Ad Hoc Networks最新文献

{"title":"AUV-Assisted data collection using hybrid clustering and reinforcement learning in underwater acoustic sensor networks","authors":"Yanxia Chen ,&nbsp;Rongxin Zhu ,&nbsp;Azzedine Boukerche ,&nbsp;Qiuling Yang","doi":"10.1016/j.adhoc.2025.103877","DOIUrl":"10.1016/j.adhoc.2025.103877","url":null,"abstract":"<div><div>Underwater Acoustic Sensor Networks (UASNs) have garnered increasing attention for applications such as environmental monitoring, disaster response, and marine resource exploration. Despite their advantages, including self-organization and flexible deployment, UASNs face significant challenges in the underwater environment, such as energy constraints, propagation delays, and limited bandwidth. Addressing these challenges requires efficient methods to optimize energy usage and data transmission. In this work, we propose ACRL, a clustering and reinforcement learning-based approach for underwater data collection. ACRL combines a hybrid Fuzzy C Means (FCM) and Firefly Algorithm (FA) to optimize clustering and cluster head selection, reducing energy consumption and workload while maintaining efficient data collection. Additionally, ACRL leverages Q-learning to refine Autonomous Underwater Vehicle (AUV) trajectory planning. Extensive simulations demonstrate that ACRL achieves reduced energy consumption and data collection delay, outperforming existing methods under various scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103877"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing load-balanced resource allocation in next-generation mobile networks: A parallelized multi-objective approach","authors":"Jesús Calle-Cancho ,&nbsp;Jesús Galeano-Brajones ,&nbsp;David Cortés-Polo ,&nbsp;Javier Carmona-Murillo ,&nbsp;Francisco Luna-Valero","doi":"10.1016/j.adhoc.2025.103912","DOIUrl":"10.1016/j.adhoc.2025.103912","url":null,"abstract":"<div><div>The rapid evolution of mobile communications, driven by the proliferation of mobile devices and data-intensive applications, has driven an unprecedented increase in data traffic, pushing the current network infrastructure to its limits. In Beyond 5G and future 6G networks, minimizing network latency is crucial to support next-generation applications, such as immersive media, autonomous systems, and critical real-time services, all of which demand ultra-low latency and high reliability. In Multi-access Edge Computing environments, where future 6G networks will be deployed, efficient allocation of virtual base stations to the access network in dense environments will be essential to optimize performance and maintain quality of service. This efficient allocation will be key to effectively addressing the challenges present in these settings. This paper addresses this problem through a parallelized multi-objective evolutionary algorithm that simultaneously optimizes signaling delay, data plane overhead, and load balancing. By leveraging a Pareto-based approach, we provide a set of optimal trade-offs that enhance network adaptability and efficiency beyond traditional single-objective methods. Moreover, we introduce a novel metric inspired by the Sharpe ratio to evaluate the efficiency of load distribution across the network. Experimental results in various network topologies show that our approach significantly enhances network performance, achieving reductions in data plane overhead of up to 51.5% and 77.9% in signaling delay compared to a state-of-the-art solution based on a specialized heuristic. By providing a set of non-dominated solutions, our approach enables network operators to select configurations that best meet specific quality of service requirements and service priorities, thereby improving network adaptability and resilience under varying conditions.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103912"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170516","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":"Battery life optimization in LoRa networks using spreading factor reallocation","authors":"Ian H. de Andrade,&nbsp;Luís Henrique M.K. Costa,&nbsp;Rodrigo S. Couto","doi":"10.1016/j.adhoc.2025.103911","DOIUrl":"10.1016/j.adhoc.2025.103911","url":null,"abstract":"<div><div>This paper proposes a dynamic strategy for the allocation of resources used by end devices in LoRa networks, which employ chirp spread spectrum modulation. The proposed battery life optimization (BLO) strategy splits end devices into different spreading factor (SF) groups. The basic idea is to reduce the collisions between end devices using the same SF. Moreover, BLO also considers the current battery level of each end device, and periodically reallocates the SF groups to optimize the battery consumption of all nodes and extend the network lifetime. The main innovation of BLO is to consider in addition to the RSSI the air time of different SFs as a weighting factor in SF allocation. We compare BLO to state-of-the-art (SoA) SF-allocation strategies, achieving 77% improvement in successful message delivery compared to LoRaWAN’s ADR scheme. Furthermore, we obtain better energy efficiency with BLO. In a scenario with one gateway and 500 devices operating over 24 h, the remaining energy with BLO is 10 and 3.6 times larger than with EXPLoRa-SF and EXPLoRa-AT SoA strategies, respectively.