication necessitates advanced solutions beyond current 5G capabilities to realize the ambitious vision of 6G. The forthcoming 6G era will witness an unprecedented scale of device connectivity, challenging conventional resource allocation paradigms with its inherent heterogeneity and dynamic nature. A key issue involves intelligently and dynamically assigning diverse user traffic to highly heterogeneous links, while still satisfying Quality of Service (QoS) requirements. Moreover, resource management strategies that rely solely on reactive real-time measurements often lead to suboptimal performance. To overcome these limitations, this paper proposes <em>HetTraffic</em>, a novel comprehensive framework for joint traffic prediction and allocation in 6G heterogeneous networks. <em>HetTraffic</em> first introduces a novel link-level traffic prediction method leveraging a hybrid Graph Attention Network (GAT) and Long Short-Term Memory (LSTM) architecture. This approach effectively captures both the complex spatial dependencies from user mobility and the temporal fluctuations within traffic data. Building upon these predictions, we develop a multi-agent reinforcement learning-based allocation strategy utilizing the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm. This is designed for efficient, decentralized resource optimization across heterogeneous links, proactively accounting for real-time conditions, QoS demands, and predicted traffic. Comprehensive experiments conducted on a dedicated 6G heterogeneous network testbed, utilizing a curated link-level traffic dataset, demonstrate the significant advantages and superior performance of our proposed traffic prediction and allocation methods compared to existing state-of-the-art approaches.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104153"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Target localization in UAV swarm under multi-error coupling: A cooperative utility of information optimization approach 多误差耦合下无人机群目标定位：一种信息优化协同效用方法

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-01-22 DOI: 10.1016/j.adhoc.2026.104154

Zou Zhou, Zuozhun Qin, Jie Peng, Hongbing Qiu, Junyi Wang

{"title":"Target localization in UAV swarm under multi-error coupling: A cooperative utility of information optimization approach","authors":"Zou Zhou, Zuozhun Qin, Jie Peng, Hongbing Qiu, Junyi Wang","doi":"10.1016/j.adhoc.2026.104154","DOIUrl":"10.1016/j.adhoc.2026.104154","url":null,"abstract":"<div><div>In complex electromagnetic environments, the scale effect of Unmanned Aerial Vehicle (UAV) swarm presents significant potential for enhancing cooperative effectiveness. However, the accuracy of Time Difference of Arrival (TDOA)-based localization for non-cooperative emitters using UAV swarm is significantly affected by the coupling of multi-source errors, which mainly include UAV position error (UPE), clock synchronization error (CSE), and TDOA measurement error (TME). To address the challenges of evaluating cooperative effectiveness under multi-source errors coupling and balancing localization accuracy with computational efficiency, a cooperative utility of information (CUoI) optimization approach is proposed. First, a TDOA observation uncertainty model is constructed by integrating multi-source errors. Then, the information gain of target position estimation is derived to build the CUoI evaluation model. Next, the characteristic of Dueling Deep Q-Network (Dueling DQN) that decouples state value from action advantage is leveraged, enabling precise evaluation of the potential benefits of different hyperparameter adjustment strategies. This characteristic facilitates adaptive tuning of key hyperparameters in Particle Swarm Optimization (PSO). Finally, a dynamic PSO framework based on Dueling DQN is proposed to effectively balance localization accuracy and computational efficiency. Numerical experiments demonstrate that the proposed algorithm achieves reductions in average localization RMSE of 19.1%, 6.0%, and 1.4%, respectively, compared to Semidefinite Relaxation-TDOA (SDR-TDOA), Grey Wolf Optimizer (GWO), and Multi-swarm Discrete Quantum-inspired Particle Swarm Optimization with Adaptive Simulated Annealing (MDQPSO-ASA).</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104154"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-agent DRL-based task offloading and trajectory optimization for low altitude UAV IoT systems 基于多智能体drl的低空无人机物联网系统任务卸载与轨迹优化

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-02-03 DOI: 10.1016/j.adhoc.2026.104164

