Ad Hoc Networks最新文献_第5页

UAV-Edge Cloud collaboration for online offloading and trajectory control in multi-layer Mobile Edge Computing 多层移动边缘计算中UAV-Edge云协同在线卸载和轨迹控制

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-08 DOI: 10.1016/j.adhoc.2025.103866

Siyang Xu , Jingyi Ma , Qiuyu Lu , Zhigang Xie , Xin Song

{"title":"UAV-Edge Cloud collaboration for online offloading and trajectory control in multi-layer Mobile Edge Computing","authors":"Siyang Xu , Jingyi Ma , Qiuyu Lu , Zhigang Xie , Xin Song","doi":"10.1016/j.adhoc.2025.103866","DOIUrl":"10.1016/j.adhoc.2025.103866","url":null,"abstract":"<div><div>Integrating Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) offers enhanced coverage and computational support for mobile IoT devices (MIDs). Due to the inherent computational capacity and energy constraints of UAV, existing UAV-assisted MEC systems struggle to satisfy computation-intensive network services for numerous MIDs. To address this issue, this paper proposes a UAV and ground-based Edge Cloud (EC) collaboration MEC system. Specifically, the EC is equipped with an energy transmitter to provide wireless power transfer (WPT) to the UAV, thereby collaboratively managing backlog tasks in scenarios where task and energy arrive stochastically. To achieve optimal service delivery, we formulate a long-term stochastic optimization problem aiming to jointly optimize UAV energy consumption and system throughput while ensuring task queue stability. However, this NP-hard problem posed by the stochastic nature of task arrivals and energy constraints, we develop an online offloading and trajectory control (OOTC) algorithm. This algorithm uses Lyapunov optimization theory to transform the long-term stochastic optimization problem into a deterministic per-slot optimization problem. The OOTC algorithm decouples control decisions across consecutive time slots, reducing computational complexity and ensuring queue stability without relying on statistical knowledge. We further decompose it into three subproblems, and derive suboptimal solutions by the Successive Convex Approximation (SCA) and the Lagrangian duality. Simulations show OOTC algorithm outperforms benchmarks and maintains stability.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103866"},"PeriodicalIF":4.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937351","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

Energy-saving and security-enhanced task offloading strategies in D2D-integrated MEC networks d2d集成MEC网络中增强节能和安全的任务卸载策略

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-08 DOI: 10.1016/j.adhoc.2025.103890

Xiaowei Shi, Linyu Huang

{"title":"Energy-saving and security-enhanced task offloading strategies in D2D-integrated MEC networks","authors":"Xiaowei Shi, Linyu Huang","doi":"10.1016/j.adhoc.2025.103890","DOIUrl":"10.1016/j.adhoc.2025.103890","url":null,"abstract":"<div><div>With the rapid development of the Internet of Things, the demand for low latency and efficient computation has increased significantly. Mobile edge computing (MEC) has become a key technology for improving the performance of Internet of Things (IoT) systems. In cellular networks, computation offloading through D2D communication between edge devices can effectively reduce task latency and improve energy efficiency. However, most of the existing works focus on energy efficiency and delay optimization and often ignores security issues. Hence, we study the D2D offloading problem of edge devices under centralized scheduling of MEC servers, and propose a risk assessment criterion based on security level to jointly optimize energy consumption and security. To solve the problem, it was first modeled as a Mixed-Integer Nonlinear Programming (MINLP) problem. By optimizing the constraints, the problem was transformed into an Integer Linear Programming (ILP) problem and the theoretical optimal solution was obtained. Considering the requirements of practical engineering applications, a low-complexity heuristic algorithm is designed. The proposed strategies can be widely used in D2D-Integrated MEC Networks to improve system energy efficiency and security.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103890"},"PeriodicalIF":4.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928324","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

Schedulability analysis in time-sensitive networking: A systematic literature review 时间敏感网络中的可调度性分析：系统的文献综述

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-08 DOI: 10.1016/j.adhoc.2025.103897

