Ad Hoc Networks最新文献

{"title":"Routing algorithm for LWSN in natural disaster environmental monitoring along railway lines","authors":"Decang Li ,&nbsp;Zhaoji Niu ,&nbsp;Zixian Qu ,&nbsp;Xiaoqiang Chen ,&nbsp;Ruxun Xu","doi":"10.1016/j.adhoc.2025.104060","DOIUrl":"10.1016/j.adhoc.2025.104060","url":null,"abstract":"<div><div>Aiming at the “energy hole” problem caused by high energy consumption of linear wireless sensor networks in the environmental detection of natural disasters along high-speed railways, a routing algorithm based on weighted least squares estimation method and particle swarm optimization theory is proposed. Firstly, a node residual energy estimation model based on adaptive unscented Kalman filter estimation is established according to the historical data of node residual energy. The parameters are estimated by dynamically adjusting the weights of the observation data, and the weighted residual sum of squares is minimized to handle data heterogeneity and adapt to the dynamic changes of node residual energy. At the same time, the observation values and design matrices are continuously updated to estimate the node residual energy in real time and accurately, optimize network resource management, and extend the network lifetime. Secondly, the fitness function is constructed by defining the node residual energy estimation factor and the cluster compactness evaluation factor. The initial position distribution of the particle swarm is improved by using the Logistic-Tent chaotic map to expand the local search range and accelerate the convergence speed of global search. The simulated annealing operation is introduced, and the Metropolis criterion of the Golden Sine Simulated Annealing (GSSA) algorithm is used to guide the population to accept new solutions or old solutions with a certain probability, thereby ensuring that the algorithm can continuously perform global optimization, reducing the risk of premature convergence and falling into local optimal solutions, and obtaining the optimal solution set of cluster heads. Finally, the data transmission of cluster head nodes is based on the Dijkstra algorithm to obtain the optimal main path from the source node to the Sink node. Simulation test results show that compared with the LEACH algorithm, IACO algorithm, and GACR algorithm, the network lifetime of the proposed method is extended by 97.62%, 40.69%, and 18.61%, respectively. The research results provide a reference basis for further optimizing the wireless sensor network for high-speed railway environmental detection and ensuring the safe operation of high-speed railways.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104060"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419846","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":"LSTMO-MADDPG: A system for intelligent computation offloading in UAV-enabled MEC in next generation networks","authors":"Amita Chauhan,&nbsp;Rijul Tandon,&nbsp;Sakshi Kaushal,&nbsp;Harish Kumar","doi":"10.1016/j.adhoc.2025.104061","DOIUrl":"10.1016/j.adhoc.2025.104061","url":null,"abstract":"<div><div>The advent of next generation networks designated as 5G and beyond signifies a revolution in wireless communications technology. They are intended to overcome the shortcomings of earlier generations with extraordinary degrees of speed, connectivity, and dependability. The integration of Unmanned Aerial Vehicles (UAVs) into new-generation communication networks results in numerous advantages, advancing the capabilities, coverage, and performance of networks. The incorporation of UAVs into Mobile Edge Computing (MEC) frameworks adds another dimension of flexibility and adaptability. A primary obstacle in UAV-supported MEC systems involves efficiently offloading computational tasks from mobile devices to nearby UAVs serving as edge servers. This paper introduces a new method for efficient computation offloading in Multi-UAV-Enabled MEC systems based on decision-making abilities of hierarchical Deep Deterministic Policy Gradient (DDPG) and forecasting abilities of Long Short-Term Memory (LSTM) networks. The proposed method is called LSTM Optimized Multi-Agent DDPG (LSTMO-MADDPG), and includes three modules, i.e., High-Level DDPG, LSTM Refiner and Low-Level DDPG. The technique is intended to improve the efficiency and reliability of computation offloading which is pivotal for next-generation networks. A large number of simulations prove considerable improvements in terms of latency minimization, energy saving, and throughput relative to several existing approaches. The implementation is publicly available on <span><span>Github</span><svg><path></path></svg></span>, ensuring reproducibility. This approach advances efficient task offloading, enhancing system performance in next-generation networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104061"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145419842","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":"Advancing intelligent transportation through digital twin: Challenges, models, and future prospects","authors":"Ling Xing ,&nbsp;Bing Li ,&nbsp;Kaikai Deng ,&nbsp;Jianping Gao ,&nbsp;Honghai Wu ,&nbsp;Huahong Ma ,&nbsp;Xiaohui Zhang","doi":"10.1016/j.adhoc.2025.104077","DOIUrl":"10.1016/j.adhoc.2025.104077","url":null,"abstract":"<div><div>As a transformative technology in next-generation