Wireless communication continuously gains importance in the industrial environment. Mobile communication, for example between Automated Guided Vehicles (AGVs), is a particularly challenging use case. The AGVs move in the industrial environment and require a connection to a central controller by means of wireless communication technologies. Most AGVs can not move without this connection. On the other hand the AGVs mobility effects the available communication channels. Therefore mobility and communication are directly linked in this use case. Common mobility and signal propagation models are not suitable to model these links and the emerging AGV behavior. In this work a new model structure for the simulation and evaluation of mobile wireless networks in the industrial context is proposed. A newly proposed mobility model is the core of this new model structure, which enables the evaluation of the communication networks effects on the mobile systems performance and behavior.
{"title":"Advanced Models for the Simulation of AGV Communication in Industrial Environments: Model proposal and Demonstration","authors":"C. Sauer, M. Schmidt, M. Sliskovic","doi":"10.1145/3416014.3424604","DOIUrl":"https://doi.org/10.1145/3416014.3424604","url":null,"abstract":"Wireless communication continuously gains importance in the industrial environment. Mobile communication, for example between Automated Guided Vehicles (AGVs), is a particularly challenging use case. The AGVs move in the industrial environment and require a connection to a central controller by means of wireless communication technologies. Most AGVs can not move without this connection. On the other hand the AGVs mobility effects the available communication channels. Therefore mobility and communication are directly linked in this use case. Common mobility and signal propagation models are not suitable to model these links and the emerging AGV behavior. In this work a new model structure for the simulation and evaluation of mobile wireless networks in the industrial context is proposed. A newly proposed mobility model is the core of this new model structure, which enables the evaluation of the communication networks effects on the mobile systems performance and behavior.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116086534","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}
Zhaowei Ma, F. Richard Yu, Xiantao Jiang, A. Boukerche
The extensive use of vehicles, especially with the emergency of autonomous driving, urges the improvement of traffic safety. Prevalent approaches, such as Global Positioning System (GPS), Internet of Things (IoT) system and Artificial Intelligence (AI), have demonstrated their strength in preventing road accidents, with the support of trustworthy data. However, in vehicular ad hoc networks (VANETs), data transmission and storage are unreliable due to various constraints such as limited physical resource and unsteady topology. Distributed schemes are widely applied in VANETs to enforce multifold protection on vehicular data. In particular, Blockchain has become a promising approach, as it implements the real-sense distributed solution with consensus algorithm and distributed ledger. To this end, we propose a novel system in this paper, which employs Blockchain technology to consolidate the traffic information sharing in VANETs and holds profound significance for intelligent applications. Our system focuses on sharing real-time visual traffic information at the frame level via Blockchain in VANETs. Integrity verification of frames based on their sequences and timestamps is imposed prior to the consensus in Blockchain, coupled with digital watermarking to protect the multimedia traffic data. Improved efficiency and reliability of sharing are achieved by the system dynamically adjusting transaction volume in terms of the frame type and number. With the fault tolerance and immutability of Blockchain, our proposal can solidly protect the traffic information sharing against vandalization in VANETs, and confidently escort the traffic with trustworthy safety guidance.
