Ad Hoc Networks最新文献

{"title":"A survey of trust management mechanisms in the Internet of Vehicles","authors":"Zijiang Yang,&nbsp;Qi Tao","doi":"10.1016/j.adhoc.2025.104131","DOIUrl":"10.1016/j.adhoc.2025.104131","url":null,"abstract":"<div><div>With the rapid growth of the automotive industry, the Internet of Vehicles (IoV) has become a vital platform for information exchange among vehicles and between vehicles and roadside infrastructure. However, due to complex network structures and high vehicle mobility, such exchanges are not always reliable. Trust management in IoV is therefore critical to ensuring safety and reliability. This paper reviews research on IoV trust management mechanisms. It first introduces the basic IoV models, and then identifies an evolutionary trajectory based on the existing literature: from early trust management models, to growing attention to privacy protection, and more recently to the adoption of emerging technologies such as blockchain for decentralized trust management. Based on this trajectory, the paper analyzes existing work from three perspectives: trust management models, privacy protection, and blockchain-IoV integration. Furthermore, this article systematically surveys experimental simulation platforms and evaluation indicators to clarify validation practices. Finally, by synthesizing the research landscape and highlighting key limitations and bottlenecks, the paper outlines future directions and priorities for IoV trust management in light of both technological advances and application needs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104131"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884345","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}

{"title":"Enhancing energy efficiency and QoS in Q-learning-based small-world WSNs","authors":"Yan Zhao,&nbsp;Lintao Huang,&nbsp;Mengzhe Ren,&nbsp;Hanyang Shi","doi":"10.1016/j.adhoc.2025.104123","DOIUrl":"10.1016/j.adhoc.2025.104123","url":null,"abstract":"<div><div>In a wireless sensor network (WSN), achieving efficient data transmission while extending network lifetime remains a critical issue. Sensor nodes may fail due to energy depletion during mass data transmission, which results in frequent changes in the network topology. The frequent topology changes not only increase the complexity of network management and maintenance, but also severely impair both energy efficiency and quality-of-service (QoS). To address these challenges, in this paper, we construct a small-world WSN (SW-WSN) and address its joint optimization problem of energy efficiency and QoS. In the constructed SW-WSN, long-range (LoRa) links are used to replace some of the conventional links between sensor nodes as well as between nodes and the gateway, and a small-world model is introduced to reduce the intermediate hop count for data transmission. A Q-learning-based adaptive link allocation algorithm is proposed to map all conventional and LoRa links to state space and action space respectively and learn to form state–action pairs through training, which determines an optimal link replacement strategy under different network states and ultimately constructs a self-optimizing network architecture SW-WSN. Then, considering the network topology, link capacity, delay tolerance, and node energy of SW-WSN, the joint optimization problem of energy efficiency and QoS is formulated as an instance of linear programming (LP) with the objective of maximizing energy efficiency while ensuring QoS, and a heuristic algorithm is further designed to obtain the optimal solution. As shown by the simulation results, the Q-learning-based SW-WSN exhibits excellent learning capability and convergence stability across different network scales, successfully achieving a relatively ideal balance between energy efficiency and QoS. The Q-learning-based SW-WSN demonstrates substantial improvements in both energy efficiency (alive or dead devices, and network residual energy) and QoS (average transmission delay, data throughput, and bandwidth utilization) compared with the reinforcement learning (RL)-based routing, low-energy adaptive clustering hierarchy (LEACH), conventional small-world characteristics (SWC), multihop data transmission, and direct data transmission methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104123"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790979","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}

{"title":"NL-MHP: Efficient and robust network localization algorithm in complex scenarios using maximum hop progress","authors":"Zhihao Dong ,&nbsp;Xiaoyong Yan ,&nbsp;Jian Zhou","doi":"10.1016/j.adhoc.2025.104110","DOIUrl":"10.1016/j.adhoc.2025.104110","url":null,"abstract":"<div><div>Accurate node localization is critical for wireless network applications, yet existing algorithms struggle with low communication efficiency, inaccurate distance measurement, and unstable localization in irregular multi-hop networks. To address these challenges, this paper proposes an Efficient and Robust <u>N</u>etwork <u>L</u>ocalization algorithm using <u>M</u>aximum <u>H</u>op <u>P</u>rogress (NL-MHP). NL-MHP integrates routing exploration and distance estimation into a single phase, significantly reducing communication overhead. It further introduces an adaptive hop count threshold to filter out erroneous distances and constructs constrained subregions for each unlocated node to ensure localization stability. The Coati optimization algorithm with Sobol sequence is then employed within these subregions to determine the optimal node location. Comprehensive simulations demonstrate that NL-MHP outperforms existing methods in efficiency, accuracy, and stability for irregular networks. Quantitatively, it improves distance estimation accuracy by 26.7% to 34.8% and localization accuracy by 31.4% to 51.7% compared to three state-of-the-art algorithms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104110"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694343","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}

