Ad Hoc Networks最新文献

{"title":"Residual multiscale attention based modulated convolutional neural network for radio link failure prediction in 5G","authors":"Ranjitham Govindasamy ,&nbsp;Sathish Kumar Nagarajan ,&nbsp;Jamuna Rani Muthu ,&nbsp;M. Ramkumar","doi":"10.1016/j.adhoc.2024.103679","DOIUrl":"10.1016/j.adhoc.2024.103679","url":null,"abstract":"<div><div>In the realm of the 5 G environment, Radio Access Networks (RANs) are integral components, comprising radio base stations communicating through wireless radio links. However, this communication is susceptible to environmental variations, particularly weather conditions, leading to potential radio link failures that disrupt services. Addressing this, proactive failure prediction and resource allocation adjustments become crucial. Existing approaches neglect the relationship between weather changes and radio communication, lacking a holistic view despite their effectiveness in predicting radio link failures for one day. Therefore, the Dynamic Arithmetic Residual Multiscale attention-based Modulated Convolutional Neural Network (DARMMCNN) is proposed. This innovative model considers radio link data and weather changes as key metrics for predicting link failures. Notably, the proposed approach extends the prediction span to 5 days, surpassing the limitations of existing one-day prediction methods. In this, input data is collected from the Radio Link Failure (RLF) prediction dataset. Then, the distance correlation and noise elimination are used to improve the quality and relevance of the data. Following that, the sooty tern optimization algorithm is used for feature selection, which contributes to link failures. Next, a multiscale residual attention modulated convolutional neural network is applied for RLF prediction, and a dynamic arithmetic optimization algorithm is accomplished to tune the weight parameter of the network. The proposed work obtains 79.03 %, 65.93 %, and 67.51 % of precision, recall, and F1-score, which are better than existing techniques. The analysis shows that the proposed scheme is appropriate for RLF prediction.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy-efficient hierarchical cluster-based routing strategies for Internet of Nano-Things: Algorithms design and experimental evaluations","authors":"Emre Sahin ,&nbsp;Orhan Dagdeviren ,&nbsp;Mustafa Alper Akkas","doi":"10.1016/j.adhoc.2024.103673","DOIUrl":"10.1016/j.adhoc.2024.103673","url":null,"abstract":"<div><div>Nanodevices (NDs), which are only a few nanometers (nm) in size, need to communicate with each other to perform complex operations. In nanonetworks, this communication typically involves multiple hops, requiring efficient routing protocols. Existing protocols are not well suited for nanonetworks due to their high resource consumption and setup overhead. In this paper, we propose three novel routing protocols for nanodevices. Non-Back Flooding Routing (NBFR) and Layer-Based Flooding Routing (LBFR) aim to reduce unnecessary packet transmission by utilizing distance and layer information based on received signal power. On the other hand, Tree-Based Forwarding Routing (TBFR) is a unicast-based approach that aims to transmit the packet to the destination using the shortest and most reliable path possible through a tree structure. The performance of these proposed methods is compared to well-known methods in terms of packet transmission, energy consumption, end-to-end delay, and setup overhead. TBFR achieved a packet transmission success of 92.95% in topology with the highest density of nanorouters (NRs), while it reached up to 99.57% for fewer nanorouters. Moreover, its end-to-end delay values are much lower than those of multi-path routing protocols. It also consumed one-fifth of the energy compared to its most challenging multi-path competitor, NBFR, regarding packet transmission success. However, for dense nanosensor (NS) topologies, NBFR and LBFR achieved higher packet transmission rates of 87.04% and 86.66%, respectively. Furthermore, in addition to achieving low end-to-end delays, the energy consumption of NBFR is very close to that of TBFR. In summary, the tests show that TBFR is more suitable for communication among nanorouters due to the requirement of building the tree structure, which results in a slightly higher setup overhead. In contrast, NBFR and LBFR are more suitable for communication between nanosensors because of their simplicity and low setup overhead. But, it should be noted that NBFR requires a larger header than the other alternatives.