{"title":"Trustworthy Traffic Information Sharing Secured via Blockchain in VANETs","authors":"Zhaowei Ma, F. Richard Yu, Xiantao Jiang, A. Boukerche","doi":"10.1145/3416014.3424601","DOIUrl":"https://doi.org/10.1145/3416014.3424601","url":null,"abstract":"The extensive use of vehicles, especially with the emergency of autonomous driving, urges the improvement of traffic safety. Prevalent approaches, such as Global Positioning System (GPS), Internet of Things (IoT) system and Artificial Intelligence (AI), have demonstrated their strength in preventing road accidents, with the support of trustworthy data. However, in vehicular ad hoc networks (VANETs), data transmission and storage are unreliable due to various constraints such as limited physical resource and unsteady topology. Distributed schemes are widely applied in VANETs to enforce multifold protection on vehicular data. In particular, Blockchain has become a promising approach, as it implements the real-sense distributed solution with consensus algorithm and distributed ledger. To this end, we propose a novel system in this paper, which employs Blockchain technology to consolidate the traffic information sharing in VANETs and holds profound significance for intelligent applications. Our system focuses on sharing real-time visual traffic information at the frame level via Blockchain in VANETs. Integrity verification of frames based on their sequences and timestamps is imposed prior to the consensus in Blockchain, coupled with digital watermarking to protect the multimedia traffic data. Improved efficiency and reliability of sharing are achieved by the system dynamically adjusting transaction volume in terms of the frame type and number. With the fault tolerance and immutability of Blockchain, our proposal can solidly protect the traffic information sharing against vandalization in VANETs, and confidently escort the traffic with trustworthy safety guidance.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635957","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}
Internet of Underwater Things (IoUTs) has gained increased momentum thanks to the advancements in underwater nodes, sensing, and communication technologies. This novel paradigm has tremendous potential to empower smart ocean applications. However, the harsh and dynamic nature of the underwater environment and underwater communication, the stringent requirements of underwater applications, and the difficulty and cost for IoUT management and maintenance have limited the development and application of IoUTs. In this regard, machine learning has been proposed to create self-adaptive IoUTs and boost the performance of smart oceans applications. In this paper, we shed light on the design of machine learning models for the on-the-fly intelligent and autonomous management of IoUT networking parameters and configurations aimed at boosting data delivery. We discuss the recent proposals for IoUT network management and how machine learning algorithms can improve such solutions at different networking layers. Finally, we point out some future research directions in need of further attention.
{"title":"Machine Learning for Self-Adaptive Internet of Underwater Things","authors":"Rodolfo W. L. Coutinho","doi":"10.1145/3416014.3424615","DOIUrl":"https://doi.org/10.1145/3416014.3424615","url":null,"abstract":"Internet of Underwater Things (IoUTs) has gained increased momentum thanks to the advancements in underwater nodes, sensing, and communication technologies. This novel paradigm has tremendous potential to empower smart ocean applications. However, the harsh and dynamic nature of the underwater environment and underwater communication, the stringent requirements of underwater applications, and the difficulty and cost for IoUT management and maintenance have limited the development and application of IoUTs. In this regard, machine learning has been proposed to create self-adaptive IoUTs and boost the performance of smart oceans applications. In this paper, we shed light on the design of machine learning models for the on-the-fly intelligent and autonomous management of IoUT networking parameters and configurations aimed at boosting data delivery. We discuss the recent proposals for IoUT network management and how machine learning algorithms can improve such solutions at different networking layers. Finally, we point out some future research directions in need of further attention.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126056564","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}
Several emerging applications for the Internet of Things, vehicular networks, or decentralized communication using smartphones rely on Mobile Ad hoc Networks (MANETs). These networks are temporary deployments of nodes equipped with infrastructure-less wireless communication. MANETs operate in highly dynamic conditions where nodes move at will, interferences are a constant and density is heterogeneous. Routing is a fundamental operations in MANETs. Our evaluation of existing routing protocol for MANETs shows that, while proactive routing protocols are suitable for highly dynamic networks, reactive routing protocols perform best in dense and more static scenarios. No protocol alone can systematically perform well when density is heterogeneous. We propose RoVy, a self-aware adaptive approach for routing in heterogeneous MANETs. Based on independent estimations of density and mobility, RoVy allows nodes to automatically switch between AODV, a reactive routing protocol and DSDV, a proactive protocol. Interoperability protocols support the integration of AODV and DSDV in a single heterogeneous MANET. RoVy maintains a dissemination overlay to speed-up route discovery and improves the emergence of alternative routes to destination nodes. Our simulations of the full network stack with 1,000 nodes shows that RoVy outperforms singular routing protocols in terms of performance, costs and reliability.
