Ad Hoc Networks最新文献

{"title":"TSIG: A geomagnetic indoor positioning method leveraging temporal and spatial domain features","authors":"Ao Liu,&nbsp;Wenguang Wang","doi":"10.1016/j.adhoc.2025.104023","DOIUrl":"10.1016/j.adhoc.2025.104023","url":null,"abstract":"<div><div>Numerous methods for geomagnetic positioning, using traditional or deep learning techniques, have made significant strides in indoor positioning. Nevertheless, geomagnetic mismatching still leads to considerable errors in positioning accuracy. To address this problem, we explore geomagnetic positioning in both the time and spatial domains concurrently. We propose a novel method named TSIG. In the temporal domain, we propose a multi-scale attention module with geomagnetic hierarchical embedding, named TIG. We first extract the temporal dependencies of geomagnetic subsequences at certain time scales from both local and global perspectives. Then, we use the extracted information to capture anomalies at different time scales and determine each scale’s contribution to the importance of features. In the spatial domain, we incorporate geomagnetic signal characteristics and propose a geomagnetic anomaly focusing and direction perception-driven feature extraction module named SIG. We reconstruct the geomagnetic sequences into images and further extract the spatial features using anomaly focusing and direction perception, thereby enhancing the representational capacity of the spatial features. Finally, the temporal and spatial domain features are input into the spatiotemporal feature fusion layer to achieve cross-domain feature fusion, generating the positioning result. The experimental results show that the proposed TSIG method can achieve accurate positioning.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104023"},"PeriodicalIF":4.8,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097013","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":"Energy efficient sleep mode strategies for communication and computing devices in cellular networks with edge computing","authors":"Jinsong Gui ,&nbsp;Xuan Zhang","doi":"10.1016/j.adhoc.2025.104027","DOIUrl":"10.1016/j.adhoc.2025.104027","url":null,"abstract":"<div><div>Over the years, to minimize the energy consumption of ultra-dense networks (UDNs), numerous sleep strategies for base stations (BSs) have been proposed. However, more fine-grained sleep patterns remain underexplored. Additionally, the integration of edge computing into UDNs introduces further complexity to the energy consumption minimizing problem. This paper aims to propose a multi-agent deep reinforcement learning (DRL)-based framework to significantly reduce energy consumption in cellular networks with edge computing. By leveraging the gate recurrent unit (GRU) to predict unobserved local state information for each agent, including channel gain values of outgoing and incoming links, minimum transmission rate requirements for user response messages and task offloading packets, as well as minimum computing power requirements for offloaded tasks, the proposed scheme enables more precise control over the sleep modes of both radio frequency (RF) transceivers of BSs and edge servers. Consequently, this scheme achieves a substantial reduction in cumulative energy consumption. Extensive simulations confirm that the proposed scheme not only reduces communication energy but also decreases the computing energy required by edge servers.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104027"},"PeriodicalIF":4.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097014","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":"Quantify the joint effect of mobility and urban environment on computation offloading to multi-UAV MEC network: Sojourn time","authors":"Basheer Ameen Raddwan ,&nbsp;Ibrahim Ahmed Al-Baltah","doi":"10.1016/j.adhoc.2025.104019","DOIUrl":"10.1016/j.adhoc.2025.104019","url":null,"abstract":"<div><div>The integration of Multi-Access or Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers transformative prospects for 5G/6G networks, especially with compute offloading in urban settings. The combined effect of the mobility of airborne MECs (airMECs) and urban environmental dynamics has not been explored in current research, which has shown that the optimization strategy may be impractical in densely populated areas due to the restricted computational resources available on airMECs. In this paper, we analyze the combined mobility of users and airMECs while considering urban dynamics constraints to quantify sojourn time, which serves as a valuable input for mobility characterization in planning. We introduce novel analytical and statistical approaches to quantify sojourn time for both omnidirectional and directional antenna scenarios. We develop the statistical approach by simulating three-dimensional mobility in numerous urban configurations and gather ray-tracing line-of-sight data for two