Ad Hoc Networks最新文献_第3页

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

Utilization of machine learning in future wireless networks for resource optimization: A survey 机器学习在未来无线网络资源优化中的应用综述

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-28 DOI: 10.1016/j.adhoc.2025.103983

Mudassar Liaq , Sana Sharif , Sherali Zeadally , Waleed Ejaz

{"title":"Utilization of machine learning in future wireless networks for resource optimization: A survey","authors":"Mudassar Liaq , Sana Sharif , Sherali Zeadally , Waleed Ejaz","doi":"10.1016/j.adhoc.2025.103983","DOIUrl":"10.1016/j.adhoc.2025.103983","url":null,"abstract":"<div><div>Future wireless networks will play an essential role as the need for performance and feature availability grows. Most of the traffic in future wireless networks is due to increased Internet of things (IoT) devices, making resource optimization critical. Traditional optimization algorithms have limitations due to their high computational complexity, which restricts their use in modern applications. To address this, machine learning algorithms are now the preferred alternative to traditional optimization algorithms due to their improved runtime complexity. We present a comprehensive survey on the use of machine learning for resource optimization in future wireless networks. The use of machine learning is divided into three categories: (i) comprehensive solutions, where machine learning is the primary component of the solution approach; (ii) partial solutions, where machine learning is used alongside a traditional approach for optimization; and (iii) environment-only solutions, where optimization is performed in a machine-learning environment. We have further classified objective functions (e.g., energy, latency, data rate, etc.) within each category based on the pure objective function, variations on the objective function, and objective function tradeoffs with respect to other objective functions. We present objective functions and constraints used in the literature for optimization problem formulation. We provide an overview of frequently used machine learning algorithms for resource optimization, followed by a detailed survey of machine learning works in the literature in the three aforementioned categories. Finally, we discuss future research directions for utilizing machine learning to optimize resource management in future wireless networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103983"},"PeriodicalIF":4.8,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780744","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

Context-aware adaptive Send-on-Delta for traffic saving in sensor networks 基于上下文感知的自适应增量发送算法在传感器网络中节省流量

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-27 DOI: 10.1016/j.adhoc.2025.103967

Iago R. Martínez-Sánchez, Joaquín Cuellar-Padilla, Joaquín Olivares, Jose M. Palomares, Fernando León-García

{"title":"Context-aware adaptive Send-on-Delta for traffic saving in sensor networks","authors":"Iago R. Martínez-Sánchez, Joaquín Cuellar-Padilla, Joaquín Olivares, Jose M. Palomares, Fernando León-García","doi":"10.1016/j.adhoc.2025.103967","DOIUrl":"10.1016/j.adhoc.2025.103967","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) are widely deployed for real-time monitoring in domains such as industrial automation, smart buildings, and the Internet of Things (IoT). However, continuous sensor data transmission often results in excessive network traffic and elevated energy consumption, particularly in resource-constrained environments.</div><div>This paper presents a context-aware adaptive data reduction strategy based on the <em>Send-on-Delta</em> (SoD) transmission scheme, enhanced with a dynamic threshold adjustment mechanism driven by Bollinger Bands. A key novelty of the proposed approach lies in its global adaptation strategy: instead of configuring transmission thresholds independently at each node, the system evaluates the collective behavior of the network to dynamically reconfigure the local thresholds in a coordinated manner.</div><div>The method is supported by a formal and algorithmic model that captures the network-wide adaptation process, and is validated through a comprehensive evaluation including: a real-world deployment with ESP32-based wireless sensors operating over a 24-hour period; a scalability study with up to 40 simulated nodes derived from real data traces; and a post-hoc comparison against representative SoD variants, such as Send-on-Area and Predictive SoD. Experimental results show that the proposed strategy achieves over 89% reduction in transmission volume while maintaining bounded error, outperforming traditional approaches in lossless, context-sensitive scenarios. These results confirm the method’s effectiveness, robustness, and scalability for energy-efficient communication in WSNs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103967"},"PeriodicalIF":4.8,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780745","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

Design and implementation of a layered time slot MAC protocol for high-density single-anchor UWB positioning systems using DS-TWR 基于DS-TWR的高密度单锚超宽带定位系统分层时隙MAC协议的设计与实现

IF 4.8 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-26 DOI: 10.1016/j.adhoc.2025.103980