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103911"},"PeriodicalIF":4.4,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178546","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":"Location privacy-preserving ride matching with verifiable and collusion resistance for Ride-Hailing Services","authors":"Hui Huang ,&nbsp;Rui Zhang ,&nbsp;Chenhuang Wu ,&nbsp;Shihui Lin","doi":"10.1016/j.adhoc.2025.103910","DOIUrl":"10.1016/j.adhoc.2025.103910","url":null,"abstract":"<div><div>Ride-Hailing Services (RHS) provide riders with convenient travel services and offer drivers economic incentives. However, during the process of requesting ride-matching from the Ride-Hailing Service Provider (RHSP), the transmission of data and query processing between riders and drivers may potentially expose sensitive user information, such as boarding and alighting locations and movement trajectories. Moreover, the RHSP might collude with drivers to manipulate the accuracy and integrity of ride-matching results. In this paper, we propose a novel privacy-preserving and collusion-resistant driver verification scheme (PPCRV) that supports privacy protection, collusion resistance, verifiability, and accountability. Our approach allows for personalized settings, enabling both riders and drivers to protect their location privacy within predefined cloaked areas. Additionally, we employ prefix encoding to link identities with location prefixes, generating unpredictable movement trajectories. We also utilize Indistinguishable Bloom Filter (IBF) for efficient querying. By IBF trees and proof information, we ensure verifiable matching results, allowing riders to independently replicate the matching process. Simultaneously, we apply traceable signature technology to maintain user anonymity while enabling effective identity verification and traceability, ensuring that malicious users can be held accountable when required. Finally, we conduct theoretical analyses and experiments to evaluate the scheme’s performance in terms of computational cost and communication overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103910"},"PeriodicalIF":4.4,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170512","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":"TReB: Task dependency aware-Resource allocation for Internet of Things using Binary offloading","authors":"Priyanka Soni ,&nbsp;Ajay Gajanan Hajare ,&nbsp;Keerthan Kumar T.G. ,&nbsp;Sourav Kanti Addya","doi":"10.1016/j.adhoc.2025.103909","DOIUrl":"10.1016/j.adhoc.2025.103909","url":null,"abstract":"<div><div>The rapid growth of Internet of Things (IoT) applications in domains such as healthcare, smart homes, and autonomous vehicles has led to an exponential increase in data generated by compute intensive tasks. Efficiently offloading these tasks to nearby computational resources in fog environments remains a significant challenge due to the inherent heterogeneity and constrained resources of Fog Nodes (FNs). Most of the existing approaches fail to address the trade-offs between latency, energy, and resource utilization, particularly when managing dependent and independent task workloads. Moreover, establishing an offloading strategy within a densely interconnected IoT network is known to be <span><math><mrow><mi>N</mi><mi>P</mi></mrow></math></span>-hard. To overcome these limitations, in this work, we propose a <strong>T</strong>ask dependency-Aware <strong>Re</strong>source allocation for IoT using <strong>B</strong>inary offloading (<span>TReB</span>) framework by considering both independent and dependent tasks of IoT applications. The <span>TReB</span> utilizes the Analytic Hierarchy Process (AHP) technique to generate the preferences of FNs and tasks by considering diverse attributes. With preferences established, a binary offloading is handled through a one-to-many matching procedure, utilizing a Deferred Acceptance Algorithm (DAA). It allows <span>TReB</span> to jointly minimize system energy consumption, latency, and the number of outages in an IoT network. We evaluated the effectiveness of <span>TReB</span> through simulation experiments, and results show that the proposed approach achieves a 49.1%, 62.4%, and 41.7% minimization in overall system latency, energy, and outages compared to the existing baselines.