scenarios. The first scenario excludes buildings to verify the statistical approach against the analytical one, whereas the second scenario includes buildings to assess their influence. Following the quantification of the suggested environment-dependent sojourn time, we present a stochastic task size quantification as an application example. Additionally, we present many evaluations to ensure the accuracy and the practicality of the proposed models. For example, we analyze the circular directionality of sojourn time using the Von Mises probability distribution, examining task sizes with offloading time, and assessing handover times for a designated task size. The findings indicate that environmental dynamics significantly influence sojourn time and computational offloading, necessitating explicit consideration in airMEC network planning and MEC application design. This study offers critical insights for developing resilient, adaptive networks that support computation-intensive applications in dynamic urban environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104019"},"PeriodicalIF":4.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050133","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":"Topology-aware model for collaborative time-slot allocation in maritime ad hoc networks","authors":"Meng’en Song,&nbsp;Di Zhang,&nbsp;Qing Hu","doi":"10.1016/j.adhoc.2025.104021","DOIUrl":"10.1016/j.adhoc.2025.104021","url":null,"abstract":"<div><div>The strongly time-varying topology of time-division multiple access (TDMA)-based maritime ad hoc networks (MANETs) leads to their low robustness and poor link stability, resulting in a high time-slot collision rate and poor communication-resource utilization. To address these problems, based on the ant colony algorithm (ACA), this research proposes a collaborative time-slot resource optimization allocation strategy for ships. The proposed strategy involves first constructing a topology-aware model, then updating the involved network topology in real time and identifying the network’s center of gravity based on dynamic clustering coefficients, and finally utilizing the pheromone matrix of the ACA for time-slot allocation, realizing optimal dynamic scheduling. Simulation results based on actual ship automatic identification system (AIS) data show that compared to RATDMA, SOTDMA, and BSO-TDMA, the proposed strategy achieves significantly better performance; it exhibits a 39% lower time-slot occupancy rate, an 87% lower collision rate, and a &gt;95% higher network throughput compared to those obtained using RATDMA. Overall, our strategy achieves time-slot collision minimization and performance reliability enhancement for ensuring secure communication in highly dynamic MANETs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104021"},"PeriodicalIF":4.8,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096993","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":"Maximizing quality of UAV-enabled surveillance over multiple restricted regions","authors":"Qile He,&nbsp;Riheng Jia,&nbsp;Minglu Li","doi":"10.1016/j.adhoc.2025.104010","DOIUrl":"10.1016/j.adhoc.2025.104010","url":null,"abstract":"<div><div>In this work, we study the problem of maximizing the unmanned aerial vehicle (UAV) surveillance quality for multiple terrestrial targets and each target is located within a restricted region where the UAV cannot enter. We propose an angle-based surveillance quality metric to quantify the surveillance effectiveness of the UAV under the no-fly zone constraint. To maximize surveillance quality within the mission completion time constraint, our two-phase approach consists of: (1) determining the visiting order of targets through a minimum spanning tree-based traveling salesman problem (TSP) solution; (2) maximizing surveillance quality by developing a Maximum Quality In Time (MQIT) algorithm that iteratively optimizes the UAV trajectory. The MQIT algorithm incorporates a global-first-local-refinement heuristic to maximize surveillance quality, along with a designed Minimum Time with Guaranteed Quality (MTGQ) module. The MTGQ module employs dynamic programming (DP) to optimize the time allocation while satisfying predefined quality thresholds. More importantly, the MTGQ algorithm can operate as an independent module to solve time-critical mission problems with specified surveillance quality requirements. Extensive simulation results demonstrate that our method outperforms several baseline approaches across various scenarios, achieving a 6% improvement in surveillance quality.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104010"},"PeriodicalIF":4.