Zitong Wang, Feng Luo, Yunpeng Li, Haotian Gan, Lei Zhu

{"title":"Schedulability analysis in time-sensitive networking: A systematic literature review","authors":"Zitong Wang, Feng Luo, Yunpeng Li, Haotian Gan, Lei Zhu","doi":"10.1016/j.adhoc.2025.103897","DOIUrl":"10.1016/j.adhoc.2025.103897","url":null,"abstract":"<div><div>Time-Sensitive Networking (TSN) is a set of standards that provide low-latency, high-reliability guarantees for the transmission of traffic in networks, and it is becoming an accepted solution for complex time-critical systems such as those in industrial automation and the automotive. In time-critical systems, it is essential to verify the timing predictability of the system, and the application of scheduling mechanisms in TSN can also bring about changes in system timing. Therefore, schedulability analysis techniques can be used to verify that the system is scheduled according to the scheduling mechanism and meets the timing requirements. In this paper, we provide a clear overview of the state-of-the-art works on the topic of schedulability analysis in TSN in an attempt to clarify the purpose of schedulability analysis, categorize the methods of schedulability analysis and compare their respective strengths and weaknesses, point out the scheduling mechanisms under analyzing and the corresponding traffic classes, clarify the network scenarios constructed during the evaluation and list the challenges and directions still needing to be worked on in schedulability analysis in TSN. To this end, we conducted a systematic literature review and finally identified 123 relevant research papers published in major conferences and journals in the past 15 years. Based on a comprehensive review of the relevant literature, we have identified several key findings and emphasized the future challenges in schedulability analysis for TSN.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103897"},"PeriodicalIF":4.4,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942231","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

Quantum GA-driven Digital Twin for task urgency-aware partitioning and offloading in multi UAV-Aided MEC systems 多无人机辅助MEC系统中任务紧急感知的量子ga驱动数字孪生

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-06 DOI: 10.1016/j.adhoc.2025.103891

Santanu Ghosh, Pratyay Kuila

{"title":"Quantum GA-driven Digital Twin for task urgency-aware partitioning and offloading in multi UAV-Aided MEC systems","authors":"Santanu Ghosh, Pratyay Kuila","doi":"10.1016/j.adhoc.2025.103891","DOIUrl":"10.1016/j.adhoc.2025.103891","url":null,"abstract":"<div><div>Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) empowers smart mobile devices (SMDs) to efficiently handle computation- and resource-intensive applications, particularly in critical scenarios. The integration of Digital Twin (DT) technology enhances scalability and streamlines the management of multi-user, multi-UAV-assisted MEC systems. This research focuses on partial task offloading within DT-enabled UAV-assisted MEC, addressing the joint problem of task partitioning and offloading using a quantum-inspired genetic algorithm (QIGA). The quantum chromosome is encoded and decoded through linear hashing. Task partitioning is performed to optimize system efficiency in terms of energy, latency, and load distribution across the MEC, while also considering task urgency. The fitness function incorporates two penalty factors to eliminate solutions that violate task deadlines or exceed the energy constraints of SMDs and edge servers. The QIGA is demonstrated to operate in polynomial time across all phases. Extensive simulations under various scenarios reveal that the proposed QIGA outperforms other algorithms in terms of energy efficiency, delay reduction, and load balancing within UAV-assisted MEC. Statistical analyses further validate the reliability and effectiveness of the results.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103891"},"PeriodicalIF":4.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916149","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

PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning 基于深度强化学习的车辆网络高效协同缓存管理方法PDRL-CM

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-06 DOI: 10.1016/j.adhoc.2025.103888

Pingjie Ou , Ningjiang Chen , Long Yang

{"title":"PDRL-CM: An efficient cooperative caching management method for vehicular networks based on deep reinforcement learning","authors":"Pingjie Ou , Ningjiang Chen , Long Yang","doi":"10.1016/j.adhoc.2025.103888","DOIUrl":"10.1016/j.adhoc.2025.103888","url":null,"abstract":"<div><div>In vehicular networks, onboard devices face the challenge of limited storage, and computational resources constrain their processing and storage capabilities. This limitation is particularly significant for applications that require complex computations and real-time responses. Additionally, limited storage capacity reduces the range of cacheable data, which can impact the immediate availability of data and the continuity of services. Therefore, improving cache utilization and meeting vehicles’ real-time data demands pose significant challenges. Deep reinforcement learning can optimize the issues arising from agents’ continuously changing state and action spaces due to increasing request demands. However, training the network may encounter instability and convergence difficulties in dynamic and complex environments or situations with sparse rewards. In response to these issues, this paper proposes a Priority-based Deep Reinforcement Learning Collaborative Cache Management method (PDRL-CM). PDRL-CM first designs a lightweight cache admission strategy that leverages data’s inherent and combined attributes. It then makes cache admission decisions Using Monte Carlo sampling and a max-value search strategy combined with a feedforward neural network. Secondly, the method considers minimizing system latency and reducing vehicle energy consumption as joint optimization problems. An improved deep reinforcement learning algorithm solves this problem and makes cache-sharding decisions. A prioritized experience replay mechanism is incorporated to adjust the network prediction model quickly and accelerate the convergence process. Experimental results indicate that, compared to existing DRL-based caching methods, PDRL-CM offers faster data processing efficiency and higher cache hit rates under varying vehicle density, storage capacity, and content volume conditions.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103888"},"PeriodicalIF":4.4,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143928323","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

