Ad Hoc Networks最新文献

{"title":"Device Fingerprinting in Power Line Communications","authors":"Muhammad Irfan ,&nbsp;Javier Hernandez Fernandez ,&nbsp;Aymen Omri ,&nbsp;Savio Sciancalepore ,&nbsp;Gabriele Oligeri","doi":"10.1016/j.adhoc.2025.103955","DOIUrl":"10.1016/j.adhoc.2025.103955","url":null,"abstract":"<div><div>Power Line Communication (PLC) use existing electrical infrastructure for data transmission but are susceptible to security threats such as spoofing and impersonation attacks due to their open nature. This paper proposes a novel Device Fingerprinting (DF) approach for device authentication in PLC systems. The approach leverages hardware-induced imperfections in signals transmitted over power lines to identify devices based on their physical-layer characteristics.</div><div>We develop a methodology that converts raw In-Phase Quadrature (IQ) samples from PLC channels into images, enabling the use of Convolutional Neural Networks for device classification. Our approach demonstrates the feasibility of CNN-based DF in PLC environments using only physical-layer information from received signals. Our experimental validation uses 8 Software Defined Radios and 2 power line couplers in real-world PLC measurements. We evaluate multiple Convolutional Neural Network (CNN) architectures and demonstrate that the PLC device fingerprint consists of two components: radio-specific and coupler-specific characteristics. The results show classification accuracy exceeding 0.9 across different configurations, establishing the viability of DF-based authentication in PLC systems without requiring additional security layers.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103955"},"PeriodicalIF":4.4,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144522914","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":"ASOCIDA: adaptive self-optimizing approach for anomaly detection and collaborative isolation in wireless sensor networks","authors":"Sabrina Boubiche ,&nbsp;Djallel Eddine Boubiche ,&nbsp;Homero Toral-Cruz","doi":"10.1016/j.adhoc.2025.103959","DOIUrl":"10.1016/j.adhoc.2025.103959","url":null,"abstract":"<div><div>Ensuring reliable and secure communication in wireless sensor networks (WSNs) is critical for a wide range of applications, including surveillance, energy management, and healthcare systems. However, existing anomaly detection and isolation methods often face difficulties in maintaining high detection accuracy, minimizing false alarms, and conserving energy, particularly under rapidly changing network conditions. This paper addresses these limitations by introducing ASOCIDA (Adaptive Self-Optimizing Approach for Anomaly Detection and Collaborative Isolation in WSNs), a novel and dynamic framework that combines real-time monitoring, collaborative validation, and self-adaptive optimization. The proposed approach leverages Mahalanobis distance and adaptive EWMA for responsive anomaly detection based on dynamic thresholds. A distributed reputation system, enhanced by Byzantine Fault Tolerance mechanisms, is used to validate anomalies collaboratively and ensure robust local isolation. Furthermore, a closed-loop control system dynamically adjusts the detection parameters based on alert reduction trends observed after isolation, ensuring continuous performance improvement. Simulation results demonstrate that ASOCIDA achieves a detection accuracy of 98%, a false alarm rate as low as 0.3%, and a 15% reduction in energy consumption, significantly outperforming traditional techniques. Additionally, it offers an average response time of 15ms and a recovery time of 1.02s, while increasing the packet delivery rate by 10% and reducing the average latency by 20%. These outcomes confirm the potential of ASOCIDA to improve both security and efficiency in dynamic and heterogeneous WSN environments.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103959"},"PeriodicalIF":4.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548495","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 in MEC-assisted vehicular networks with task migration and result feedback","authors":"Jingwei Geng,&nbsp;Shunfu Jin","doi":"10.1016/j.adhoc.2025.103936","DOIUrl":"10.1016/j.adhoc.2025.103936","url":null,"abstract":"<div><div>In vehicular networks, roadside units (RSUs) with mobile edge computing (MEC) assistance bring computing resources to the edge for enhancing the computation capacities of vehicles. However, uneven distribution of vehicles leads to load imbalance between MEC computing servers and this poses a huge challenge to computation offloading in vehicular networks. In this paper, we consider task migration between RSUs with different loads on a horizontal scale and computation result feedback in a MEC-assisted computation offloading scenario. We develop a vehicle trajectory prediction module based on deep neural networks for predicting the vehicle position after a task is completed and calculating the delay and energy consumption in the result feedback process. We formulate a computation offloading problem with the aim of minimizing total computation cost within continuous time slots. To address the coupling of decisions under different time slots, we propose a Lyapunov-based novel online heuristic offloading (LNOHO) algorithm for the formulated problem. Applying the Lyapunov optimization framework, the original multi-slot problem is decomposed into multiple per-slot subproblems. Each subproblem is a nonlinear integer programming (NIP) problem. For such an NP-hard problem, we divide it into three processes and propose a load-aware migration heuristic (LMH) algorithm with low complexity to obtain per-slot decisions. The simulation results based on real road topology show that our proposed vehicle trajectory prediction module and algorithm can achieve better performance.