Shanchen Pang , Miaomiao Fan , Xiao He , Wenhao Ji , Sibo Qiao , Chenhao Zhang

{"title":"Multi-agent DRL-based task offloading and trajectory optimization for low altitude UAV IoT systems","authors":"Shanchen Pang , Miaomiao Fan , Xiao He , Wenhao Ji , Sibo Qiao , Chenhao Zhang","doi":"10.1016/j.adhoc.2026.104164","DOIUrl":"10.1016/j.adhoc.2026.104164","url":null,"abstract":"<div><div>In low-altitude Internet of Things (IoT) networks, the Unmanned Aerial Vehicle (UAV) is employed as a mobile edge node to provide computational services for task processing. However, the spatio-temporal dynamics of User Devices (UDs) and the heterogeneity of task prioritization exacerbate the multidimensional resource competition encountered during the processing of tasks. This significantly affects energy consumption, service delay, and task completion rate, degrading user Quality of Service (QoS). To address these challenges, we propose a collaborative Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to improve user QoS through the joint optimization of UAV three-Dimensional (3D) trajectories, resource allocation, and task offloading strategies. Specifically, we design a Graph Convolutional Network (GCN)-based UAV actor network to optimize the dynamic trajectory by modeling user distribution in a topology-aware manner. In addition, we construct a centralized critic network based on a multi-head attention mechanism, wherein attention scaling is utilized to quantify differences in task demands and guide resource decision-making. These two components are jointly optimized through a ”topology association–demand difference” cooperative evaluation mechanism, enabling a multi-dimensional coupling of spatio-temporal characteristics and task demand decision-making. Experimental results demonstrate that the proposed algorithm reduces system energy consumption and delay by approximately 18.5% and 22.7%, respectively, while improving the task completion rate by about 16.2%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104164"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DDPG-based data collection for AoI in multi-UAV-assisted IoT networks 基于ddpg的多无人机辅助物联网AoI数据采集

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-02-01 DOI: 10.1016/j.adhoc.2026.104162

Jianbin Xue , Xiao Li , Zhenqin Wang , Chang Li

{"title":"DDPG-based data collection for AoI in multi-UAV-assisted IoT networks","authors":"Jianbin Xue , Xiao Li , Zhenqin Wang , Chang Li","doi":"10.1016/j.adhoc.2026.104162","DOIUrl":"10.1016/j.adhoc.2026.104162","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are increasingly employed to facilitate effective information acquisition within Internet of Things (IoT) systems due to their superior mobility and operational flexibility. Maintaining the freshness of information in UAV-assisted IoT systems is crucial for real-time monitoring applications, particularly when dealing with stochastic generation patterns of sensory data. Orchestrating multiple energy-constrained UAVs to ensure the temporal validity of collected information poses significant technical challenges due to dynamic mission constraints and limited onboard power supplies. To address this issue, we investigate the problem of information freshness optimization in a multi-UAV collaborative data collection environment, proposes an attention-based deep deterministic policy gradient (A-DDPG) algorithm, constructs a multi-UAV-assisted IoT data collection system model that considers data freshness, communication quality, and energy efficiency, and models the UAV trajectory planning, hovering point selection, and task assignment problems as a Markov decision process. Due to the limitations of the standard DDPG algorithm in handling high-dimensional state spaces and multiple constraints, we introduce an attention layer in the A-DDPG algorithm to enhance the perception of key state features, designs a prioritized experience replay mechanism to enhance data sampling efficiency in reinforcement learning processes., implements normalization strategies based on the characteristics of different state components, and develops action constraint handling methods to ensure that UAV behaviors meet physical constraints. Through comprehensive simulation tests, the proposed algorithm is compared with existing technologies, demonstrating its high effectiveness in terms of average age of information, energy efficiency, and task completion rate.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104162"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

qIoV: A quantum-driven approach for environmental monitoring and rapid response systems using internet of vehicles qIoV：一种量子驱动的方法，用于环境监测和使用车联网的快速反应系统

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-02-02 DOI: 10.1016/j.adhoc.2026.104158

Ankur Nahar , Koustav Kumar Mondal , Debasis Das , Rajkumar Buyya

{"title":"qIoV: A quantum-driven approach for environmental monitoring and rapid response systems using internet of vehicles","authors":"Ankur Nahar , Koustav Kumar Mondal , Debasis Das , Rajkumar Buyya","doi":"10.1016/j.adhoc.2026.104158","DOIUrl":"10.1016/j.adhoc.2026.104158","url":null,"abstract":"<div><div>This paper addresses the critical demand for advanced rapid response mechanisms in managing a wide array of environmental hazards, including urban pipeline leaks, industrial gas discharges, methane emissions from landfills, chlorine leaks from water treatment plants, and residential carbon monoxide releases. Conventional sensing and alert systems often struggle with the timely analysis of high-dimensional sensor data and suffer delays as data volume increases. We propose a novel framework, <em>qIoV</em>, which integrates quantum computing with the Internet of Vehicles (IoVs) to leverage the computational efficiency, parallelism, and entanglement properties inherent in quantum mechanics. The qIoV framework utilizes vehicular-mounted environmental sensors for highly accurate air quality assessments, where quantum principles enhance both sensitivity and precision. A core innovation is the Quantum Mesh Network Fabric (QMF), which dynamically adapts the quantum network topology to vehicular movement, maintaining quantum state integrity among environmental and vehicular disruptions, thereby ensuring robust data transmission. Furthermore, we implement a variational quantum classifier (VQC) with advanced entanglement techniques, significantly reducing latency in hazard alerts and facilitating rapid communication with emergency response teams and the public. Our experimental evaluations using the IBM OpenQASM 3 platform with a 127-qubit system achieved over 90% precision, recall, and F1-score in pair plot analysis, alongside an 83% increase in toxic gas detection speed compared to conventional methods. Theoretical analysis further substantiates the efficiency of quantum rotation, teleportation protocols, and the fidelity of quantum entanglement, highlighting the potential of quantum computing in environmental hazard management.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104158"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepSpect: An RF spectrogram-based deep learning approach for near-real-time attack detection in FANETs DeepSpect：一种基于射频频谱图的深度学习方法，用于近实时攻击检测

