Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843203
Jihyeon Lee, Sangwon Seo, Taehun Yang, Soochang Park
This paper proposes a novel scheme to detect and localize the spy cameras based on AI algorithm based raw traffic analytics, named AI-aided Hidden Camera Locator (AHCL). In AHCL, the video streaming data are filtered via the SVM (support vector machine) algorithm to quickly monitor whole raw network traffic from a router to the networks first. Then, gathered traffic data are denoised by the Denoising Autoencoder (DAE) technique to improve the data quality of classification for localization, where a camera transmits video streaming. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.5% positioning accuracy of camera detection with the Ensemble Neural Networks (NNs).
{"title":"AI-aided Hidden Camera Detection and Localization based on Raw IoT Network Traffic","authors":"Jihyeon Lee, Sangwon Seo, Taehun Yang, Soochang Park","doi":"10.1109/LCN53696.2022.9843203","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843203","url":null,"abstract":"This paper proposes a novel scheme to detect and localize the spy cameras based on AI algorithm based raw traffic analytics, named AI-aided Hidden Camera Locator (AHCL). In AHCL, the video streaming data are filtered via the SVM (support vector machine) algorithm to quickly monitor whole raw network traffic from a router to the networks first. Then, gathered traffic data are denoised by the Denoising Autoencoder (DAE) technique to improve the data quality of classification for localization, where a camera transmits video streaming. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.5% positioning accuracy of camera detection with the Ensemble Neural Networks (NNs).","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132709445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843398
Chinh Tran, M. Mehmet-Ali
Future vehicles are expected to generate large amounts of data which may need to be off-loaded to a proximate server for processing. This led to the introduction of vehicular clouds (VC), which proposes that computing is done at nearby vehicles. However, as the vehicles may leave and join the VC randomly, the computing services of VC are time-varying, which may cause service interruptions. This work analytically evaluates the performance of the VCs under a service strategy that overcomes the interruptions caused by resource volatility. We use order statistics to derive the probability distribution of the number of vehicle arrivals to assign all the tasks of a job, the upper and lower bounds of mean job completion time, and the probability density function of the completion time of the longest task. Finally, we present the numerical results for the analysis and the simulation results to show the correctness of the analysis.
{"title":"Towards Job Completion Time in Vehicular Cloud by Overcoming Resource Volatility","authors":"Chinh Tran, M. Mehmet-Ali","doi":"10.1109/LCN53696.2022.9843398","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843398","url":null,"abstract":"Future vehicles are expected to generate large amounts of data which may need to be off-loaded to a proximate server for processing. This led to the introduction of vehicular clouds (VC), which proposes that computing is done at nearby vehicles. However, as the vehicles may leave and join the VC randomly, the computing services of VC are time-varying, which may cause service interruptions. This work analytically evaluates the performance of the VCs under a service strategy that overcomes the interruptions caused by resource volatility. We use order statistics to derive the probability distribution of the number of vehicle arrivals to assign all the tasks of a job, the upper and lower bounds of mean job completion time, and the probability density function of the completion time of the longest task. Finally, we present the numerical results for the analysis and the simulation results to show the correctness of the analysis.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130994439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843478
Hauke Heseding, M. Zitterbart
Volumetric Distributed Denial of Service attacks forcefully disrupt the availability of online services by congesting network links with arbitrary high-volume traffic. This brute force approach has collateral impact on the upstream network infrastructure, making early attack traffic removal a key objective. To reduce infrastructure load and maintain service availability, we introduce ReCEIF, a topology-independent mitigation strategy for early, rule-based ingress filtering leveraging deep reinforcement learning. ReCEIF utilizes hierarchical heavy hitters to monitor traffic distribution and detect subnets that are sending high-volume traffic. Deep reinforcement learning subsequently serves to refine hierarchical heavy hitters into effective filter rules that can be propagated upstream to discard traffic originating from attacking systems. Evaluating all filter rules requires only a single clock cycle when utilizing fast ternary content-addressable memory, which is commonly available in software defined networks. To outline the effectiveness of our approach, we conduct a comparative evaluation to reinforcement learning-based router throttling.