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103909"},"PeriodicalIF":4.4,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147765","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 5G and 6G networks through a dynamic dual-stage machine learning heuristic framework for selecting UEs as UE-VBSs","authors":"Iacovos Ioannou ,&nbsp;S.V. Jansi Rani ,&nbsp;Prabagarane Nagaradjane ,&nbsp;Christophoros Christophorou ,&nbsp;Vasos Vassiliou ,&nbsp;Andreas Pitsillides","doi":"10.1016/j.adhoc.2025.103908","DOIUrl":"10.1016/j.adhoc.2025.103908","url":null,"abstract":"<div><div>Adapting mobile networks to the diverse and evolving demands of 5G and forthcoming 6G technologies requires flexible, efficient, and dynamic strategies—especially in ultra-dense environments and infrastructure-limited areas. This paper proposes a robust two-stage Machine Learning (ML) heuristic framework to dynamically select a group of User Equipment (UEs) to act as Virtual Base Stations (UE-VBSs) for network augmentation. In the first stage, Self-Organizing Maps (SOM) are employed to cluster UEs based on their spatial characteristics while preserving topological relationships, achieving a silhouette score of 0.64—a 30% improvement over conventional methods such as <span><math><mi>K</mi></math></span>-Means (0.46) and Mean-Shift (0.43). In the second stage, a Random Forest classifier enhanced via the Synthetic Minority Over-sampling Technique (SMOTE) attains an average accuracy of 97% and an F1-Score of 0.88 in identifying eligible devices to become UE-VBSs, outperforming recent frameworks that typically report accuracies ranging between 85% and 92%.</div><div>Comparative evaluation results demonstrate that our two-stage ML heuristic framework not only improves clustering accuracy and UE-VBS classification but also consistently outperforms state-of-the-art clustering methods in terms of network sum rate, power consumption, and scalability. Specifically, across all device densities (i.e., 200, 400, 600, 800, and 1000 UEs), our approach achieves the highest sum rate—peaking at nearly 1.8 billion bps (or 1.8 Gbps) at 1000 UEs—thus surpassing methods such as Affinity Propagation and Grid-based Clustering. Furthermore, by intelligently selecting UE-VBSs, the framework significantly reduces power consumption by effectively minimizing redundant transmissions and interference, making it an energy-efficient solution for large-scale 5G networks. Although the complexity of SOM clustering and Random Forest classification introduces higher computational overhead, the resulting improvements in throughput, energy efficiency, and scalability justify this cost, making it a robust and practical solution for real-world deployments. Validated on both synthetic and real-world datasets, our findings underscore the efficacy, scalability, and high impact of employing robust unsupervised and ensemble learning techniques for dynamic network optimization in next-generation architectures, delivering up to a five-fold increase in network sum rate under high-density conditions compared to state-of-the-art approaches like grid-assisted clustering and affinity propagation.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103908"},"PeriodicalIF":4.4,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing energy efficiency in Long Term Evolution-Advanced networks: Analyzing latency through Connected mode Discontinuous Reception with Markov modeling","authors":"B. Arunsundar,&nbsp;P. Sakthivel","doi":"10.1016/j.adhoc.2025.103905","DOIUrl":"10.1016/j.adhoc.2025.103905","url":null,"abstract":"<div><div>Long Term Evolution, particularly Long Term Evolution-Advanced, has revolutionized mobile communication by delivering high-bandwidth connectivity for data-intensive applications. To meet the growing demand for high-speed data transmission in the rapidly expanding global mobile communication industry, Long Term Evolution-Advanced has emerged as the standard for 5G networks. However, energy consumption and latency remain critical challenges in wireless network communications. Discontinuous Reception, specifically Connected mode Discontinuous Reception, has been implemented to optimize the energy consumption of User Equipment while maintaining network performance. This paper analyzes the average latency of the Discontinuous Reception mechanism using both Markov and recursive models. Analytical modeling is employed to validate simulation results, incorporating various Discontinuous Reception parameters. The study demonstrates that the proposed Discontinuous Reception system, with adaptive settings based on a logic controller, enhances energy conservation performance by learning from past delay times and packet arrival rates. The simulation results highlight the improved efficacy of the Discontinuous Reception system in terms of latency and energy efficiency, offering valuable insights into the evolving landscape of wireless communication networks.