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027124","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":"Device cooperation and energy efficiency optimization for backscatter-assisted wireless-powered D2D in Industrial Internet of Things","authors":"Ling Tan,&nbsp;Jing Song,&nbsp;Haifeng Wang,&nbsp;Hai Xu","doi":"10.1016/j.adhoc.2025.104018","DOIUrl":"10.1016/j.adhoc.2025.104018","url":null,"abstract":"<div><div>To address the issues of energy lifetime and resource utilization in the Industrial Internet of Things (IIoT), the combination of wireless energy harvesting and Backscatter Communication (BC) technology significantly enhances the large-scale interconnection capability of IIoT. In this paper, we propose a hybrid Device-to-Device (D2D) communication framework that combines BC with Active Transmission (AT), where devices can make intelligent decisions to switch between modes based on distance, channel, and energy conditions. By jointly optimizing device associations, backscatter coefficients, load, and time allocation, the system maximizes energy efficiency while meeting delay, energy, and task requirements. Meanwhile, considering that selfish device behaviors may hinder collaboration, we design a payment mechanism based on resource-sharing benefits, incorporating social relationships to incentivize devices to actively participate in cooperation. Additionally, we propose a Multiagent Deep Deterministic Policy Gradient algorithm in the D2D hybrid communication network to solve the long-term joint optimization problem, enabling devices to dynamically adjust offloading strategies and construct optimal D2D links. Simulation results show that the proposed algorithm achieves higher rewards and faster learning speeds, improving system energy efficiency by approximately 5.3% compared to A3C-HAB and by about 33.3% compared to other benchmark solutions, demonstrating superior energy efficiency performance.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104018"},"PeriodicalIF":4.8,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145059933","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":"BMCH: Blockchain-enhanced multidimensional behavior scoring and privacy-preserving cluster head election scheme for the internet of vehicles","authors":"Ping Guo ,&nbsp;Yuheng Jiang ,&nbsp;Yanglei Gao ,&nbsp;Puwadol Oak Dusadeerungsikul","doi":"10.1016/j.adhoc.2025.104022","DOIUrl":"10.1016/j.adhoc.2025.104022","url":null,"abstract":"<div><div>The high mobility and dynamic nature of vehicles pose significant challenges for existing cluster head election mechanisms, which struggle to maintain network stability and efficiency amidst frequent topology changes. To address these challenges, this study proposes a novel scheme based on Blockchain-enhanced Multidimensional vehicle behavior scoring for Cluster Head election (BMCH). The proposed method evaluates a range of factors, including participation degree, integrity degree, vehicle resources allocation, location and mobility pattern, vehicle compliance and legal adherence and vehicle anomalous behaviors. By dynamically adjusting scoring weights, the mechanism thereby ensures more accurate cluster head election under varying network conditions. Additionally, blockchain technology is integrated to encrypt and securely store trust scores and election outcomes, ensuring data integrity, consistency and decentralized tamper resistance. Experimental simulations demonstrate that the proposed approach enhances network stability even in the presence of malicious nodes, while achieving shorter cluster head election times, higher packet delivery rates, increased blockchain throughput, and acceptable energy consumption compared to representative existing schemes.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104022"},"PeriodicalIF":4.8,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050105","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":"Age of information-aware multi-agent task scheduling strategy for vehicular edge computing","authors":"Yang Zhang ,&nbsp;Ling Yang ,&nbsp;Yan Tan","doi":"10.1016/j.adhoc.2025.103996","DOIUrl":"10.1016/j.adhoc.2025.103996","url":null,"abstract":"<div><div>The rapid proliferation of intelligent transportation applications has created unprecedented demands for real-time data processing in vehicular networks. Vehicular Edge Computing (VEC) addresses these challenges by leveraging computing resources on moving vehicles, but ensuring information timeliness remains problematic due to network dynamics and resource constraints. This paper proposes a novel multi-agent scheduling framework that utilizes Age of Information (AoI) as the primary metric to maintain data freshness in VEC environments. Unlike conventional approaches that focus solely on latency reduction, our strategy employs a distributed multi-agent architecture where vehicle-mounted servers collaborate through limited information exchange to make coordinated scheduling decisions. We develop a hybrid optimization model combining game theory and reinforcement learning that enables agents to balance individual and collective objectives while adapting to vehicular mobility patterns. The scheduling mechanism operates across three dimensions: spatial (server selection), temporal (execution timing), and priority-based (task importance). Simulation results using real-world urban traffic datasets demonstrate that our approach reduces average AoI by 37 % compared to centralized scheduling methods, while improving computational resource utilization by 42 % and decreasing communication overhead by 29 %. The proposed framework shows particular effectiveness in dense urban scenarios and heterogeneous task environments, offering a scalable solution for maintaining information freshness in next-generation intelligent transportation systems.