{"title":"Reactive Overlays for Adaptive Routing in Mobile Ad hoc Networks","authors":"Raziel Carvajal Gómez, E. Rivière","doi":"10.1145/3416014.3424608","DOIUrl":"https://doi.org/10.1145/3416014.3424608","url":null,"abstract":"Several emerging applications for the Internet of Things, vehicular networks, or decentralized communication using smartphones rely on Mobile Ad hoc Networks (MANETs). These networks are temporary deployments of nodes equipped with infrastructure-less wireless communication. MANETs operate in highly dynamic conditions where nodes move at will, interferences are a constant and density is heterogeneous. Routing is a fundamental operations in MANETs. Our evaluation of existing routing protocol for MANETs shows that, while proactive routing protocols are suitable for highly dynamic networks, reactive routing protocols perform best in dense and more static scenarios. No protocol alone can systematically perform well when density is heterogeneous. We propose RoVy, a self-aware adaptive approach for routing in heterogeneous MANETs. Based on independent estimations of density and mobility, RoVy allows nodes to automatically switch between AODV, a reactive routing protocol and DSDV, a proactive protocol. Interoperability protocols support the integration of AODV and DSDV in a single heterogeneous MANET. RoVy maintains a dissemination overlay to speed-up route discovery and improves the emergence of alternative routes to destination nodes. Our simulations of the full network stack with 1,000 nodes shows that RoVy outperforms singular routing protocols in terms of performance, costs and reliability.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121209440","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}
Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang, J. A. Calero
To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.
为了提高搜救(SAR)行动中人类检测的速度和准确性，本文通过扩展You Only Look Once (YOLO)算法，提出了一种新型的高效机器学习授权系统，该系统设计并部署在嵌入式系统上。该方法已在Jetson AGX Xavier平台上进行了实际条件下的评估，结果表明该系统在准确性、速度和可移植性方面都达到了良好的平衡。此外，该系统还展示了其在低光条件下、不同高度和坐姿(如坐、走和跑)下对无人机(UAV)接收的红外图像进行低像素人体检测的弹性。该方法在受限环境下实现了89.26%的准确率和24.6 FPS，突破了实时目标识别的障碍。
{"title":"Real-Time Low-Pixel Infrared Human Detection From Unmanned Aerial Vehicles","authors":"Ignacio Martinez-Alpiste, Gelayol Golcarenarenji, Qi Wang, J. A. Calero","doi":"10.1145/3416014.3424600","DOIUrl":"https://doi.org/10.1145/3416014.3424600","url":null,"abstract":"To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and deployed on an embedded system. The proposed approach has been evaluated under real-world conditions on a Jetson AGX Xavier platform and the results have shown a well-balanced system in terms of accuracy, speed and portability. Moreover, the system demonstrates its resilience to perform low-pixel human detection on infrared images received from an Unmanned Aerial Vehicle (UAV) at low-light conditions, different altitudes and postures such as sitting, walking and running. The proposed approach has achieved in a constrained environment a total of 89.26% of accuracy and 24.6 FPS, surpassing the barrier of real-time object recognition.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115889915","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}
Yuanhao Zhao, Xuting Duan, Daxin Tian, Zhengguo Sheng, Victor C. M. Leung
High precision positioning in non-open area has always been a bottleneck in the development of V2X. In order to ensure the positioning performance of V2X in non-open area, this paper proposes a vehicle fusion localization method based on Channel State Information (CSI). In the proposed method, a positioning framework for on-board unit (OBU) and road-side unit (RSU) is established based on the communication characteristics of V2X. Meanwhile, the algorithms for the operation of OBU and RSU are given respectively. On this basis, the proposed method uses CSI to calculate the signal flight time, and combines with the least square method to locate the vehicle on the basis of communication equipment. To improve the reliability of CSI data analysis and solve the problem of CSI analysis under multipath propagation, the traditional optimization model is solved by a quadratic convex programming method based on algebraic optimization.