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418712","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 privacy-preserving Self-Supervised Learning-based intrusion detection system for 5G-V2X networks","authors":"Shajjad Hossain ,&nbsp;Sidi-Mohammed Senouci ,&nbsp;Bouziane Brik ,&nbsp;Abdelwahab Boualouache","doi":"10.1016/j.adhoc.2024.103674","DOIUrl":"10.1016/j.adhoc.2024.103674","url":null,"abstract":"<div><div>In light of the ongoing transformation in the automotive industry, driven by the adoption of 5G and the proliferation of connected vehicles, network security has emerged as a critical concern. This is particularly true for the implementation of cutting-edge 5G services such as Network Slicing (NS), Software Defined Networking (SDN), and Multi-access Edge Computing (MEC). As these advanced services become more prevalent, they introduce new vulnerabilities that can be exploited by cyber attackers. Consequently, Network Intrusion Detection Systems (NIDSs) are pivotal in safeguarding vehicular networks against cyber threats. Still, their efficacy hinges on extensive data, which often contains sensitive and confidential information such as vehicle positions and owner’s behaviors, raising privacy concerns. To address this issue, we propose a Privacy-Preserving Self-Supervised Learning (SSL) based Intrusion Detection System for 5G-V2X networks. The majority of works in the literature relying on Federated Learning (FL) and often overlook data labeling on the end devices. Our methodology leverages SSL to pre-train NIDSs using unlabeled data. Post-training is then performed with a minimal amount of labeled data, which can be carefully crafted by an expert. This novel technique allows the training of NIDSs with huge datasets without compromising privacy, consequently enhancing the efficacy of cyber-attack protection. Our innovative SSL pre-training methodology has yielded remarkable results, demonstrating a substantial improvement of up to 9% in accuracy across a diverse range of training dataset sizes, including scenarios with as few as 200 data samples. Our approach highlights the potential to enhance automotive network security significantly, showcasing groundbreaking achievements that set a new standard in the field of automotive cybersecurity.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-agent reinforcement learning for task offloading with hybrid decision space in multi-access edge computing","authors":"Ji Wang,&nbsp;Miao Zhang,&nbsp;Quanjun Yin,&nbsp;Lujia Yin,&nbsp;Yong Peng","doi":"10.1016/j.adhoc.2024.103671","DOIUrl":"10.1016/j.adhoc.2024.103671","url":null,"abstract":"<div><div>Multi-access Edge Computing (MEC) has become a significant technology for supporting the computation-intensive and time-sensitive applications on the Internet of Things (IoT) devices. However, it is challenging to jointly optimize task offloading and resource allocation in the dynamic wireless environment with constrained edge resource. In this paper, we investigate a multi-user and multi-MEC servers system with varying task request and stochastic channel condition. Our purpose is to minimize the total energy consumption and time delay by optimizing the offloading decision, offloading ratio and computing resource allocation simultaneously. As the users are geographically distributed within an area, we formulate the problem of task offloading and resource allocation in MEC system as a partially observable Markov decision process (POMDP) and propose a novel multi-agent deep reinforcement learning (MADRL) -based algorithm to solve it. In particular, two aspects have been modified for performance enhancement: (1) To make fine-grained control, we design a novel neural network structure to effectively handle the hybrid action space arisen by the heterogeneous variables. (2) An adaptive reward mechanism is proposed to reasonably evaluate the infeasible actions and to mitigate the instability caused by manual configuration. Simulation results show the proposed method can achieve <span><math><mrow><mn>7</mn><mo>.</mo><mn>12</mn><mtext>%</mtext><mo>−</mo><mn>20</mn><mo>.</mo><mn>97</mn><mtext>%</mtext></mrow></math></span> performance enhancements compared with the existing approaches.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142328329","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":"Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles","authors":"Zhipeng Cheng ,&nbsp;Lu Liu ,&nbsp;Minghui Liwang ,&nbsp;Ning Chen ,&nbsp;Xuwei Fan","doi":"10.1016/j.adhoc.2024.103667","DOIUrl":"10.1016/j.adhoc.2024.103667","url":null,"abstract":"<div><div>In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315173","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":"Dual-stage machine learning approach for advanced malicious node detection in WSNs","authors":"Osama A. Khashan","doi":"10.1016/j.adhoc.2024.103672","DOIUrl":"10.1016/j.adhoc.2024.103672","url":null,"abstract":"<div><div>Within wireless sensor networks (WSNs), a multitude of vulnerabilities can arise, particularly those originating from malicious nodes (MNs), which lead to compromised data integrity, network stability, and critical application reliability. Although security and energy efficiency remain critical, current MN detection