digital innovation, digital twin is instrumental in enhancing the efficiency and safety of traffic management while driving advancements in intelligent transportation systems. To this end, we systematically examine the primary challenges associated with the implementation of digital twin technology in transportation and introduce a novel 5 + 2 digital twin model tailored for this domain. Additionally, a comprehensive five-layer architecture is proposed to support the development of digital twin transportation. We further investigate the potential applications of digital twins in critical areas, including traffic flow management, intelligent transportation planning, resource allocation of Internet of Vehicles, traffic safety control, and assisted autonomous driving. Alongside these discussions, we provide an in-depth analysis of the current technological limitations and identify key areas requiring further research. Finally, we explore future development trajectories and the broader impact of digital twin transportation, offering new perspectives and insights to propel the evolution of intelligent transportation systems.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"181 ","pages":"Article 104077"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468464","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 user fairness in UAV-assisted RSMA networks : A proximal policy optimization approach","authors":"Donghyeon Hur ,&nbsp;Donghyun Lee ,&nbsp;Manh Cuong Ho ,&nbsp;Wonjong Noh ,&nbsp;Sungrae Cho","doi":"10.1016/j.adhoc.2025.104041","DOIUrl":"10.1016/j.adhoc.2025.104041","url":null,"abstract":"<div><div>Rate-splitting multiple access (RSMA) and unmanned aerial vehicles (UAVs) are emerging as key technologies for enhancing connectivity and resource efficiency in future 6G networks. This study investigated a UAV-assisted RSMA downlink communication system and developed a novel framework that jointly optimizes the UAV’s trajectory, precoding matrix, and common rate to maximize the minimum achievable user rate. The proposed system was formulated as a Markov decision process (MDP) and solved using deep reinforcement learning (DRL), specifically using the proximal policy optimization (PPO) algorithm. This learning-based approach enables UAVs to adapt dynamically to the environment without relying on prior channel state information (CSI), allowing for efficient resource allocation and interference management in complex and dynamic wireless scenarios. Furthermore, a precoding design based on a uniform rectangular array (URA) was employed to enhance directional transmission and spatial multiplexing. In simulations, the proposed method significantly outperformed existing benchmarks, achieving an average minimum rate improvement of 23.31% over Deep Deterministic Policy Gradient (DDPG), 48.59% over Soft Actor–Critic (SAC), 50.87% over Trust Region Policy Optimization (TRPO), 63.36% over REINFORCE algorithm, 79.13% over Greedy algorithm, and 145.67% over Random strategies, respectively. These results confirm the potential of UAV-aided RSMA networks in next-generation wireless environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104041"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269958","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":"Personalized trajectory privacy protection method based on Transformer-CGAN in mobile crowd sensing networks","authors":"Xinya Yu,&nbsp;Qi Xu,&nbsp;Jianpo Li,&nbsp;Yunfeng Peng","doi":"10.1016/j.adhoc.2025.104050","DOIUrl":"10.1016/j.adhoc.2025.104050","url":null,"abstract":"<div><div>Trajectory privacy is a critical issue in mobile crowd sensing (MCS) network, but existing methods ignore location sensitivity differences, making it difficult to achieve personalized protection. To this end, this paper proposes a personalized trajectory synthesis method (PTS-TCGAN) that fuses Transformer and conditional generative adversarial networks (CGAN). Firstly, the trajectory point sensitivity is evaluated by spatiotemporal features. Secondly, a sensitivity-based Transformer-CGAN model is introduced, which uses Transformer to capture spatiotemporal dependencies and generates high-quality synthetic trajectories through CGAN, which enhances trajectory privacy protection while improving the model’s utility. Meanwhile, the multi-task TrajLoss function is introduced to improve the accuracy of the synthetic trajectories. Finally, experiments on Foursquare NYC, T-Drive, and Geolife datasets show that the proposed method improves privacy preservation by at least 38% and utility by at least 10% compared to existing TrajGAN and LSTM-TrajGAN methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104050"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321999","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":"Exploiting multiple links with a single interface: A performance study","authors":"Stefano Avallone,&nbsp;Pasquale Imputato,&nbsp;Ferdinando Marrone","doi":"10.1016/j.adhoc.2025.104029","DOIUrl":"10.1016/j.adhoc.2025.104029","url":null,"abstract":"<div><div>The IEEE 802.11 working group is in the final stages of completing the 802.11be amendment, which outlines the features to be supported by Wi-Fi 7 devices. One of the key innovations introduced is Multi-Link Operation (MLO), which enables a device to function across multiple frequency channels simultaneously. Among the various MLO configurations, Enhanced Multi-Link Single Radio (EMLSR) has garnered significant attention from vendors due to its ability to leverage multiple links with reduced hardware complexity.