Xiangyang Li , Yan Tan , Yuanxiao Dang , Jianxin Xu , Senping Tian

{"title":"Design and implementation of a layered time slot MAC protocol for high-density single-anchor UWB positioning systems using DS-TWR","authors":"Xiangyang Li , Yan Tan , Yuanxiao Dang , Jianxin Xu , Senping Tian","doi":"10.1016/j.adhoc.2025.103980","DOIUrl":"10.1016/j.adhoc.2025.103980","url":null,"abstract":"<div><div>Ultra-wideband (UWB) technology, with its centimeter-level ranging accuracy, has become a key enabler for Internet of Things (IoT) applications such as asset tracking, smart warehouses, and proximity-aware systems. However, conventional MAC protocols struggle to support real-time, high-density localization in confined environments due to inefficient channel utilization and limited scalability. This paper proposes a novel MAC protocol featuring a layered time slot mechanism that hierarchically decouples logical (macro) and physical (micro) slot allocations. This design enables efficient scheduling of double-sided two-way ranging (DS-TWR) exchanges through non-contiguous resource reuse, without compromising ranging precision. A dynamic superframe structure is introduced, prioritizing deterministic ranging in the Contention-Free Period (CFP) and supporting retries in the Contention Access Period (CAP), and accommodating heterogeneous update rates across tags. Theoretical analysis and simulations demonstrate substantial improvements in tag capacity, bandwidth efficiency, and ranging throughput compared to standard guaranteed time slot (GTS)-based protocols. Furthermore, a hardware implementation using DecaWave DWM1000 modules validates the protocol’s feasibility, confirming effective multi-tag localization under real-world conditions. Beyond positioning, this layered time slot mechanism can also be applied to other domains besides positioning where several transmissions and receptions are necessary for one transaction.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103980"},"PeriodicalIF":4.8,"publicationDate":"2025-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dual-layer UAV-assisted mobile edge computing system for disaster rescue: Coordinated optimization of coverage, obstacle-avoidance path planning and task offloading 一种用于灾害救援的双层无人机辅助移动边缘计算系统：覆盖、避障路径规划和任务卸载协同优化

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-07-25 DOI: 10.1016/j.adhoc.2025.103981

Weiyu Gu , Tuanfa Qin , Dan Chen , Shixuan Xian , Xiao Jiang , Wenhao Guo , Yongle Hu

{"title":"A dual-layer UAV-assisted mobile edge computing system for disaster rescue: Coordinated optimization of coverage, obstacle-avoidance path planning and task offloading","authors":"Weiyu Gu , Tuanfa Qin , Dan Chen , Shixuan Xian , Xiao Jiang , Wenhao Guo , Yongle Hu","doi":"10.1016/j.adhoc.2025.103981","DOIUrl":"10.1016/j.adhoc.2025.103981","url":null,"abstract":"<div><div>This study addresses critical challenges in urban disaster rescue operations, such as fires, including communication failures, complex environments, and information scarcity. We propose a novel Dual-layer UAV-assisted Mobile Edge Computing (DUAMEC) system, leveraging an air–space–ground collaborative communication framework and intelligent task scheduling to overcome traditional limitations like information blind spots, decision-making delays, and inefficient response. DUAMEC innovatively combines a high-altitude upper-layer UAV (U-UAV) for wide-area coverage and a low-altitude down-layer UAV (D-UAV) for task processing, achieving strong coverage, low latency, and high energy efficiency. The core innovations of the DUAMEC system are manifested in the following aspects: First, we propose a grid-based adaptive multi-stage greedy optimization algorithm for optimal UAV deployment, dynamically generating multi-level candidate grids and employing adaptive step-size contraction. An uncovered-point compensation mechanism ensures continuous area coverage. Second, we design a Multi-Agent TD3 with Hindsight Priority Experience Replay (MATD3-HP) algorithm, utilizing a multi-dimensional state space and compound reward mechanism to optimize resource allocation, path planning, and task offloading in dynamic obstacle environments. Experimental results demonstrate that compared to conventional single-layer UAV-MEC systems and fixed path planning schemes, the DUAMEC system achieves an 66.78% reduction in system overhead while maintaining 98% user coverage. Simultaneously, it sustains stable performance with low task processing latency and energy consumption even in scenarios with dense user distribution and highly dynamic obstacles, thereby providing an efficient and reliable intelligent solution for urban disaster rescue operations.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103981"},"PeriodicalIF":4.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711858","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