{"title":"Towards byzantine fault-tolerant non-interactive zero knowledge cross-domain batch authentication in VANET","authors":"Shaolin Qiu ,&nbsp;Shuchao Deng ,&nbsp;Honglei Pang","doi":"10.1016/j.adhoc.2025.104120","DOIUrl":"10.1016/j.adhoc.2025.104120","url":null,"abstract":"<div><div>Vehicle Ad Hoc Network (VANET) is becoming an essential vehicle communication network paradigm. However, the safe and reliable operation of VANETs faces serious challenges, especially in how users authenticate to the network. Idealized access control rules for trusted network entities are highly susceptible to the leakage of critical private data from inbound users. In addition, the open network environment puts the security of networked entities at significant risk, and the presence of Byzantine devices severely hampers legitimate vehicles from requesting network access services. To address the above problems, this paper proposes an efficient vehicle cross-domain identity batch authentication framework that is functional, privacy-preserving, and resistant to Byzantine attacks. The scheme uses non-interactive zero-knowledge proofs to achieve anonymous vehicle authentication. We integrate an improved Practical Byzantine Fault Tolerance (PBFT) protocol to enhance the robustness of zero-knowledge authentication. It achieves efficient consensus for inbound vehicle authentication in cross-domain scenarios, even in the presence of a small number of Byzantine entities. The scheme is further integrated into cross-domain authentication frameworks to support seamless inter-domain access. Under dynamic VANET topologies, we derive consensus probability and authentication accuracy; simulations confirm high accuracy with low communication overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104120"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694342","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}

{"title":"Protecting autonomous systems from GPS spoofing with a machine learning-driven approach","authors":"Arslan Shafique ,&nbsp;Abid Mehmood ,&nbsp;Moatsum Alawida ,&nbsp;Shehzad Ashraf Chaudhry","doi":"10.1016/j.adhoc.2025.104101","DOIUrl":"10.1016/j.adhoc.2025.104101","url":null,"abstract":"<div><div>With the rapid evolution of interactive multimedia systems, ensuring strong security measures has become increasingly vital. Autonomous platforms, such as drones, are vulnerable to sophisticated cyber threats, including jamming and spoofing attacks. One common spoofing strategy involves manipulating Global Positioning System (GPS) signals. By broadcasting counterfeit signals, attackers can deceive drone navigation systems. To mitigate these risks, this research introduces a machine learning-based framework aimed at intelligently detecting spoofing attempts. The approach employs signal characteristics that reflect variations in jitter, shimmer, and frequency modulations, rooted in mathematical analysis. A private dataset is used for this work. This dataset, developed by our research team, is collected under varied environmental conditions, such as during daylight and in low-light settings, over multiple sessions. It categorizes signal statistics into three distinct ranges: the initial and final segments suggest spoofed signals, whereas the middle range corresponds to genuine ones. Further, using signal reception strength (SRS) values, additional data was sourced from trusted Long Range Wide Area Network (LoRaWAN) devices. This information supports the development of a singular-class support vector machine (S-CSVM) classifier for spoofing detection. For training purposes, the dataset was partitioned into two subsets: a training set (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>t</mi><mi>r</mi><mi>a</mi><mi>i</mi><mi>n</mi></mrow></msub></math></span>) and a testing set (<span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>t</mi><mi>e</mi><mi>s</mi><mi>t</mi></mrow></msub></math></span>). The performance of the model is evaluated using standard metrics, including precision, recall, F-score, and overall accuracy. By utilizing all available features, the model achieves its highest scores: 99.99% precision, 99.77% recall, and 99.95% F-score. The highest accuracy of 99.22% is achieved when all features are selected, and the distance between LoRaWAN devices and the monitoring device ranges from 6 to 8 m, outperforming the results obtained through feature selection in the ablation study. A thorough evaluation further highlights how the proposed ML-based solution outperforms existing methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104101"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651805","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}

{"title":"A deep learning-based approach for heterogeneous hotspot-coverage in UAV deployment","authors":"Kolichala Rajashekar ,&nbsp;Vamsi Krishna Sunkara ,&nbsp;Subhajit Sidhanta","doi":"10.1016/j.adhoc.2025.104127","DOIUrl":"10.1016/j.adhoc.2025.104127","url":null,"abstract":"<div><div>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 <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> 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.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104127"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884185","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}

{"title":"Reliability analysis of multi-state wireless sensor networks with functional dependency based on dynamic Bayesian networks","authors":"Haozhe Liu ,&nbsp;Jinfang Zhao ,&nbsp;Qun Zhao ,&nbsp;Hongliang Sun","doi":"10.1016/j.adhoc.2025.104125","DOIUrl":"10.1016/j.adhoc.2025.104125","url":null,"abstract":"<div><div>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 <em>k</em>-out-of-<em>n</em> 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 <em>α</em>-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.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104125"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884344","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}

