Ad Hoc Networks最新文献_第4页

Anomaly detection in unmanned aerial vehicles flight data: A survey 无人机飞行数据异常检测研究进展

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-08-22 DOI: 10.1016/j.adhoc.2025.103989

Ahad Ghasemi , Ali Ghaffari , Nahideh Derakhshanfard , Nadir iBRAHIMOĞLU , Amir pakmehr

{"title":"Anomaly detection in unmanned aerial vehicles flight data: A survey","authors":"Ahad Ghasemi , Ali Ghaffari , Nahideh Derakhshanfard , Nadir iBRAHIMOĞLU , Amir pakmehr","doi":"10.1016/j.adhoc.2025.103989","DOIUrl":"10.1016/j.adhoc.2025.103989","url":null,"abstract":"<div><div>UAVs (unmanned aerial vehicles) have become one of the most important technologies. industries, such as military, agriculture, environmental monitoring, and delivery, are based on them. The lack of a human pilot and the strong reliance on sensor data can create problems for these devices. This leads to reduced performance, greater crash risks, and potential cybersecurity threats. The process of the analysis of data to identify outliers or unusual patterns in flight data is known as anomaly detection in UAV flight data. In this review, we compare statistical methods and AI techniques for detecting anomalies in flight data from UAVs. Statistical methods like principal components analysis (PCA), regression models, and Mahalanobis distance are used to find flight anomalies. These methods are simple and efficient to use, but they have limits. They struggle with complex and non-linear patterns. AI methods like machine learning and deep learning perform better on large and complex data. They can correctly detect many types of anomalies, like point, drift, and mixed anomalies. This paper reviews past studies. It also highlights challenges and suggests future research directions in UAV anomaly detection. This study shows that using a mix of methods, hybrid learning, and better algorithms can boost the accuracy and reliability of anomaly detection systems.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103989"},"PeriodicalIF":4.8,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893551","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

Intelligent routing methods for low-Earth orbit satellite networks based on machine learning: A comprehensive survey 基于机器学习的近地轨道卫星网络智能路由方法综述

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-08-20 DOI: 10.1016/j.adhoc.2025.103995

Zhenyu Zhu , Zheheng Rao , Shitong Xiao , Ye Yao , Yanyan Xu , Weizhi Meng

{"title":"Intelligent routing methods for low-Earth orbit satellite networks based on machine learning: A comprehensive survey","authors":"Zhenyu Zhu , Zheheng Rao , Shitong Xiao , Ye Yao , Yanyan Xu , Weizhi Meng","doi":"10.1016/j.adhoc.2025.103995","DOIUrl":"10.1016/j.adhoc.2025.103995","url":null,"abstract":"<div><div>With the continuous progress of modern communication technology and the emergence of the 6G concept, people’s demand for high-quality and widely accessible data transmission is becoming increasingly intense. Low Earth Orbit (LEO) satellite networks show great attraction due to their characteristics of global coverage and low latency. Traditional terrestrial routing methods face significant challenges in adapting to LEO satellite networks due to challenges such as highly dynamic topologies, resource constraints, and insufficient multi-objective optimization capabilities. Therefore, developing routing methods suitable for LEO satellite application scenarios is crucial for further improving network transmission performance and is also one of the key technologies of future 6G. Compared with traditional algorithms, routing algorithms based on machine learning (ML) are more intelligent and begin to show obvious performance advantages, and are more suitable for 6G networks. However, in existing research work, there is a lack of comprehensive analysis content on integrating ML into LEO satellite network routing tasks. We comprehensively summarize the latest progress of intelligent routing algorithms based on ML in LEO satellite networks from four aspects: routing models, design challenges, training and deployment, and future research directions. The aim is to provide theoretical support for the design of artificial intelligence satellite communication systems and further promote the innovative development of satellite network optimization technologies.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103995"},"PeriodicalIF":4.8,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892094","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-UAV assisted cross-boundary communication scheme for AUV swarms via multi-agent reinforcement learning approach 基于多智能体强化学习的AUV群多无人机辅助跨界通信方案

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-08-18 DOI: 10.1016/j.adhoc.2025.103993