{"title":"Vehicle Fusion Positioning Model based on CSI","authors":"Yuanhao Zhao, Xuting Duan, Daxin Tian, Zhengguo Sheng, Victor C. M. Leung","doi":"10.1145/3416014.3424583","DOIUrl":"https://doi.org/10.1145/3416014.3424583","url":null,"abstract":"High precision positioning in non-open area has always been a bottleneck in the development of V2X. In order to ensure the positioning performance of V2X in non-open area, this paper proposes a vehicle fusion localization method based on Channel State Information (CSI). In the proposed method, a positioning framework for on-board unit (OBU) and road-side unit (RSU) is established based on the communication characteristics of V2X. Meanwhile, the algorithms for the operation of OBU and RSU are given respectively. On this basis, the proposed method uses CSI to calculate the signal flight time, and combines with the least square method to locate the vehicle on the basis of communication equipment. To improve the reliability of CSI data analysis and solve the problem of CSI analysis under multipath propagation, the traditional optimization model is solved by a quadratic convex programming method based on algebraic optimization.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"2 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125714120","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}
Xiantao Jiang, Zhaowei Ma, F. Yu, Tian Song, A. Boukerche
In intelligent transportation systems (ITS), video analytics is a potential technology to enhance the safety of the Internet of Vehicles (IoV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for IoV. Furthermore, due to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoV. In this paper, for video analytics applications, the multi-access edge computing (MEC) and blockchain technologies are integrated into IoV to optimize the transaction throughput as well as reducing the latency of the MEC system. Furthermore, the joint optimization problem is formulated as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results show that the proposed approach can fast converge and signifcantly improve the performance of blockchain-enabled IoV with MEC.
{"title":"Edge Computing for Video Analytics in the Internet of Vehicles with Blockchain","authors":"Xiantao Jiang, Zhaowei Ma, F. Yu, Tian Song, A. Boukerche","doi":"10.1145/3416014.3424582","DOIUrl":"https://doi.org/10.1145/3416014.3424582","url":null,"abstract":"In intelligent transportation systems (ITS), video analytics is a potential technology to enhance the safety of the Internet of Vehicles (IoV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for IoV. Furthermore, due to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoV. In this paper, for video analytics applications, the multi-access edge computing (MEC) and blockchain technologies are integrated into IoV to optimize the transaction throughput as well as reducing the latency of the MEC system. Furthermore, the joint optimization problem is formulated as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results show that the proposed approach can fast converge and signifcantly improve the performance of blockchain-enabled IoV with MEC.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133507289","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}
Omar Minawi, Jason Whelan, Abdulaziz Almehmadi, K. El-Khatib
The automotive industry continues to innovate at an exponential rate to provide a safer and more efficient experience for consumers. Autonomous vehicles and Vehicle-to-Everything technologies are at the forefront of defining the future of transportation. Enabling vehicles to connect to various services has exposed critical in-vehicle networks such as the Controller Area Network (CAN) to potential exploitation by adversaries. In its standard form, the CAN bus suffers from multiple vulnerabilities such as limited bandwidth and lack of authentication. Attacks can be initiated through physical and wireless mediums, exploiting diagnostic interfaces, Bluetooth and infotainment systems to compromise the confidentiality, integrity and availability of data communication within vehicles. In this paper, a holistic, comprehensive, Machine Learning-Based intrusion detection system for the CAN bus is proposed to secure the critical in-vehicle network. The proposed system is modular, scalable and can be adapted to the ever-changing threat landscape of cyber vehicle attacks. On an unseen testing dataset, our system achieved 100% accuracy in protecting against denial of service and multiple impersonation injection attacks, as well as 95.67% accuracy of fuzzy injection attacks.