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103936"},"PeriodicalIF":4.4,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144366051","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 Q-learning-based trust model in underwater acoustic sensor networks (UASNs)","authors":"Mehdi Hosseinzadeh ,&nbsp;Amir Haider ,&nbsp;Amir Masoud Rahmani ,&nbsp;Khursheed Aurangzeb ,&nbsp;Zhe Liu ,&nbsp;Mohammad Sadegh Yousefpoor ,&nbsp;Efat Yousefpoor ,&nbsp;Sang-Woong Lee ,&nbsp;Parisa Khoshvaght","doi":"10.1016/j.adhoc.2025.103918","DOIUrl":"10.1016/j.adhoc.2025.103918","url":null,"abstract":"<div><div>Underwater acoustic sensor networks (UASNs) play a pivotal role in various civil and military fields. However, due to their open nature, they are susceptible to multiple security threats. As such, developing robust and reliable security strategies is essential to ensure the normal operation of UASNs. This paper proposes a Q-learning-based trust model (QLTM) for UASNs. To detect hostile nodes, each underwater sensor node is required to collect trust evidence –namely energy trust evidence, data trust evidence, and communication trust evidence–through communication and interaction with its neighboring nodes. After gathering the trust evidence, QLTM presents a distributed Q-learning-based trust management model that adapts to dynamic underwater environments. It continuously updates the trust parameters based on ongoing interactions between the agent and the environment. The Q-learning-based trust management model includes a state set with three states: trust, distrust, and uncertain. Additionally, the reward function is calculated according to the gathered trust evidence, and the weight of each trust evidence is determined such that evidence with a lower value carries more weight, thus having a greater effect on the generated reward. Experimental results demonstrate the effectiveness of QLTM compared to other trust mechanisms, so that QLTM improves the detection accuracy rate by 5.04%. However, when the attack mode changes in the network, QLTM performs approximately 4.29% worse than TUMRL in detecting malicious nodes. On the other hand, QLTM reduces the false alarm rate by about 7.39% and increases energy efficiency by approximately 4.26%.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103918"},"PeriodicalIF":4.4,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330591","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 centralized discovery-based method for integrating Data Distribution Service and Time-Sensitive Networking for In-Vehicle Networks","authors":"Feng Luo ,&nbsp;Yi Ren ,&nbsp;Yanhua Yu ,&nbsp;Yunpeng Li ,&nbsp;Qin Liu ,&nbsp;Xiaobo Zhang","doi":"10.1016/j.adhoc.2025.103950","DOIUrl":"10.1016/j.adhoc.2025.103950","url":null,"abstract":"<div><div>As the Electronic and Electrical Architecture (E/EA) of Intelligent and Connected Vehicles (ICVs) evolves, traditional distributed and signal-oriented architectures are being replaced by centralized, Service-Oriented Architectures (SOA). This new generation of E/EA demands In-Vehicle Networks (IVNs) that offer high bandwidth, real-time performance, reliability, and service orientation. Data Distribution Service (DDS) and Time-Sensitive Networking (TSN) are increasingly adopted to address these requirements. However, research on the integrated deployment of DDS and TSN in automotive applications is still in its infancy. This paper presents a DDS over TSN communication architecture based on the centralized discovery architecture. First, a lightweight DDS implementation (FastDDS-lw) is developed for resource-constrained in-vehicle devices. Next, a DDS Flow Identification Algorithm (DFIA) based on the centralized discovery architecture is introduced to identify potential DDS flows automatically during the discovery phase. Finally, the DDS over TSN communication architecture, incorporating FastDDS-lw and DFIA, is designed. Experimental results show that the DDS over TSN architecture significantly reduces end-to-end latency and jitter for critical DDS flows compared to traditional Ethernet. Additionally, the DDS over TSN architecture provides automated network scheduling and network configuration to handle the addition, removal, and modification of DDS flows in IVNs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103950"},"PeriodicalIF":4.4,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313189","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":"An efficient computational offloading method using deep reinforcement learning in edge-end-cloud","authors":"Xinrui Liu ,&nbsp;Libo Feng ,&nbsp;Peiying Zhang ,&nbsp;Yimin Yu ,&nbsp;Jinli Wang","doi":"10.1016/j.adhoc.2025.103941","DOIUrl":"10.1016/j.adhoc.2025.103941","url":null,"abstract":"<div><div>Due to certain limitations of cloud and edge computing, the issue of delayed response arises from both. We propose an edge-end-cloud computational unloading solution based on deep reinforcement learning. Firstly, we introduce the pre-division algorithm to facilitate the implementation of the second stage and address the threshold selection of the calculation unloading strategy. Secondly, we analyze the computing resources within the DQN and Q-learning frameworks. Finally, we present the parameter verification of the blockchain. The integration of blockchain technology enhances the security and credibility of data transmission. Additionally, blockchain technology can strengthen the credibility of the edge ecology and mitigate the single point of trust risk encountered by traditional centralized architectures on the edge side. The experimental results indicate that the proposed computational unloading strategy in this paper decreases the edge-end-cloud architecture using DQN and Q-learning by approximately 40% compared to other computing strategies. When we adjust the server’s computing power to <span><math><mrow><mi>F</mi><mo>=</mo><mn>10</mn></mrow></math></span> GHz/s, the energy consumption of Q-learning and DQN becomes nearly identical, suggesting that if the server’s computational power is sufficiently strong, the unloading results can often be more favorable.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103941"},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330590","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":"Multi-type service placement for joint transmission and processing in mobile augmented reality","authors":"Fuyu Liu ,&nbsp;Jingbo Ji ,&nbsp;Xuejian Chi","doi":"10.1016/j.adhoc.2025.103939","DOIUrl":"10.1016/j.adhoc.2025.103939","url":null,"abstract":"<div><div>Augmented Reality (AR) technology can achieve real-time responsiveness and efficient processing by leveraging Edge Computing. Deploying mobile AR applications on edge servers significantly reduces service latency and enhances system quality of service (QoS). However, existing studies primarily focus on service placement on edge servers, while often neglecting the critical impact of network access points on service latency. In this study, we propose a joint transmission and processing multi-type service placement framework. This framework first determines the user’s access point and then identifies the optimal location for deploying the application serving that user, aiming to minimize the total system service latency. The framework faces two key challenges: user mobility and the diversity of service demands. To address these challenges, this paper designs a heuristic approach based on Block Coordinate Descent (HBCD). This algorithm dynamically selects the optimal access point and edge server for users during each epoch, effectively reducing total system service latency. Theoretical analysis demonstrates that the discrepancy in performance between the proposed algorithm and the optimal solution is tightly bounded. Furthermore, extensive simulations driven by real-world data demonstrate that the HBCD algorithm reaches near-optimal performance and markedly surpasses existing methods.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103939"},"PeriodicalIF":4.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313190","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":"Reliability-Aware Packet Replication in Multi-Path Data Transmission for Mission-Critical IoT Networks","authors":"Soumya Nandan Mishra,&nbsp;Manas Khatua","doi":"10.1016/j.adhoc.2025.103940","DOIUrl":"10.1016/j.adhoc.2025.103940","url":null,"abstract":"<div><div>Mission-critical IoT applications require a strict reliability guarantee of at least 99% to ensure seamless operation. However, due to the resource-constrained nature of IoT networks, data transmissions inherently suffer from losses. This contradiction presents significant challenges for the traditional IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL), which was originally designed for general IoT networks. To address this issue, various multi-path RPL-based routing approaches have been proposed. One such solution is Reliable Multi-Path RPL (RMP-RPL), which attempts to enhance reliability by replicating packets and forwarding them through multiple parents. However, meeting the reliability requirement even with a multi-parent-based approach is difficult when the wireless links have high error rates. This is because one transmission attempt to each parent for a packet is not enough for such links. On the other hand, increasing the number of parents is also limited by many factors like resource consumption. To address these issues, we propose Reliability-Aware Packet Replication in Multi-Path Data Transmission (RAPID), which dynamically selects the number of parents and optimally determines the number of replicated packets per parent to meet the reliability constraint while minimizing redundant transmissions. The proposed scheme introduces a joint delivery ratio metric, and proposes greedy-based (RAPID-G) and approximation-based (RAPID-A) packet replication strategies to manage packet replication efficiently. Experimental results in Contiki COOJA simulator show that RAPID-A can achieve the reliability requirements of 90%, 95% and 99% under varying packet reception ratio and network density with an average energy consumption reduction of 34.23% as compared to RMP-RPL. The proposed protocol, RAPID-G, outperforms another multi-path algorithm, LFC, by 41.23%, 39.23% and 35.63% in terms of packet delivery ratio, end-to-end delay and energy consumption, respectively, and RAPID-A outperforms LFC by 38.63%, 44.12%, and 40.12% in terms of packet delivery ratio, end-to-end delay and energy consumption, respectively.