{"title":"Adaptive scale-free topology optimization using deep reinforcement learning in UASNs","authors":"Haoran Cheng,&nbsp;Xiangyu Bai,&nbsp;Xiaoying Yang","doi":"10.1016/j.adhoc.2025.104121","DOIUrl":"10.1016/j.adhoc.2025.104121","url":null,"abstract":"<div><div>In underwater acoustic sensor networks (UASNs), topology optimization strategies are effective ways to enhance network robustness, reduce transmission delay, and address issues such as high bit error rates and limited energy. However, current topology optimization strategies exhibit limitations in multifactorial consideration and dynamic adaptability, resulting in low energy efficiency and compromised robustness in UASNs. To address these issues, this paper proposes an adaptive scale-free topology optimization algorithm based on deep reinforcement learning (SFTG-DRL), aiming to ensure the network’s lifetime, reduce delay, and enhance the network’s fault tolerance. First, we optimize the transmission power of each sensor node to obtain its optimal power level, while proposing an improved preferential attachment model that incorporates node energy and depth information to achieve scale-free network characteristics. Then, deep reinforcement learning is applied to constrain minimum connections, further refining the topology for improved dynamic adaptability. Finally, extensive simulation experiments are conducted to validate the performance of the proposed algorithm, assessing aspects such as exploration strategy, node degree distribution, fault tolerance, network lifetime, and end-to-end delay.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104121"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791117","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}

{"title":"Optimizing task allocation in Mobile Crowdsensing with multiple opportunistic users and participatory UAVs","authors":"Shi Yu,&nbsp;Bing Shi,&nbsp;Xiao Su,&nbsp;Saisai Li,&nbsp;Shuai Li,&nbsp;Xing Tang","doi":"10.1016/j.adhoc.2025.104128","DOIUrl":"10.1016/j.adhoc.2025.104128","url":null,"abstract":"<div><div>Mobile Crowdsensing (MCS) refers to the use of human users or unmanned aerial vehicles (UAVs) equipped with mobile devices to collect sensing data. However, existing MCS methods suffer from insufficient coverage due to sparse population distribution in some sensing areas and restrictions on UAV flights in densely populated urban regions. To address this issue, we propose a collaborative approach that combines opportunistic users, who are passively engaged and widely distributed across cities, with participatory UAVs, which are capable of covering sparsely populated regions. Specifically, we introduce a hybrid task allocation strategy called MUMUHTA (Multi-User Multi-UAV Hybrid Task Allocation) to optimize sensing coverage. The strategy assumes that grid heat is derived from historical user trajectory data, and UAVs are assigned only tasks without autonomous control. MUMUHTA uses user trajectory prediction and greedy recruitment for opportunistic users, along with per-slot UAV matching based on the Kuhn–Munkres algorithm, while a dynamic switching rule determines when UAVs take over tasks from users. Simulation experiments using the Rome user trajectory dataset and Shanghai Telecom task dataset show that MUMUHTA improves the task completion rate by an average of 29.70%, 8.11%, 9.69%, 5.05%, and 15.38% compared to benchmark strategies: MPU, MOU-Random, DLMV-MPU, DLMV(T)-MPU, and HR-DLVCS.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104128"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884181","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}

{"title":"MUFO: Multi-UAV flight optimization for enhancing connectivity in remote driving services","authors":"Van-Linh Nguyen ,&nbsp;Lan-Huong Nguyen ,&nbsp;Ren-Hung Hwang","doi":"10.1016/j.adhoc.2025.104129","DOIUrl":"10.1016/j.adhoc.2025.104129","url":null,"abstract":"<div><div>Besides autonomous driving, remote driving is a typical example of leveraging communications to eliminate high-risk driving situations in which drivers are fatigued. However, remote driving remains challenging, particularly in urban areas where buildings’ absorption and reflection may significantly hamper control signals. This paper proposes MUFO, a deep-reinforcement-learning-based multi-UAV flight-optimization framework whose objectives are twofold: (i) path planning to determine optimal UAV trajectories that sustain stable links for remote-driving vehicles, and (ii) efficient deployment to minimize the number of UAVs and their energy consumption while guaranteeing service continuity with minimum data rate. First, the coverage and flight cost issues are defined in a multi-objective optimization problem with constraints on UAV energy and collision avoidance. Based on a built-in map of weak signal areas, a novel technique is proposed: a multi-agent deep deterministic policy gradient (MADDPG) scheme. The goal is to determine the best flying strategy for the UAVs to fly over weak signal areas, enhance signal strengths, and relay connectivity when the remote vehicles arrive there. The simulation results show that MADDPG in MUFO outperforms state-of-the-art deep learning methods and searches by up to 8% of deployment efficiency (energy savings, number of deployed UAVs), particularly when there is a high density of ground traffic jam areas and UAVs are required to hover at those areas for an unexpected additional time. MUFO’s strength is that it considerably improves the deployment efficiency of UAVs via cumulative learning from many trials or completed missions.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"183 ","pages":"Article 104129"},"PeriodicalIF":4.8,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884182","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}