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-02-12 DOI: 10.1016/j.adhoc.2026.104178

Cengizhan Yapıcıoğlu , Sedef Demirci , Mehmet Demirci

{"title":"DeepSpect: An RF spectrogram-based deep learning approach for near-real-time attack detection in FANETs","authors":"Cengizhan Yapıcıoğlu , Sedef Demirci , Mehmet Demirci","doi":"10.1016/j.adhoc.2026.104178","DOIUrl":"10.1016/j.adhoc.2026.104178","url":null,"abstract":"<div><div>Flying ad-hoc networks (FANETs) facilitate autonomous communication and collaboration among unmanned aerial vehicles (UAVs) and are increasingly utilized in defense, disaster response, agriculture, and environmental monitoring. However, their limited computational resources and critical operational roles make them susceptible to cyber–physical threats such as jamming, deauthentication, and physical attacks. Existing solutions often target individual attacks and rely on complex, resource-intensive methods that are impractical for lightweight drones. In this study, we propose a novel deep learning-based approach for near-real-time multi-class attack detection in FANETs using RF spectrogram images. RF spectrograms provide a robust, environment-independent representation of drone communications, enabling accurate attack detection without high computational overhead. We introduce DroneAttackRF, the first publicly available real-world dataset of RF spectrograms collected from DJI Ryze Tello and Piranha F-55 drones under various attack scenarios. We develop and evaluate seven deep learning classifiers, including two customized models based on CNN and Autoencoder, as well as five transfer learning models based on VGG-16, ResNet50, InceptionV3, MobileNet, and Xception. The developed models achieved competitive or higher performance compared to prior studies, with the CNN-based model attaining 98.9% accuracy in multi-class detection of different attack types, though dataset and methodology differences limit the feasibility of direct comparison. Additionally, our approach demonstrated fast detection capability, with RF spectrogram acquisition taking only 0.52 s and CNN-based attack classification completing in 0.55 s. The proposed approach demonstrates significant improvements in detection accuracy and efficiency, offering a practical and scalable solution for enhancing UAV network security.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104178"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A spatio-temporal graph learning framework with attention mechanism for secure RPL in mobile IoT 基于注意机制的移动物联网安全RPL时空图学习框架

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2026-04-15 Epub Date: 2026-01-27 DOI: 10.1016/j.adhoc.2026.104156

Zohre Shoaei, Rasool Esmaeilyfard, Reza Javidan

{"title":"A spatio-temporal graph learning framework with attention mechanism for secure RPL in mobile IoT","authors":"Zohre Shoaei, Rasool Esmaeilyfard, Reza Javidan","doi":"10.1016/j.adhoc.2026.104156","DOIUrl":"10.1016/j.adhoc.2026.104156","url":null,"abstract":"<div><div>Mobility‑aware IoT networks operate under rapidly shifting topologies, where even authorized nodes can perform stealthy routing attacks that bypass standard cryptographic defenses. These threats are compounded by dynamic connectivity patterns, fluctuating link qualities, and heterogeneous node behaviors, creating a high‑dimensional, non‑stationary security landscape. We introduce a temporal–spatial trust framework that represents the network as a continuously evolving dynamic graph, embedding per‑node behavioral states together with aggregated neighborhood patterns across structural, mobility, and traffic domains. These high‑context sequences feed into a multi‑layer GRU‑based Sequence‑to‑Sequence architecture equipped with multi‑head attention, enabling concurrent modeling of local temporal fluctuations and long‑range spatial dependencies. A composite trust scoring mechanism integrates model‑inferred anomalies with deterministic protocol checks and peer‑reported reputation, regulated by hyper‑parameter‑optimized fusion weights. Trust scores are embedded into RPL’s rank metric and filtered through a hysteresis‑governed parent selection policy to ensure both rapid threat isolation and topological stability. Extensive simulations in Contiki-NG, leveraging real-world urban mobility traces from the Microsoft GeoLife dataset and the RADAR benchmark, demonstrate robustness against five specific threats (Rank, Blackhole, Sybil, Sinkhole, and Selective Forwarding). Results indicate up to 96 % detection accuracy, a 38 % reduction in detection latency, and 20–40 % lower control overhead, all while maintaining a runtime memory footprint under 10 KB. By combining dynamic graph‑based context encoding, attention‑driven sequence learning, and multi‑source trust fusion, the proposed approach offers a deployable, high‑fidelity, and scalable security enhancement for RPL in next‑generation IoT environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"185 ","pages":"Article 104156"},"PeriodicalIF":4.8,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0