</div><div>This paper begins by presenting an overview of the EMLSR specifications as defined in the standard and outlines the implementation model used in the ns-3 simulator, one of the most widely used simulators for wired and wireless networks. The performance of EMLSR operations has not been analyzed in depth so far. To the best of our knowledge, this paper is the first one to offer a theoretical analysis along with a simulation-based evaluation to assess the performance of an EMLSR client. Under saturated network conditions without external interference, the EMLSR client demonstrates throughput performance comparable to – or even exceeding – that of a traditional Single-Link Device (SLD), despite the overhead introduced by MLO functionality. In scenarios with unsaturated traffic and interference, the EMLSR client not only achieves lower latency than an SLD but also matches or outperforms a Multi-Link Device (MLD) with more advanced hardware that operates across multiple links simultaneously.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104029"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145134835","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 patient-centric secure access control architecture with dynamic edge data integrity verification in Internet of Medical Things","authors":"Keerat Kaur,&nbsp;Saifur Rahman,&nbsp;Shantanu Pal,&nbsp;Chandan Karmakar","doi":"10.1016/j.adhoc.2025.104055","DOIUrl":"10.1016/j.adhoc.2025.104055","url":null,"abstract":"<div><div>In the Internet of Medical Things (IoMT), securing patient data access is critical, but must be achieved without overwhelming the limited computational resources of edge devices. While cryptographic methods and access policies are widely applied to secure medical data, existing solutions often assume high computational capacity, centralized infrastructure, or predefined key structures, which are not ideal for the heterogeneous and resource-constrained environments found in IoMT. In addition to security, patient-centricity is becoming an essential design principle, where patients must have control over who accesses their data and under what conditions. Similarly, edge computing has emerged as a means to reduce latency, but edge devices are often semi-trusted, exposed to physical threats, and limited in processing power, making them unsuitable for heavyweight integrity verification or outsourced computation. Therefore, this paper presents a lightweight, patient-centric architecture that unifies attribute-based access control, dynamic edge data integrity verification, and consent-driven sharing in a secure and scalable architecture. The system minimizes communication and computational overhead by eliminating predefined keys, restricting edge computation, and guaranteeing verifiable data delivery. The experimental results demonstrate efficient handling of tampered and untampered cases, achieving fast, verifiable, and secure access with minimal resource consumption. This paper offers a practical and integrative solution to ensure security without sacrificing lightweight performance in real-world IoMT deployments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104055"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145364134","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":"OCUS: A game-theoretic approach to optimal UAV coalitions in UAV-as-a-Service platforms","authors":"Prince Kumar ,&nbsp;Akhand Pratap Narayan Singh ,&nbsp;Anchal Dubey ,&nbsp;Farid Nait-Abdesselam ,&nbsp;Arijit Roy","doi":"10.1016/j.adhoc.2025.104030","DOIUrl":"10.1016/j.adhoc.2025.104030","url":null,"abstract":"<div><div>This paper presents OCUS, an optimal coalition formation scheme designed to enhance the performance of UAV-as-a-Service (UaaS) platforms for Internet of Things (IoT) applications. UaaS platforms typically involve a set of heterogeneous UAVs owned by different entities. Serving IoT applications such as agriculture, traffic monitoring, and surveillance often requires multiple UAVs with diverse sensing, processing, and communication capabilities. Thus, forming an optimal coalition of these UAVs is essential for efficient resource utilization and application performance. Despite the growing interest in UAV-based services, existing literature lacks coalition formation schemes tailored for UaaS platform. To address this gap, OCUS leverages a coalition game-theoretic approach, where each UAV acts as a player in the game. The scheme introduces a selection score, which evaluates UAV owners based on reputation, cost competitiveness, and task allocation fairness. Using a payoff-driven method, OCUS promotes coalition stability and maximizes utility for UaaS platforms. The payoff function considers utility, reputation, task allocation fairness, costs, and equilibrium constraints. By employing Nash Equilibrium (NE) principles, OCUS ensures stable coalitions, discouraging UAVs from switching coalitions for higher payoffs. OCUS optimizes coalition formation by maximizing task completion rates, minimizing energy consumption, and ensuring fair and stable UAV participation through game-theoretic modeling. To evaluate OCUS, simulation experiments were conducted over 100 iterations involving 1 to 25 heterogeneous UAVs across a 50 × 50 unit² area, with varying distances and task counts (<span><math><mrow><mn>1</mn><mo>−</mo><mn>15</mn></mrow></math></span>). Simulation results demonstrate that OCUS achieves 25%–30% more stable coalitions compared to state-of-the-art approaches. Furthermore, NE principles enable OCUS to form coalitions that balance efficiency and fairness, advancing the capabilities of UaaS platforms in diverse IoT scenarios. OCUS offers promising applicability in real-world domains such as urban infrastructure management and emergency response, where adaptive and coordinated UAV operations can significantly enhance the delivery of services.