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103940"},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297188","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":"QLA-MAODV: A Q-learning adaptive multicast routing protocol for mobile ad-hoc networks","authors":"Ola Ashour ,&nbsp;Thomas Kunz ,&nbsp;Marc St-Hilaire","doi":"10.1016/j.adhoc.2025.103942","DOIUrl":"10.1016/j.adhoc.2025.103942","url":null,"abstract":"<div><div>Mobile Ad-hoc Networks face challenges in achieving efficient multicasting due to dynamic topology changes and unreliable links. Existing multicast approaches either suffer from low packet delivery ratio or high overhead. These approaches rely on simple metrics like hop count to find the optimal path to the destination. Once the path is selected, all packets are sent over the same path as long as it remains available. However, a path that is deemed optimal at a specific instance of time may not retain its optimality at a subsequent moment due to node mobility. Moreover, using a metric like hop count that does not consider link quality can lead to poor packet delivery ratio, as it can favor an unreliable path over a reliable one just because it is the shortest. To tackle these concerns, a Q-Learning Adaptive Multicast Ad-hoc On-Demand Distance Vector routing protocol is proposed. It is an adaptive and bandwidth-efficient solution that utilizes link reliability as a routing metric instead of hop count, aiming to build a more stable multicast tree. By leveraging Q-learning principles, the proposed protocol continuously updates path costs to detect any deterioration. Additionally, the protocol dynamically explores the network using periodic group hello messages, enabling the identification of alternative paths and proactively switches to them if path costs deteriorate. Simulations conducted in Network Simulator 3 demonstrate the superiority of the proposed protocol over the traditional Multicast Ad-hoc On-Demand Distance Vector protocol. Furthermore, it outperforms a modified version, called Multicast Ad-hoc On-Demand Distance Vector-Route Reliability, that uses link reliability as a metric, demonstrating enhanced packet delivery ratio and reduced multicast-related overhead.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103942"},"PeriodicalIF":4.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321792","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":"Indoor localization using router-to-router RSSI and transfer learning for dynamic environments","authors":"Liuyi Yang,&nbsp;Patrick Finnerty,&nbsp;Chikara Ohta","doi":"10.1016/j.adhoc.2025.103938","DOIUrl":"10.1016/j.adhoc.2025.103938","url":null,"abstract":"<div><div>With the increasing demand for indoor localization, received signal strength indicator (RSSI)-based fingerprint localization has gained widespread attention due to its low equipment costs. Traditional methods only use RSSI data collected from user devices to train localization models, but the coarse granularity of RSSI often limits accuracy. Additionally, changes in the environment, such as door opening and closing or furniture rearrangements, can render these models ineffective. While resource-intensive and time-consuming, data re-collection and model retraining are essential for capturing updated signal characteristics after environment changes, ensuring the model remains accurate and effective. To enhance localization accuracy, we expand on traditional approaches by incorporating RSSI data measured between wireless routers as additional fingerprint features, achieving nearly a 20% accuracy improvement. Furthermore, we address the challenges of dynamic environments by introducing a multi-task domain-adversarial transfer learning method, which extracts consistent features before and after environment changes. Transfer learning allows us to leverage knowledge from the environment before the change, thereby reducing the need for data re-collection after the change. Experiment results from simulated, real-world, and open dataset environments confirm the effectiveness of the proposed method in dynamic indoor localization. Our approach reduced the mean error distance (MED) by 35%, 44%, and 28%, respectively, with only 16%, 20%, and 17% of the data re-collected.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":"178 ","pages":"Article 103938"},"PeriodicalIF":4.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297187","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}