Visible to the public Biblio

Filters: Keyword is Roads  [Clear All Filters]
Lu, Chang, Lei, Xiaochun, Xie, Junlin, Wang, Xiaolong, Mu, XiangBoge.  2020.  Panoptic Feature Pyramid Network Applications In Intelligent Traffic. 2020 16th International Conference on Computational Intelligence and Security (CIS). :40–43.
Intelligenta transportation is an important part of urban development. The core of realizing intelligent transportation is to master the urban road condition. This system processes the video of dashcam based on the Panoptic Segmentation network and adds a tracking module based on the comparison of front and rear frames and KM algorithm. The system mainly includes the following parts: embedded device, Panoptic Feature Pyramid Network, cloud server and Web site.
Razin, Y. S., Feigh, K. M..  2020.  Hitting the Road: Exploring Human-Robot Trust for Self-Driving Vehicles. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

With self-driving cars making their way on to our roads, we ask not what it would take for them to gain acceptance among consumers, but what impact they may have on other drivers. How they will be perceived and whether they will be trusted will likely have a major effect on traffic flow and vehicular safety. This work first undertakes an exploratory factor analysis to validate a trust scale for human-robot interaction and shows how previously validated metrics and general trust theory support a more complete model of trust that has increased applicability in the driving domain. We experimentally test this expanded model in the context of human-automation interaction during simulated driving, revealing how using these dimensions uncovers significant biases within human-robot trust that may have particularly deleterious effects when it comes to sharing our future roads with automated vehicles.

Ajenaghughrure, I. B., Sousa, S. C. da Costa, Lamas, D..  2020.  Risk and Trust in artificial intelligence technologies: A case study of Autonomous Vehicles. 2020 13th International Conference on Human System Interaction (HSI). :118–123.
This study investigates how risk influences users' trust before and after interactions with technologies such as autonomous vehicles (AVs'). Also, the psychophysiological correlates of users' trust from users” eletrodermal activity responses. Eighteen (18) carefully selected participants embark on a hypothetical trip playing an autonomous vehicle driving game. In order to stay safe, throughout the drive experience under four risk conditions (very high risk, high risk, low risk and no risk) that are based on automotive safety and integrity levels (ASIL D, C, B, A), participants exhibit either high or low trust by evaluating the AVs' to be highly or less trustworthy and consequently relying on the Artificial intelligence or the joystick to control the vehicle. The result of the experiment shows that there is significant increase in users' trust and user's delegation of controls to AVs' as risk decreases and vice-versa. In addition, there was a significant difference between user's initial trust before and after interacting with AVs' under varying risk conditions. Finally, there was a significant correlation in users' psychophysiological responses (electrodermal activity) when exhibiting higher and lower trust levels towards AVs'. The implications of these results and future research opportunities are discussed.
Liu, X., Gao, W., Feng, D., Gao, X..  2020.  Abnormal Traffic Congestion Recognition Based on Video Analysis. 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). :39—42.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.

Ma, Y., Bai, X..  2019.  Comparison of Location Privacy Protection Schemes in VANETs. 2019 12th International Symposium on Computational Intelligence and Design (ISCID). 2:79–83.
Vehicular Ad-hoc Networks (VANETs) is a traditional mobile ad hoc network (MANET) used on traffic roads and it is a special mobile ad hoc network. As an intelligent transportation system, VANETs can solve driving safety and provide value-added services. Therefore, the application of VANETs can improve the safety and efficiency of road traffic. Location services are in a crucial position for the development of VANETs. VANETs has the characteristics of open access and wireless communication. Malicious node attacks may lead to the leakage of user privacy in VANETs, thus seriously affecting the use of VANETs. Therefore, the location privacy issue of VANETs cannot be ignored. This paper classifies the attack methods in VANETs, and summarizes and compares the location privacy protection techniques proposed in the existing research.
Fraiji, Yosra, Ben Azzouz, Lamia, Trojet, Wassim, Saidane, Leila Azouz.  2018.  Cyber security issues of Internet of electric vehicles. 2018 IEEE Wireless Communications and Networking Conference (WCNC). :1—6.