methods are resource-intensive and time-consuming, rendering them unsuitable for constrained WSNs. Although machine learning-based methods excel at detecting MNs, they often incur significant time overhead owing to extensive data transmission and coordination, leading to increased latency and energy consumption within the network. This study introduces DSMND, a novel dual-stage MN detection scheme that harnesses machine learning to enhance MN identification in WSNs. The initial stage uses dynamic threshold detection and decision-tree algorithms at the cluster head (CH) level. This adaptive detection process optimizes CH resource levels, feature counts, and threshold values for efficient MN identification. When thresholds are exceeded, the second stage activates on the server side, employing an advanced MN detection model that seamlessly integrates a hybrid convolutional neural network and a random forest classifier to boost detection accuracy. Leveraging SensorNetGuard, a dataset with diverse node and network features, further enhances reliability. Extensive analysis shows that our scheme achieves up to 99.5 % detection accuracy at the CH level and nearly 100 % at the server side. The average execution time is 124.63 ms, making it 97 % faster than conventional methods. Additionally, DSMND reduces CH power consumption by up to 70 % and extends network lifetime by 2.7 times compared to existing methods. These results confirm the effectiveness of our approach for real-time detection and mitigation of MNs within WSNs.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142322575","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":"Escrow-free and efficient dynamic anonymous privacy-preserving batch verifiable authentication scheme for VANETs","authors":"Girraj Kumar Verma ,&nbsp;Vinay Chamola ,&nbsp;Asheesh Tiwari ,&nbsp;Neeraj Kumar ,&nbsp;Dheerendra Mishra ,&nbsp;Saurabh Rana ,&nbsp;Ahmed Barnawi","doi":"10.1016/j.adhoc.2024.103670","DOIUrl":"10.1016/j.adhoc.2024.103670","url":null,"abstract":"<div><div>To enhance road safety, Vehicular Ad-hoc Networks (VANETs) facilitate the exchange of safety-critical messages between smart vehicles and road traffic authorities. However, VANET’s wireless channels are prone to several attacks, such as replay or modification. Therefore, to protect the links, robust authentication and message integrity mechanisms are required. Previously, several robust authentication schemes have been devised. However, those designs often struggle with complex certificate management, the key escrow problem, and the necessity for secure channels to establish user keys. Additionally, prior methods rely on pseudonyms to ensure user privacy. To implement it, several pseudonyms are stored in the vehicle’s device, which burdens the device. To overcome these limitations, this study introduces an efficient and escrow-free dynamic anonymous authentication scheme tailored for VANETs. By utilizing the paradigm of certificate-based cryptography and fuzzy identity generation, the proposed design eliminates the limitations. Through rigorous security analysis, the proposed design’s effectiveness against various threats is demonstrated. Furthermore, a detailed performance analysis, including computational and communication cost comparisons, showcases the scheme’s feasibility for VANET deployment. An NS-3 simulation further confirms the suitability of the proposed scheme for real-world VANET communication scenarios.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315174","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 method for simultaneously implementing trajectory planning and DAG task scheduling in multi-UAV assisted edge computing","authors":"Wenchao Yang,&nbsp;Yuxing Mao,&nbsp;Xueshuo Chen,&nbsp;Chunxu Chen,&nbsp;Bozheng Lei,&nbsp;Qing He","doi":"10.1016/j.adhoc.2024.103668","DOIUrl":"10.1016/j.adhoc.2024.103668","url":null,"abstract":"<div><div>UAV-assisted edge computing(UEC) as a new framework is able to provide computing services to remote areas. However, facing computationally intensive tasks with huge computation time forces them to hover near the user’s devices(UDs) for long periods of time. To better utilize the available arithmetic resources and reduce the computation time of UAVs, it is imperative to introduce directed acyclic graph (DAG) task scheduling into the UEC framework. Therefore, this article proposes a DAG-type task-driven trajectory planning (DAG-TDTP) model, which can plan UAV routes while scheduling DAG subtasks between UAVs that offload from UDs. To implement the DAG-TDTP model, we propose a distance-based heterogeneous earliest-finish-time (D-HEFT) algorithm and a time segmentation method based on the cooperative task offloading matrix. To stimulate the potential of the DAG-TDTP model in reducing energy consumption, we propose a genetic algorithm based on temporary key nodes (TKNGA) for the proposed model. Through simulation analysis, we verify the superiority of the proposed model in reducing UAV system energy consumption and the superiority of TKNGA compared to other algorithms.