ESFMTO: A reliable task offloading strategy based on edge server failure model in IIoT ESFMTO：基于IIoT边缘服务器故障模型的可靠任务卸载策略

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-04 DOI: 10.1016/j.adhoc.2025.103887

Yubin Yang , Yan Chen , Ningjiang Chen , Juan Chen

{"title":"ESFMTO: A reliable task offloading strategy based on edge server failure model in IIoT","authors":"Yubin Yang , Yan Chen , Ningjiang Chen , Juan Chen","doi":"10.1016/j.adhoc.2025.103887","DOIUrl":"10.1016/j.adhoc.2025.103887","url":null,"abstract":"<div><div>The extensive use of automation equipment and sensors in the Industrial Internet of Things (IIoT) has led to a significant increase in data volume, which has placed higher demands on the real-time processing capability. Edge computing enables real-time response and rapid decision-making by offloading data processing to the edge of the network. However, the complexity of the industrial production environment leads to edge server failures, which seriously affects the system stability and security. To address this issue, this paper develops an edge server failure model for IIoT, analyzing the interaction between the hardware failure and the container failure during the failure occurrence and recovery. Further, based on the edge server failure model, a task offloading strategy named ESFMTO is proposed, which employs the SAC-BNN (Soft Actor-Critic with Bayesian Neural Network) algorithm. The probability distribution of the task completion time is updated through the Bayesian Neural Network (BNN), accurately evaluating the Conditional Value at Risk (CVaR). During the training process of Deep Reinforcement Learning (DRL), a perturbation neural network is introduced to perturb the input state, which enhances the system robustness under uncertain failure conditions. Unlike existing approaches that often assume each edge server hosts a single container, the paper explicitly considers multi-container deployment in IIoT, bridging the gap between theoretical assumptions and real-world industrial requirements. Experimental results demonstrate that SAC-BNN outperforms existing methods in dealing with the edge server failure, reducing CVaR by at least around 0.9 and improving task completion rates by at least 13.86% compared to the baseline algorithms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103887"},"PeriodicalIF":4.4,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143911784","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

Swarm intelligence-inspired localization and power control for terahertz (THz) UAV-vehicle networks 基于群智能的太赫兹（THz）无人机网络定位和功率控制

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-05-01 DOI: 10.1016/j.adhoc.2025.103892

Enis Körpe , Mustafa Alper Akkaş , Yavuz Öztürk

{"title":"Swarm intelligence-inspired localization and power control for terahertz (THz) UAV-vehicle networks","authors":"Enis Körpe , Mustafa Alper Akkaş , Yavuz Öztürk","doi":"10.1016/j.adhoc.2025.103892","DOIUrl":"10.1016/j.adhoc.2025.103892","url":null,"abstract":"<div><div>In recent years, terahertz (THz) communication has gained significant attention as a transformative technology for high-speed wireless networks, addressing the limitations of conventional frequency bands in meeting the escalating demand for data transmission. THz communication is particularly critical in vehicle-to-everything (V2X) and unmanned aerial vehicle (UAV)-based communication networks, where ultra-low latency, high bandwidth, and reliable connectivity are essential. Operating in the frequency spectrum between the microwave and infrared bands, THz communication offers the potential for multi-gigabit data transmission rates, rendering it a promising enabler for next-generation intelligent transportation systems, autonomous vehicles, and UAV-supported applications. Furthermore, artificial intelligence (AI) emerges as a pivotal tool to enhance the reliability and efficiency of THz-based V2X and UAV communication networks by enabling the prediction of network traffic patterns and mobility dynamics. This study introduces a swarm intelligence-based AI approach designed to optimize system performance by minimizing latency and transmission power requirements while ensuring the required signal-to-noise ratio (SNR) within a UAV-assisted vehicular network operating in the THz band. The proposed methodology employs a dual-objective optimization framework that balances latency and transmission power within a predefined communication time frame. Comparative analysis is conducted between a baseline network with randomly distributed UAVs and a network employing UAV deployment guided by the proposed AI scheme. Also, the performance of proposed method is compared with existing swarm intelligence algorithms. Performance metrics, including SNR and latency, are evaluated to assess the system’s efficacy. The channel modeling process leverages the Line-by-Line Radiative Transfer Model (LBLRTM) to characterize the propagation environment in the UAV-assisted vehicular network.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103892"},"PeriodicalIF":4.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906974","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