The use of Electric Vehicle (EV) is growing rapidly due to its environmental benefits. However, the major problem of these vehicles is their limited battery, the lack of charging stations and the re-charge time. Introducing Information and Communication Technologies, in the field of EV, will improve energy efficiency, energy consumption predictions, availability of charging stations, etc. The Internet of Vehicles based only on Electric Vehicles (IoEV) is a complex system. It is composed of vehicles, humans, sensors, road infrastructure and charging stations. All these entities communicate using several communication technologies (ZigBee, 802.11p, cellular networks, etc). IoEV is therefore vulnerable to significant attacks such as DoS, false data injection, modification. Hence, security is a crucial factor for the development and the wide deployment of Internet of Electric Vehicles (IoEV). In this paper, we present an overview of security issues of the IoEV architecture and we highlight open issues that make the IoEV security a challenging research area in the future.

Davydov, Vadim, Bezzateev, Sergey.  2018.  Secure Information Exchange in Defining the Location of the Vehicle. 2018 41st International Conference on Telecommunications and Signal Processing (TSP). :1—5.

With the advent of the electric vehicle market, the problem of locating a vehicle is becoming more and more important. Smart roads are creating, where the car control system can work without a person - communicating with the elements on the road. The standard technologies, such as GPS, can't always accurately determine the location, and not all vehicles have a GPS-module. It is very important to build an effective secure communication protocol between the vehicle and the base stations on the road. In this paper we consider different methods of location determination, propose the improved communicating protocol between the vehicle and the base station.

Anzer, Ayesha, Elhadef, Mourad.  2018.  A Multilayer Perceptron-Based Distributed Intrusion Detection System for Internet of Vehicles. 2018 IEEE 4th International Conference on Collaboration and Internet Computing (CIC). :438—445.

Security of Internet of vehicles (IoV) is critical as it promises to provide with safer and secure driving. IoV relies on VANETs which is based on V2V (Vehicle to Vehicle) communication. The vehicles are integrated with various sensors and embedded systems allowing them to gather data related to the situation on the road. The collected data can be information associated with a car accident, the congested highway ahead, parked car, etc. This information exchanged with other neighboring vehicles on the road to promote safe driving. IoV networks are vulnerable to various security attacks. The V2V communication comprises specific vulnerabilities which can be manipulated by attackers to compromise the whole network. In this paper, we concentrate on intrusion detection in IoV and propose a multilayer perceptron (MLP) neural network to detect intruders or attackers on an IoV network. Results are in the form of prediction, classification reports, and confusion matrix. A thorough simulation study demonstrates the effectiveness of the new MLP-based intrusion detection system.

Kumar, Sushil, Mann, Kulwinder Singh.  2019.  Prevention of DoS Attacks by Detection of Multiple Malicious Nodes in VANETs. 2019 International Conference on Automation, Computational and Technology Management (ICACTM). :89—94.

Vehicular Adhoc Network (VANET), a specialized form of MANET in which safety is the major concern as critical information related to driver's safety and assistance need to be disseminated between the vehicle nodes. The security of the nodes can be increased, if the network availability is increased. The availability of the network is decreased, if there is Denial of Service Attacks (DoS) in the network. In this paper, a packet detection algorithm for the prevention of DoS attacks is proposed. This algorithm will be able to detect the multiple malicious nodes in the network which are sending irrelevant packets to jam the network and that will eventually stop the network to send the safety messages. The proposed algorithm was simulated in NS-2 and the quantitative values of packet delivery ratio, packet loss ratio, network throughput proves that the proposed algorithm enhance the security of the network by detecting the DoS attack well in time.