</div></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142357129","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":"TAVA: Traceable anonymity-self-controllable V2X Authentication over dynamic multiple charging-service providers","authors":"Qingwen Han ,&nbsp;Tianlin Yang ,&nbsp;Yao Li ,&nbsp;Yongsheng Zhao ,&nbsp;Shuai Zhang ,&nbsp;Guoqiang Zu","doi":"10.1016/j.adhoc.2024.103666","DOIUrl":"10.1016/j.adhoc.2024.103666","url":null,"abstract":"<div><p>The widespread deployment of Electric vehicles (EVs) leads to an increasing demand for charging piles and corresponding charging service (CS) from CS providers (CSPs). Pseudonym-based authentication mechanisms have been designed to resist the attacks which exploit the charging-authentication information to infer EV users’ identities and their driving routes. However, these existing mechanisms generated EV users' pseudonyms by relying on a trusted third entity, which affects the authentication system's resilience and EV user privacy-preservation.</p><p>To this end, this paper proposes a <em>T</em>raceable <em>A</em>nonymity-self-controllable <em>V</em>2X <em>A</em>uthentication (TAVA) scheme for the multiple-CSP (forming a CSP set) scenario, where each CSP independently manages its own CPs and a CSP randomly joins or leaves the CSP set. TAVA has a series of security capabilities. (1) First, it allows the mutual authentication between an EV user and a CP, while preserving EV user privacy and also assuring forward and backward security. This capability is achieved by using the multi-party computation technique to let all CSPs join the process of generating EV-users’ credentials but each CSP knows nothing about the credentials. (2) Secondly, TAVA has the capabilities of self-controllable anonymity and unlinkability by allowing each EV user to self-generate verifiable and unlinkable one-time pseudonyms based on bilinear- mapping technique. (3) At last, each EV user under TAVA is traceable. It is achieved by applying the two-factor authentication technique in TAVA and linking the one-time pseudonym to the two factors, namely, the credential and the EV user's biometric characteristics with low entropy rates. Note that all these security capabilities are achieved with less performance degradation in terms of communication and storage overheads in the dynamic environment. We conduct the informal and formal analysis of security capabilities and also make performance evaluations. The results indicate that, compared with the latest works, the computation overhead of the mutual authentication in TAVA is reduced by up to 89 %.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270760","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":"RL-based mobile edge computing scheme for high reliability low latency services in UAV-aided IIoT networks","authors":"Zahraa Sweidan ,&nbsp;Sanaa Sharafeddine ,&nbsp;Mariette Awad","doi":"10.1016/j.adhoc.2024.103646","DOIUrl":"10.1016/j.adhoc.2024.103646","url":null,"abstract":"<div><p>The prevailing adoption of Internet of Things paradigm is giving rise to a wide range of use cases in various vertical industries including remote health, industrial automation, and smart agriculture. However, the realization of such use cases is mainly challenged due to their stringent service requirements of high reliability and low latency. This challenge grows further when the service entails processing collected data for informed decision making. In this work, we consider a field of industrial Internet of Things devices that generate computational tasks and are covered by a nearby base station equipped with an edge server. The edge server offers fast processing to the devices’ tasks to help in meeting their latency requirement. Due to statistical wireless variability, the task data may not be correctly delivered in time for processing. To this end, we utilize an unmanned aerial vehicle as a supplemental edge server that tailors its trajectory and flies closer to the IIoT devices to ensure a highly reliable task delivery based on the given task reliability constraints. We formulate the problem as a Markov Decision Process, and propose a deep reinforcement learning-based approach using proximal policy optimization to optimize the unmanned aerial vehicle trajectory and scheduling devices to offload their data for processing. We present simulation results for various system scenarios to illustrate the effectiveness of the proposed solution as compared to several baseline approaches.</p></div>","PeriodicalId":55555,"journal":{"name":"Ad Hoc Networks","volume":null,"pages":null},"PeriodicalIF":4.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243787","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}