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104030"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160000","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":"VPrWS: Vision transformer-based prototypical network with cross-inductive bias distillation for cross-domain wireless human sensing","authors":"Phuoc Nguyen T.H. ,&nbsp;Minh Tuan Pham","doi":"10.1016/j.adhoc.2025.104049","DOIUrl":"10.1016/j.adhoc.2025.104049","url":null,"abstract":"<div><div>Wireless-based human sensing (WHS) is a powerful technique for device-free activity recognition. It analyzes channel state information (CSI) extracted from wireless signals. However, domain shift remains a major challenge, as performance degrades significantly when models are applied across users, environments, or locations. Few-shot learning (FSL) offers a practical solution by enabling rapid adaptation using limited labeled samples. Among FSL approaches, Prototypical Networks (PNs) are favored for their simplicity and effectiveness in low-data regimes. However, due to the heterogeneous nature of CSI data, constructing a robust and semantically meaningful prototype metric space remains challenging.</div><div>To address this issue, we propose VPrWS, a system that integrates a prototypical network (PN) with a vision transformer (ViT) and employs cross-inductive-bias knowledge distillation to enrich the prototype space. Specifically, we develop a knowledge distillation framework for PNs that induces complementary inductive biases from bidirectional long short-term memory (BiLSTM)- and convolutional neural network (CNN)-based PNs to a ViT-based PN. This approach yields a more robust and semantically structured metric space, especially in the context of CSI data, thereby improving cross-domain WHS performance without materially increasing inference time. Evaluations on three public CSI datasets under both in-domain and cross-domain settings demonstrate that VPrWS consistently outperforms existing representative baselines.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104049"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321996","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":"IG-APSO-DNN: Deep learning intrusion detection model to detect false data injection attacks in smart grids","authors":"Saad Hammood Mohammed ,&nbsp;Mandeep S. Jit Singh ,&nbsp;Abdulmajeed Al-Jumaily ,&nbsp;Mohammad Tariqul Islam ,&nbsp;Md. Shabiul Islam ,&nbsp;Abdulmajeed M. Alenezi ,&nbsp;Mohamad A. Alawad ,&nbsp;Muaadh A. Alsoufi","doi":"10.1016/j.adhoc.2025.104053","DOIUrl":"10.1016/j.adhoc.2025.104053","url":null,"abstract":"<div><div>False Data Injection Attacks (FDIAs) present a significant threat to smart grids by manipulating measurement data, which may lead control centers to make incorrect operational decisions. Accurate and efficient detection of FDIAs is critical for ensuring reliable grid operation. Existing deep learning approaches often fail to capture both short-term local features and long-term dependencies in power grid data, and they typically show weak correlations with past and future time series information, reducing the trustworthiness of detection results. Similarly, conventional Intrusion Detection Systems (IDS) struggle to detect advanced FDIAs due to their reliance on predefined signatures and rule-based mechanisms. To overcome these limitations, we propose IG-APSO-DNN, a two-stage deep learning model for detecting FDIAs in smart grids. The first stage employs Information Gain (IG) and Adaptive Particle Swarm Optimization (APSO) for feature selection, reducing data dimensionality and improving model efficiency. The second stage uses a Deep Neural Network (DNN) to effectively capture both spatial and temporal patterns in smart grid measurements. The proposed model is evaluated on the Industrial Control System (ICS) Cyber Attack Power System Dataset, which simulates various FDIA scenarios. Results demonstrate that IG-APSO-DNN significantly outperforms traditional methods, improving key performance metrics including detection accuracy, precision, recall, and F-measure, while ensuring reliable operation of the smart grid. This study presents a robust anomaly-based IDS framework and highlights future directions, such as real-world validation, adaptive learning, exploration of novel optimization algorithms, and addressing scalability and real-time processing challenges.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"180 ","pages":"Article 104053"},"PeriodicalIF":4.8,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145322056","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}