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 103996"},"PeriodicalIF":4.8,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050102","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":"Reliable multi-RAT connectivity in urban V2X architectures: An experimental campaign","authors":"Lucas Bréhon╌Grataloup ,&nbsp;Rahim Kacimi ,&nbsp;André-Luc Beylot","doi":"10.1016/j.adhoc.2025.104007","DOIUrl":"10.1016/j.adhoc.2025.104007","url":null,"abstract":"<div><div>The future of smart cities relies on the quick and reliable exchange of contextual information between devices. Deploying access points at the edge of the network significantly reduces the propagation distance of sidelink messages, resulting in very low latencies. However, urban environments pose significant challenges, as numerous obstacles can block direct links between devices and their intended destinations. While the typical fallback solution is cellular connectivity, the difference in performance is substantial. To address this, the present work puts forward solutions for improved reliability in multiple Radio Access Technology urban vehicular architectures. On the one hand, we address situations where multiple links are available, by establishing a make-before-break vertical handover scheme for optimized radio interface selection, monitoring the performance of received packets. On the other hand, we also reflect on the situations where communication ranges are shortened, and thus propose a method where packets sent via Cellular-Vehicle-to-Everything (C-V2X) sidelink are relayed. Here, the initial message is received by an intermediary with better connectivity to the target access point and then retransmitted from this improved position. These solutions have yet to be studied in real-life urban testbeds. We thus present experimental campaigns aimed at exploring the performance and reliability of such communications in urban V2X architectures, exploiting C-V2X, 5G, autonomous vehicles and state-of-the-art hardware. We study multiple scenarios, extending or shortening line-of-sight situations to analyze coverage and performance. Performance analysis of our vertical handover solution shows a reduced usage of cellular connectivity, leading to a 27% reduction of packet latency. Then, relayed C-V2X packets show high reliability at up to 200-meter ranges from one peer to another, with observed latencies being, at worst, 42% more favorable than the cellular fallback.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104007"},"PeriodicalIF":4.8,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050106","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":"Computation offloading for vehicular networks via sparse-attention-based multi-agent reinforcement learning","authors":"Shuaiyang Ma ,&nbsp;Zhengyi Chai ,&nbsp;Yalun Li ,&nbsp;Jiani Fu ,&nbsp;LiLing Sun","doi":"10.1016/j.adhoc.2025.104006","DOIUrl":"10.1016/j.adhoc.2025.104006","url":null,"abstract":"<div><div>For Mobile Edge Computing (MEC) offloading in large-scale Internet of Vehicles (IoV), existing methods struggle to provide low-latency services. This paper proposes a multi-agent algorithm based on sparse attention weighting, aiming to minimize the expected cost. Specifically, a long short-term memory (LSTM) model is integrated into the actor network to assist VUs in predicting the future states of edge servers (ESs). Additionally, a sparse attention mechanism is employed to compress the joint observation space, reducing computational complexity and enabling adaptation to large-scale VU environments. Furthermore, to address the convergence difficulties arising from a large number of agents, curriculum learning is adopted for phased training. We conduct evaluations in a custom IoV simulation environment,experimental results demonstrate that the proposed Multi-Agent Sparse-Attention Soft Actor–Critic (MASASAC) algorithm outperforms baseline methods in both performance and convergence speed, achieving an improvement of approximately 16.4 % to 37.6 % and further enhancing performance by approximately 10.9 % through curriculum learning.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"179 ","pages":"Article 104006"},"PeriodicalIF":4.8,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027123","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}