{"title":"ReCEIF: Reinforcement Learning-Controlled Effective Ingress Filtering","authors":"Hauke Heseding, M. Zitterbart","doi":"10.1109/LCN53696.2022.9843478","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843478","url":null,"abstract":"Volumetric Distributed Denial of Service attacks forcefully disrupt the availability of online services by congesting network links with arbitrary high-volume traffic. This brute force approach has collateral impact on the upstream network infrastructure, making early attack traffic removal a key objective. To reduce infrastructure load and maintain service availability, we introduce ReCEIF, a topology-independent mitigation strategy for early, rule-based ingress filtering leveraging deep reinforcement learning. ReCEIF utilizes hierarchical heavy hitters to monitor traffic distribution and detect subnets that are sending high-volume traffic. Deep reinforcement learning subsequently serves to refine hierarchical heavy hitters into effective filter rules that can be propagated upstream to discard traffic originating from attacking systems. Evaluating all filter rules requires only a single clock cycle when utilizing fast ternary content-addressable memory, which is commonly available in software defined networks. To outline the effectiveness of our approach, we conduct a comparative evaluation to reinforcement learning-based router throttling.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115154518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843821
Leonard Bradatsch, M. Haeberle, Benjamin Steinert, F. Kargl, M. Menth
Service Function Chaining (SFC) enables dynamic steering of traffic through a set of service functions based on classification of packets, allowing network operators fine-grained and flexible control of packet flows. New paradigms like Zero Trust (ZT) pose additional requirements to the security of network architectures. This includes client authentication, confidentiality, and integrity throughout the whole network, while also being able to perform operations on the unencrypted payload of packets. However, these requirements are only partially addressed in existing SFC literature. Therefore, we first present a comprehensive analysis of the security requirements for SFC architectures. Based on this analysis, we propose a concept towards the fulfillment of the requirements while maintaining the flexibility of SFC. In addition, we provide and evaluate a proof of concept implementation, and discuss the implications of the design choices.
SFC (Service Function chains)是一种基于报文分类，通过一组业务功能对流量进行动态引导的技术，使网络运营商能够对报文流进行细粒度、灵活的控制。像零信任(ZT)这样的新范式对网络架构的安全性提出了额外的要求。这包括整个网络中的客户端身份验证、机密性和完整性，同时还能够对未加密的数据包有效负载执行操作。然而，这些要求在现有的SFC文献中只得到部分解决。因此，我们首先对SFC架构的安全需求进行了全面的分析。基于这一分析，我们提出了一个在保持SFC灵活性的同时满足需求的概念。此外，我们提供并评估了概念实现的证明，并讨论了设计选择的影响。
{"title":"Secure Service Function Chaining in the Context of Zero Trust Security","authors":"Leonard Bradatsch, M. Haeberle, Benjamin Steinert, F. Kargl, M. Menth","doi":"10.1109/LCN53696.2022.9843821","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843821","url":null,"abstract":"Service Function Chaining (SFC) enables dynamic steering of traffic through a set of service functions based on classification of packets, allowing network operators fine-grained and flexible control of packet flows. New paradigms like Zero Trust (ZT) pose additional requirements to the security of network architectures. This includes client authentication, confidentiality, and integrity throughout the whole network, while also being able to perform operations on the unencrypted payload of packets. However, these requirements are only partially addressed in existing SFC literature. Therefore, we first present a comprehensive analysis of the security requirements for SFC architectures. Based on this analysis, we propose a concept towards the fulfillment of the requirements while maintaining the flexibility of SFC. In addition, we provide and evaluate a proof of concept implementation, and discuss the implications of the design choices.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133874586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843650
Abdelmounaim Bouroudi, A. Outtagarts, Y. H. Aoul
Network slicing, also known as the virtual network embedding (VNE) problem, is an NP-hard optimization problem. Compared to traditional approaches, the methods relying on deep reinforcement learning yield better performance without exhibiting issues such as stacking at local minima and/or solutions’ space exploration limits. These algorithms present, however, different performances according to the employed approach, and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose the adoption of the best algorithm, from a selection of learning strategies, in terms of reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network by dynamically selecting the best solution for a specific scenario. Our solution proved its efficiency and managed to dynamically select the best algorithm in terms of the best acceptance ratio of the deployed services and outperform all the standalone algorithms.