Hang Tao , Mingyue Shao , Yinyan Wang, Xinxiang Wang, Hanjiang Luo

{"title":"Multi-UAV assisted cross-boundary communication scheme for AUV swarms via multi-agent reinforcement learning approach","authors":"Hang Tao , Mingyue Shao , Yinyan Wang, Xinxiang Wang, Hanjiang Luo","doi":"10.1016/j.adhoc.2025.103993","DOIUrl":"10.1016/j.adhoc.2025.103993","url":null,"abstract":"<div><div>In maritime emergency response operations, autonomous underwater vehicles (AUVs) are frequently deployed for underwater search and marine data collection missions. However, establishing real-time water-air cross-boundary communication with AUV remains a crucial challenge. Traditional deployment of surface methods faces limitations, such as unreliable and imbalanced connections, especially when AUVs are tasked with covering large, dynamic search areas. To address the challenge, this paper proposes a novel multiple unmanned aerial vehicles (UAVs) collaboration communication scheme, in which the UAVs utilize hydrophones as mobile base stations to establish water-air cross-boundary communication with AUV swarms. First, we develop a communication coverage and energy consumption model for UAVs. Then, we introduce an AUV position prediction algorithm based on particle filter (PF), which estimates the position state information of AUVs in real time while reducing the frequency of dynamic adjustment by UAVs. Finally, we formulate the dynamic deployment of UAVs as a partially observable Markov decision process (POMDP) to optimize communication performance and energy consumption, and propose a dynamic deployment scheme based on multi-agent deep deterministic policy gradient (MADDPG) to deal with the coverage imbalance problem and provide maximum coverage service. Extensive simulations demonstrate that the proposed scheme can reduce the energy consumption by about 7.9% compared to the no-prediction scheme, effectively balancing coverage fairness and energy consumption while satisfying the communication requirements of AUV swarms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103993"},"PeriodicalIF":4.8,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144864267","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

TAGA-FLEACH: Energy-efficient and secure clustering in WSNs using trust-aware GA-FLEACH with dead-hole monitoring 基于信任感知GA-FLEACH的无线传感器网络的高效安全聚类

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-08-09 DOI: 10.1016/j.adhoc.2025.103990

Akanksha Shakya, Abhinesh Kaushik

引用次数: 0

On-demand cellular coverage using drone-mounted base stations 使用无人机安装的基站进行按需蜂窝覆盖

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-08-09 DOI: 10.1016/j.adhoc.2025.103988

Neeraj Naurange , Avirup Das , Dibakar Saha

{"title":"On-demand cellular coverage using drone-mounted base stations","authors":"Neeraj Naurange , Avirup Das , Dibakar Saha","doi":"10.1016/j.adhoc.2025.103988","DOIUrl":"10.1016/j.adhoc.2025.103988","url":null,"abstract":"<div><div>This paper proposes a novel technique for on-demand cellular coverage in areas where traditional network infrastructure has been destroyed or is unavailable. Due to the unavailability of terrestrial communication networks, drone-mounted base stations (<em>drone-BS</em>) can be essential to establish temporary communications infrastructure by placing them at higher altitudes. Using such <em>drone-BS</em> to provide cellular coverage in disaster-stricken regions or regions where the network infrastructure is unavailable, we focus on the affected users and deploy <em>drone-BS</em>s accordingly based on the data rate requirements of the affected users. We propose an efficient algorithm that finds users in the affected region and determines the minimum number of <em>drone-BS</em>s required to provide coverage for all the identified users while satisfying their data-rate requirements. The algorithm is capable of restoring cellular coverage using backup <em>drone-BS</em>s and is also robust with respect to the real-time addition of new <em>drone-BS</em>s. The performance of our proposed algorithm demonstrates its potential and efficiency in determining the number of <em>drone-BS</em>s needed to provide cellular coverage to users based on the altitude and energy consumption of the <em>drone-BS</em>. In comparison with existing work, our proposed technique can reduce approximately 20% of the extra displacement of <em>drone-BS</em> during the user discovery process. Furthermore, it minimizes the number of <em>drone-BS</em>s required to cover all the affected users.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103988"},"PeriodicalIF":4.8,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810561","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

Joint computation offloading and resource allocation in clustered MEC-enabled ultra-dense networks with multi-slope channels 多坡通道集群mec超密集网络的联合计算卸载与资源分配