{"title":"Machine Learning-Based Intrusion Detection System for Controller Area Networks","authors":"Omar Minawi, Jason Whelan, Abdulaziz Almehmadi, K. El-Khatib","doi":"10.1145/3416014.3424581","DOIUrl":"https://doi.org/10.1145/3416014.3424581","url":null,"abstract":"The automotive industry continues to innovate at an exponential rate to provide a safer and more efficient experience for consumers. Autonomous vehicles and Vehicle-to-Everything technologies are at the forefront of defining the future of transportation. Enabling vehicles to connect to various services has exposed critical in-vehicle networks such as the Controller Area Network (CAN) to potential exploitation by adversaries. In its standard form, the CAN bus suffers from multiple vulnerabilities such as limited bandwidth and lack of authentication. Attacks can be initiated through physical and wireless mediums, exploiting diagnostic interfaces, Bluetooth and infotainment systems to compromise the confidentiality, integrity and availability of data communication within vehicles. In this paper, a holistic, comprehensive, Machine Learning-Based intrusion detection system for the CAN bus is proposed to secure the critical in-vehicle network. The proposed system is modular, scalable and can be adapted to the ever-changing threat landscape of cyber vehicle attacks. On an unseen testing dataset, our system achieved 100% accuracy in protecting against denial of service and multiple impersonation injection attacks, as well as 95.67% accuracy of fuzzy injection attacks.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134381729","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}
M. Malinverno, Francesco Raviglione, C. Casetti, C. Chiasserini, J. Mangues‐Bafalluy, M. Requena-Esteso
The vast majority of vehicular applications leverage vehicle-to-everything communications (V2X) to increase road safety, optimize the available transportation resources, and improve the user experience. Because of the complexity and the high deployment costs of vehicular applications, it is usually convenient to extensively test them by simulation. We present an open-source simulation framework for the ns-3 simulator, featuring state-of-the-art vehicular communication models, in which the mobility is managed by the SUMO (Simulation of Urban MObility) simulator. Unlike other simulation frameworks, where the user is mostly limited to a single communication stack, our framework unifies multiple stacks under a single open-source repository. The framework is designed to make it easier to configure the communication stacks, and to enable a fast and easy deployment of vehicular applications. It comes with the support for centralized and distributed vehicular network architectures, embedding the IEEE 802.11p, 3GPP C-V2X Mode 4 and LTE communication stacks, and with vehicular messages dissemination stacks compliant with ETSI standards. We also present two sample applications thought to show the potentiality of the framework, namely an area speed advisory and an emergency vehicle alert.
{"title":"A Multi-stack Simulation Framework for Vehicular Applications Testing","authors":"M. Malinverno, Francesco Raviglione, C. Casetti, C. Chiasserini, J. Mangues‐Bafalluy, M. Requena-Esteso","doi":"10.1145/3416014.3424603","DOIUrl":"https://doi.org/10.1145/3416014.3424603","url":null,"abstract":"The vast majority of vehicular applications leverage vehicle-to-everything communications (V2X) to increase road safety, optimize the available transportation resources, and improve the user experience. Because of the complexity and the high deployment costs of vehicular applications, it is usually convenient to extensively test them by simulation. We present an open-source simulation framework for the ns-3 simulator, featuring state-of-the-art vehicular communication models, in which the mobility is managed by the SUMO (Simulation of Urban MObility) simulator. Unlike other simulation frameworks, where the user is mostly limited to a single communication stack, our framework unifies multiple stacks under a single open-source repository. The framework is designed to make it easier to configure the communication stacks, and to enable a fast and easy deployment of vehicular applications. It comes with the support for centralized and distributed vehicular network architectures, embedding the IEEE 802.11p, 3GPP C-V2X Mode 4 and LTE communication stacks, and with vehicular messages dissemination stacks compliant with ETSI standards. We also present two sample applications thought to show the potentiality of the framework, namely an area speed advisory and an emergency vehicle alert.","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158153","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}
{"title":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","authors":"","doi":"10.1145/3416014","DOIUrl":"https://doi.org/10.1145/3416014","url":null,"abstract":"","PeriodicalId":213859,"journal":{"name":"Proceedings of the 10th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130680590","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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