Optimization and localization based framework for priority-aware node ranking and routing in IoT-driven acoustic systems 物联网声学系统中基于优先级感知节点排序和路由的优化和定位框架

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-04-30 DOI: 10.1016/j.adhoc.2025.103893

Tariq Ali , Umar Draz , Sana Yasin , Mohammad Hijji , Muhammad Ayaz , Isha Yasin , Tareq Alhmiedat

{"title":"Optimization and localization based framework for priority-aware node ranking and routing in IoT-driven acoustic systems","authors":"Tariq Ali , Umar Draz , Sana Yasin , Mohammad Hijji , Muhammad Ayaz , Isha Yasin , Tareq Alhmiedat","doi":"10.1016/j.adhoc.2025.103893","DOIUrl":"10.1016/j.adhoc.2025.103893","url":null,"abstract":"<div><div>Beacon node ranking is crucial for minimizing energy consumption and optimizing data routing in IoT-driven Underwater Acoustic Sensor Networks (UASN), where accurate node positioning is essential for applications such as underwater robotics, unmanned autonomous vehicles, and location-based services. This research presents an efficient beacon node ranking framework by integrating a modified PageRank mechanism with bio-inspired metaheuristic approaches, including Ant Colony Optimization (ACO) for optimal path selection, Artificial Bee Colony (ABC) for identifying high-fitness nodes, and Fish School Search (FSS) for optimal node selection. To enhance localization accuracy, the PageRank parameters are optimized through metaheuristic hybridization using an adaptive mathematical approach, while key performance indicators such as energy reduction, localization error, and route optimization are evaluated over multiple iterations. The proposed Priority Ranking Algorithm for Localization (PRAL) is designed for energy-efficient localization in both obstacle-free and obstacle-rich environments, assessing localization error, success ratio, ineffective position rate, and average localization time per node. Network Simulator-v3.35 and the AquaSim framework, PRAL demonstrates significant improvements over baseline methods (MBIL, LoMoB, LSMB, etc.), achieving a 6 % higher localization success rate (98 %), a 3.7 % reduction in localization error, an 18 % decrease in energy consumption, a 20 % increase in network lifetime, and a 15 % improvement in obstacle-handling efficiency. This framework enhances data transfer reliability, positioning accuracy, and overall network performance in deep-sea environments by effectively mitigating localization and energy consumption challenges.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103893"},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923837","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

MQTT-E: E2E encryption in MQTT via proxy re-encryption avoiding broker overloading MQTT- e：通过代理重新加密在MQTT中进行端到端加密，避免代理过载

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-04-30 DOI: 10.1016/j.adhoc.2025.103878

Francesco Buccafurri , Vincenzo De Angelis , Sara Lazzaro , Anusha Vangala

{"title":"MQTT-E: E2E encryption in MQTT via proxy re-encryption avoiding broker overloading","authors":"Francesco Buccafurri , Vincenzo De Angelis , Sara Lazzaro , Anusha Vangala","doi":"10.1016/j.adhoc.2025.103878","DOIUrl":"10.1016/j.adhoc.2025.103878","url":null,"abstract":"<div><div>A smart traffic monitoring system in smart city surveillance requires publisher and subscriber MQTT-enabled vehicles to share sensitive vehicle and route data with semi-trusted RSU nodes as brokers. To ensure end-to-end confidentiality, we propose the use of an RSU broker as a proxy to perform re-encryption of the exchanged messages between publisher and subscriber vehicles. The RSU brokers are implemented as serverless edge devices with the proxy re-encryption functions designed as function-as-a-service. In peak traffic scenarios, the RSU proxy brokers can become overloaded and drop the re-encryption operations. Additionally, a malicious actor can send counterfeit re-encryption requests to overload the brokers leading to Denial-of-Service attacks. In this paper, we propose a novel solution to mitigate DoS attacks by balancing the re-encryption functions from overloaded brokers. This problem is modeled as an online optimization problem, solved using a greedy heuristic approach, and compared with a baseline approach. The objective function is to reallocate the minimum number of clients when brokers are overloaded since this operation brings additional overhead for clients. Our experimental analysis shows that the greedy approach manages to move up to 5 times fewer clients than the baseline approach, depending on the scenario considered.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"176 ","pages":"Article 103878"},"PeriodicalIF":4.4,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923456","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

An improved algorithm for indoor localization fingerprint database construction 一种改进的室内定位指纹数据库构建算法

IF 4.4 3区计算机科学

Ad Hoc Networks Pub Date : 2025-04-29 DOI: 10.1016/j.adhoc.2025.103858

Zhuo Li, Wei Liu

引用次数: 0