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-31 DOI: 10.1016/j.adhoc.2025.103986

Tianqing Zhou , Fei Tang , Dong Qin , Xuan Li , Xuefang Nie , Chunguo Li

引用次数: 0

AoI-and-energy tradeoff scheduling for multi-UAV-enabled data acquisition in Wireless Sensor Networks 无线传感器网络中多无人机数据采集的aoi和能量权衡调度

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-30 DOI: 10.1016/j.adhoc.2025.103985

Huixiang Zhao, Yu Lu, Yi Hong, Chuanwen Luo, Xin Fan, Zhibo Chen

{"title":"AoI-and-energy tradeoff scheduling for multi-UAV-enabled data acquisition in Wireless Sensor Networks","authors":"Huixiang Zhao, Yu Lu, Yi Hong, Chuanwen Luo, Xin Fan, Zhibo Chen","doi":"10.1016/j.adhoc.2025.103985","DOIUrl":"10.1016/j.adhoc.2025.103985","url":null,"abstract":"<div><div>Leveraging their flexibility and mobility, Unmanned Aerial Vehicles (UAVs) have the potential to significantly bolster wireless communications by providing high-quality services and ensuring ubiquitous connectivity across extensive Wireless Sensor Networks (WSNs). However, the limited onboard energy of UAVs poses a considerable challenge to sustaining prolonged operational tasks. To address this issue, we introduce Mobile Unmanned Vehicles (MUVs) as mobile charging stations, tasked with providing timely energy replenishment to UAVs and thereby enabling extended data acquisition missions. This paper delves into a multi-UAV and multi-MUV-assisted data acquisition framework, with the aim of exploring and optimizing the intricate trade-off between maximizing the Age of Information (AoI) and minimizing the energy consumption of UAVs. Given the non-convex nature of the resultant multi-objective optimization problem, we propose a comprehensive joint optimization strategy that integrates sensor scheduling, resource allocation, and UAV trajectory planning. Specifically, we decompose the original problem into three manageable subproblems and solve them using a successive convex approximation (SCA) technique within an efficient iterative algorithm. Extensive simulations validate the proposed framework, underscoring its effectiveness in striking a balance between data freshness and energy efficiency. Consequently, this approach enhances the sustainability and overall performance of large-scale WSNs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103985"},"PeriodicalIF":4.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738200","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

KDN-Driven zero-shot learning for intelligent self-healing in 6G small cell networks 基于kdn驱动的6G小蜂窝网络智能自愈零射击学习

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-30 DOI: 10.1016/j.adhoc.2025.103984

Tuğçe Bilen

{"title":"KDN-Driven zero-shot learning for intelligent self-healing in 6G small cell networks","authors":"Tuğçe Bilen","doi":"10.1016/j.adhoc.2025.103984","DOIUrl":"10.1016/j.adhoc.2025.103984","url":null,"abstract":"<div><div>6G cellular networks represent a significant advancement in wireless communication by offering higher data rates, lower latency, and improved reliability. Ultra-dense small cell deployment is vital for providing enhanced capabilities in 6G, which in turn facilitates the development of data-intensive applications. Despite the benefits, dense small-cell deployments can also significantly increase anomaly rates. The complexity arising from the dynamic, dense, and data-intensive environment of 6G networks presents a significant challenge for anomaly detection and resolution. To address this issue, this paper proposes a Knowledge-Defined Networking (KDN)-enabled intelligent self-healing approach for 6G small cell networks, based on zero-shot learning. Our system continuously monitors network metrics and collects data. It utilises a semantic zero-shot learning model to detect anomalies, including new and previously unseen ones, without requiring retraining. When an anomaly is detected, the system analyses it using historical data and predefined rules to find the root cause. Once the root cause is identified, the system executes self-healing actions to resolve the issue in a closed-loop manner. The proposed system operates in an AI-native and zero-touch fashion, aligning with key 6G goals. It is evaluated in a simulation environment configured with realistic 6G parameters, including mmWave frequency (28 GHz), massive MIMO, and energy-aware small cells. The performance results underline that the proposed scheme achieves lower packet loss and reduced latency compared to conventional healing approaches. These results confirm that the architecture supports scalable, autonomous, and real-time fault management for future 6G infrastructures.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103984"},"PeriodicalIF":4.8,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144772664","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