{"title":"Robust Deep Reinforcement Learning Algorithm for VNF-FG Embedding","authors":"Abdelmounaim Bouroudi, A. Outtagarts, Y. H. Aoul","doi":"10.1109/LCN53696.2022.9843650","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843650","url":null,"abstract":"Network slicing, also known as the virtual network embedding (VNE) problem, is an NP-hard optimization problem. Compared to traditional approaches, the methods relying on deep reinforcement learning yield better performance without exhibiting issues such as stacking at local minima and/or solutions’ space exploration limits. These algorithms present, however, different performances according to the employed approach, and the problem to be treated, resulting in robustness problems. To overcome these limits, we propose the adoption of the best algorithm, from a selection of learning strategies, in terms of reward and sample efficiency at each time step. The proposed strategy acts as a meta-algorithm that brings more robustness to the network by dynamically selecting the best solution for a specific scenario. Our solution proved its efficiency and managed to dynamically select the best algorithm in terms of the best acceptance ratio of the deployed services and outperform all the standalone algorithms.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134069258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843733
Salwa Abougamila, Mohammed Elmorsy, E. Elmallah
In this paper, we deal with a wireless sensor network (WSN) infrastructure management problem where a provider wants to partition a network into a given number of node-disjoint subgraphs (called slices) for running different user applications. Nodes in the given infrastructure use energy harvesting for prolonged service time. The nodes manage fluctuations in their stored energy by adjusting their transmission range. We assume that each node is assigned an importance weight, and model the overall network using a probabilistic graph. In this context, we formalize a problem, denoted k-WBS-RU (for k weighted balanced slices with range uncertainty), to partition the network into k slices subject to some connectivity and operation constraints. We devise a solution to the problem, and present numerical results on the quality of the obtained slices. We also discuss an application of the proposed framework and solution when the assigned weights are derived from an area coverage application.
{"title":"On Slicing Weighted Energy-Harvesting Wireless Sensing Networks with Transmission Range Uncertainty","authors":"Salwa Abougamila, Mohammed Elmorsy, E. Elmallah","doi":"10.1109/LCN53696.2022.9843733","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843733","url":null,"abstract":"In this paper, we deal with a wireless sensor network (WSN) infrastructure management problem where a provider wants to partition a network into a given number of node-disjoint subgraphs (called slices) for running different user applications. Nodes in the given infrastructure use energy harvesting for prolonged service time. The nodes manage fluctuations in their stored energy by adjusting their transmission range. We assume that each node is assigned an importance weight, and model the overall network using a probabilistic graph. In this context, we formalize a problem, denoted k-WBS-RU (for k weighted balanced slices with range uncertainty), to partition the network into k slices subject to some connectivity and operation constraints. We devise a solution to the problem, and present numerical results on the quality of the obtained slices. We also discuss an application of the proposed framework and solution when the assigned weights are derived from an area coverage application.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133905326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843470
Atefeh Talebian, Alvin Valera, Jyoti Sahni, W. Seah
Low latency is critical to applications such as control of unmanned aerial vehicles. Such latency-sensitive services can be hosted closer to the user at the fog layer which can reduce overall latency through the reduction of transmission time and network congestion. To keep the latency low for mobile users connected to services deployed at the fog, these services need to be constantly migrated to follow the users. Unlike the cloud nodes, fog nodes are less reliable and are therefore subject to higher failure rate. In this paper, we propose an enhancement to the post-copy live migration algorithm to make it robust against failure. Simulation results show that robust migration reduces total migration time between 10-26% and downtime between 2-23% compared to non-robust migration. Furthermore, when the bandwidth to the backup node is lower, robust migration provides further improvement in both metrics.