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103905"},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190451","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 machine learning framework for inferring properties of embedded devices","authors":"Shariq Bashir","doi":"10.1016/j.adhoc.2025.103907","DOIUrl":"10.1016/j.adhoc.2025.103907","url":null,"abstract":"<div><div>Nowadays, the prevalence of Internet of Things (IoT) devices has increased in our homes and workplaces, providing us with more convenience. However, the security of these devices is often compromised. The objective of this paper is to assess the security of embedded IoT devices. Existing passive fingerprinting approaches are inapplicable in the configuration when the network traffic of devices connected to an IoT hub is inaccessible. We proposed a firmware analysis technique for analyzing devices’ security by inspecting their firmware contents. Our aim is not to identify unknown vulnerabilities, but only those that are already known. We also intend to investigate whether the software that is executing services is outdated or not. Precise information regarding the name and version of servers, as well as login credentials and passwords, can be obtained through the analysis of firmware. Having obtained this information, we have created an active identification technique that enables an attacker to deduce specific characteristics of a connected device, such as the name of the software employed for the HTTP server or usernames. Our method involves training a classifier using data extracted from firmware. The results of our experiments indicate that our approach is more effective and covert compared to a brute-force method. We scrutinized 5,204 firmware of devices using our approach. Our findings suggest that the level of exposure of connected devices has grown in recent years. As connected devices become more open to services, it increases the potential attack surface while reducing their security.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103907"},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131737","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":"AGV-Integrated Noise-Aware Adaptive Clustering for Industrial Wireless Sensor Networks in smart factories","authors":"Ying Duan ,&nbsp;Tongyao Fu ,&nbsp;Lingling Li ,&nbsp;Pasquale Pace ,&nbsp;Gianluca Aloi ,&nbsp;Giancarlo Fortino","doi":"10.1016/j.adhoc.2025.103906","DOIUrl":"10.1016/j.adhoc.2025.103906","url":null,"abstract":"<div><div>Industrial Wireless Sensor Networks (IWSNs) play a critical role in real-time monitoring and data collection in smart factories. However, energy constraints in sensor nodes significantly limit the network lifespan. In addition, traditional simulation methods overlook the impact of industrial noise, reducing the truthfulness of experimental results. To address these challenges, we propose an Automated Guided Vehicle-Integrated Noise-Aware Adaptive Clustering (A-INAC) algorithm. The algorithm incorporates an Industrial Wireless Noise Model (IWNM) to reflect noise characteristics in the factory environment and optimizes the selection of cluster directors to achieve more balanced energy consumption. In addition, a hierarchical transmission strategy leveraging the mobility of AGVs is designed to meet large-scale network transmission needs. Simulation results demonstrate that the A-INAC algorithm can effectively reduce network energy consumption and extend network lifetime by 39% and 118% compared to LEACH and LEACH-C, respectively.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103906"},"PeriodicalIF":4.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144170515","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":"Heuristic and reinforcement learning-based survivable trust-aware virtual network embedding for IoT networks","authors":"Parinaz Rezaeimoghaddam,&nbsp;Irfan Al-Anbagi","doi":"10.1016/j.adhoc.2025.103898","DOIUrl":"10.1016/j.adhoc.2025.103898","url":null,"abstract":"<div><div>Integrating virtual wireless sensor networks (VWSNs) with the Internet of Things (IoT) improves the quality of information (QoI) and quality of service (QoS). It manages wireless interference, critical to providing efficient and reliable services. Among the challenges in IoT-WSN virtualization, the survivable virtual network embedding (SVNE) problem stands out, as it efficiently maps a virtual network request (VNR) onto a WSN substrate while considering potential substrate failures and network security standards. This paper proposes a trust-aware fault recovery mechanism to address the security and survivability of virtualized IoT-WSN applications against physical infrastructure failures with two heuristic and intelligent approaches. Our proposed heuristic approach utilizes a node importance measurement strategy for faulty nodes based on the technique for order of preference by similarity to the ideal solution (TOPSIS) method. On the other hand, in our intelligent approach, we apply the deep Q-Learning (DQL) method to ensure end-to-end failure recovery for both nodes and links and improve physical resource utilization. To maintain cost efficiency, when a VNR experiences failure due to a fault in the physical infrastructure, its operation is restored through node/link migration without considering any backup resources. Our simulation results demonstrate that the proposed strategy effectively ensures the survivability of the VNRs, mitigates failures with our proposed failure recovery algorithms, and enhances the VNR acceptance rate.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"177 ","pages":"Article 103898"},"PeriodicalIF":4.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131736","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}