Hierarchical classification for intrusion detection system: Effective design and empirical analysis 入侵检测系统的分层分类：有效设计与实证分析

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-29 DOI: 10.1016/j.adhoc.2025.103982

Md. Ashraf Uddin , Sunil Aryal , Mohamed Reda Bouadjenek , Muna Al-Hawawreh , Md. Alamin Talukder

{"title":"Hierarchical classification for intrusion detection system: Effective design and empirical analysis","authors":"Md. Ashraf Uddin , Sunil Aryal , Mohamed Reda Bouadjenek , Muna Al-Hawawreh , Md. Alamin Talukder","doi":"10.1016/j.adhoc.2025.103982","DOIUrl":"10.1016/j.adhoc.2025.103982","url":null,"abstract":"<div><div>The growing adoption of network technologies, particularly the Internet of Things (IoT), has led to the emergence of new and increasingly complex cyberattacks. To protect critical infrastructure from these evolving threats, it is essential to implement Intrusion Detection Systems (IDS) capable of accurately detecting a wide range of attacks while minimizing false alarms. While machine learning has been widely applied in IDS, most approaches rely on flat multi-class classification to distinguish between normal traffic and various attack types. However, cyberattacks often exhibit a hierarchical structure, where granular attack subtypes can be grouped under broader high-level categories—an aspect largely underexplored in IDS research. In this paper, we investigate the effectiveness of hierarchical classification in the context of IDS. We propose a three-level hierarchical classification model: the first level distinguishes between benign and attack traffic; the second level categorizes coarse-grained attack types; and the third level identifies specific, fine-grained attack subtypes. Our experimental evaluation, conducted using 10 different machine learning classifiers across 10 contemporary IDS datasets, reveals that hierarchical and flat classification approaches achieve comparable performance in terms of overall accuracy, precision, recall, and F1-score. However, flat classifiers are more likely to misclassify attack traffic as normal, whereas the hierarchical approach tends to misclassify one attack type as another. This distinction is critical, as failing to identify an attack altogether poses a greater risk to cybersecurity than incorrectly labeling its type. Thus, our findings highlight the value of hierarchical classification in enhancing the robustness of IDS, especially in environments where minimizing false negatives is paramount.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103982"},"PeriodicalIF":4.8,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780842","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

Mobility-aware task offloading in UAV-MEC-assisted IoV: A two-stage approach 无人机- mec辅助IoV的机动感知任务卸载：两阶段方法

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-28 DOI: 10.1016/j.adhoc.2025.103956

Kai Peng , Yuanlin Lin , Xiaolong Xu , Kunkun Yue , Victor C.M. Leung

{"title":"Mobility-aware task offloading in UAV-MEC-assisted IoV: A two-stage approach","authors":"Kai Peng , Yuanlin Lin , Xiaolong Xu , Kunkun Yue , Victor C.M. Leung","doi":"10.1016/j.adhoc.2025.103956","DOIUrl":"10.1016/j.adhoc.2025.103956","url":null,"abstract":"<div><div>With the development of 5G technology, Internet of Things technology has been integrated into vehicles, resulting in the concept of Internet of Vehicles (IoV). Nonetheless, IoV entails unique requirements and challenges. It is not wise to put all the tasks in the vehicle for computing. Fortunately, mobile edge computing (MEC) can improve the computing performance of IoV tasks by offloading some or all tasks to servers deployed at edge nodes to assist in computing. However, edge servers have limited resources, and some tasks generated by vehicles have strict processing time constraints, and the high mobility of vehicles introduces many uncertainties in task offloading. In view of the above issues, we study the problem of computation offloading for delay-sensitive applications in MEC-enabled high-mobility scenarios of IoV. Technically, we propose a two-stage mobility prediction and computation offloading method. More specifically, in the first stage, we propose a Transformer-based algorithm to predict the mobility of vehicles. Then, the predicted future positions of the vehicles are used as the input for the second stage, and computation offloading decision is made using Multi-Agent Proximal Policy Optimization algorithm. Extensive experiments are conducted to demonstrate the effectiveness and superiority of our proposed solutions in different situations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103956"},"PeriodicalIF":4.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144723151","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