{"title":"Robust Intra-Slice Migration in Fog Computing","authors":"Atefeh Talebian, Alvin Valera, Jyoti Sahni, W. Seah","doi":"10.1109/LCN53696.2022.9843470","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843470","url":null,"abstract":"Low latency is critical to applications such as control of unmanned aerial vehicles. Such latency-sensitive services can be hosted closer to the user at the fog layer which can reduce overall latency through the reduction of transmission time and network congestion. To keep the latency low for mobile users connected to services deployed at the fog, these services need to be constantly migrated to follow the users. Unlike the cloud nodes, fog nodes are less reliable and are therefore subject to higher failure rate. In this paper, we propose an enhancement to the post-copy live migration algorithm to make it robust against failure. Simulation results show that robust migration reduces total migration time between 10-26% and downtime between 2-23% compared to non-robust migration. Furthermore, when the bandwidth to the backup node is lower, robust migration provides further improvement in both metrics.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130906139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843337
Jiangbin Chen, Yujing Liu, Shuhui Chen, Xiangquan Shi
AS relationships are the basis for studying Internet security, route hijacking, route leakage, etc. To obtain more complete AS relationships, using machine learning (ML) models to learn the similarity between adjacent link groups and predict hidden links is a method that can obtain more complete AS relationships. The features selected by the ML model have a large impact on the accuracy of the prediction results, and we extract 10 ML features by combining the actual geographic location information of AS. After our optimization, the accuracy of the prediction model reaches 91.57%. In the classification of hidden link types, we oversample the small sample type data and optimize the classifier, and the classification accuracy of hidden link categories reaches 97.42%. The recall rate of p2c and c2p links improved by 24.29% and 7.17%, respectively. We found that the hidden links caused the change of network traffic transmission routes by the change of "Critical AS" in the AS network. AS 3549 has the highest number of effective paths, and the network traffic prefers to choose the AS with a lower hierarchy for forwarding.
{"title":"Research on the derivation of AS hidden links and the Discovery of Critical AS","authors":"Jiangbin Chen, Yujing Liu, Shuhui Chen, Xiangquan Shi","doi":"10.1109/LCN53696.2022.9843337","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843337","url":null,"abstract":"AS relationships are the basis for studying Internet security, route hijacking, route leakage, etc. To obtain more complete AS relationships, using machine learning (ML) models to learn the similarity between adjacent link groups and predict hidden links is a method that can obtain more complete AS relationships. The features selected by the ML model have a large impact on the accuracy of the prediction results, and we extract 10 ML features by combining the actual geographic location information of AS. After our optimization, the accuracy of the prediction model reaches 91.57%. In the classification of hidden link types, we oversample the small sample type data and optimize the classifier, and the classification accuracy of hidden link categories reaches 97.42%. The recall rate of p2c and c2p links improved by 24.29% and 7.17%, respectively. We found that the hidden links caused the change of network traffic transmission routes by the change of \"Critical AS\" in the AS network. AS 3549 has the highest number of effective paths, and the network traffic prefers to choose the AS with a lower hierarchy for forwarding.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127218523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843744
T. Zimmermann, Eric Lanfer, N. Aschenbruck
In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.
{"title":"Developing a Scalable Network of High-Interaction Threat Intelligence Sensors for IoT Security","authors":"T. Zimmermann, Eric Lanfer, N. Aschenbruck","doi":"10.1109/LCN53696.2022.9843744","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843744","url":null,"abstract":"In the last decade, numerous Industrial IoT systems have been deployed. Attack vectors and security solutions for these are an active area of research. However, to the best of our knowledge, only very limited insight in the applicability and real-world comparability of attacks exists. To overcome this widespread problem, we have developed and realized an approach to collect attack traces at a larger scale. An easily deployable system integrates well into existing networks and enables the investigation of attacks on unmodified commercial devices.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131843401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-09-26DOI: 10.1109/LCN53696.2022.9843737
Ankur Nahar, Himani Sikarwar, D. Das
This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges. Experimental results show that AlcFier performs efficiently, improves cluster stability, reduces transmission delays, and improves throughput compared with the state-of-the-art routing protocols.
{"title":"AlcFier: Adaptive Self-Learning Classifier for Routing in Vehicular Ad-Hoc Network","authors":"Ankur Nahar, Himani Sikarwar, D. Das","doi":"10.1109/LCN53696.2022.9843737","DOIUrl":"https://doi.org/10.1109/LCN53696.2022.9843737","url":null,"abstract":"This paper presents an adaptive self-learning classifier-based clustering algorithm called AlcFier, to support scalability, enhance the stability of the network topology, and provide efficient routing. We incorporate mobility and channel characteristics (i.e., orientation, adjacency, link availability, queue occupancy, and signal-to-noise ratio) into the clustering approach as a channel-aware metric to provide a new direction to the taxonomy of the approaches employed to handle cluster head election, cluster affiliation, and cluster administration challenges. Experimental results show that AlcFier performs efficiently, improves cluster stability, reduces transmission delays, and improves throughput compared with the state-of-the-art routing protocols.","PeriodicalId":303965,"journal":{"name":"2022 IEEE 47th Conference on Local Computer Networks (LCN)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134182099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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