Dong, Hongbo, Zhu, Qianxiang.  2019.  A Cyber-Physical Interaction Model Based Impact Assessment of Cyberattacks for Internet of Vehicles. 2019 4th International Conference on Communication and Information Systems (ICCIS). :79–83.
Internet of Vehicles are the important part of Intelligence Traffic Systems (ITS), which are essential for the national security and economy development. The impact assessment for cyberattacks in the IoV protection is of great theoretical and practical significance. Most of the researchers in this field pay attention on the attack impact on a vehicle, and the seldom investigate the impact on the whole system which combines all the vehicles as a whole integration. To tackle this problem, a cyber-physical interaction model based impact assessment of cyberattacks is presented. In this approach, the operation of IoV is modeled from the cyberphysical interaction perspective, and then the propagating process from cyber layer to physical layer is investigated. Based on above model, the impact assessment of cyberattacks on IoV is realized quantitatively. Finally, a simulation on an IoV is conducted to verify the effectiveness of this approach.
Engoulou, Richard Gilles, Bellaiche, Martine, Halabi, Talal, Pierre, Samuel.  2019.  A Decentralized Reputation Management System for Securing the Internet of Vehicles. 2019 International Conference on Computing, Networking and Communications (ICNC). :900–904.
The evolution of the Internet of Vehicles (IoV) paradigm has recently attracted a lot of researchers and industries. Vehicular Ad Hoc Networks (VANET) is the networking model that lies at the heart of this technology. It enables the vehicles to exchange relevant information concerning road conditions and safety. However, ensuring communication security has been and still is one of the main challenges to vehicles' interconnection. To secure the interconnected vehicular system, many cryptography techniques, communication protocols, and certification and reputation-based security approaches were proposed. Nonetheless, some limitations are still present, preventing the practical implementation of such approaches. In this paper, we first define a set of locally-perceived behavioral reputation parameters that enable a distributed evaluation of vehicles' reputation. Then, we integrate these parameters into the design of a reputation management system to exclude malicious or faulty vehicles from the IoV network. Our system can help in the prevention of several attacks on the VANET environment such as Sybil and Denial of Service attacks, and can be implemented in a fully decentralized fashion.
Xie, Kun, Li, Xiaocan, Wang, Xin, Xie, Gaogang, Xie, Dongliang, Li, Zhenyu, Wen, Jigang, Diao, Zulong.  2019.  Quick and Accurate False Data Detection in Mobile Crowd Sensing. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :2215—2223.

With the proliferation of smartphones, a novel sensing paradigm called Mobile Crowd Sensing (MCS) has emerged very recently. However, the attacks and faults in MCS cause a serious false data problem. Observing the intrinsic low dimensionality of general monitoring data and the sparsity of false data, false data detection can be performed based on the separation of normal data and anomalies. Although the existing separation algorithm based on Direct Robust Matrix Factorization (DRMF) is proven to be effective, requiring iteratively performing Singular Value Decomposition (SVD) for low-rank matrix approximation would result in a prohibitively high accumulated computation cost when the data matrix is large. In this work, we observe the quick false data location feature from our empirical study of DRMF, based on which we propose an intelligent Light weight Low Rank and False Matrix Separation algorithm (LightLRFMS) that can reuse the previous result of the matrix decomposition to deduce the one for the current iteration step. Our algorithm can largely speed up the whole iteration process. From a theoretical perspective, we validate that LightLRFMS only requires one round of SVD computation and thus has very low computation cost. We have done extensive experiments using a PM 2.5 air condition trace and a road traffic trace. Our results demonstrate that LightLRFMS can achieve very good false data detection performance with the same highest detection accuracy as DRMF but with up to 10 times faster speed thanks to its lower computation cost.

Sliwa, Benjamin, Haferkamp, Marcus, Al-Askary, Manar, Dorn, Dennis, Wietfeld, Christian.  2018.  A radio-fingerprinting-based vehicle classification system for intelligent traffic control in smart cities. 2018 Annual IEEE International Systems Conference (SysCon). :1–5.
The measurement and provision of precise and up-to-date traffic-related key performance indicators is a key element and crucial factor for intelligent traffic control systems in upcoming smart cities. The street network is considered as a highly-dynamic Cyber Physical System (CPS) where measured information forms the foundation for dynamic control methods aiming to optimize the overall system state. Apart from global system parameters like traffic flow and density, specific data, such as velocity of individual vehicles as well as vehicle type information, can be leveraged for highly sophisticated traffic control methods like dynamic type-specific lane assignments. Consequently, solutions for acquiring these kinds of information are required and have to comply with strict requirements ranging from accuracy over cost-efficiency to privacy preservation. In this paper, we present a system for classifying vehicles based on their radio-fingerprint. In contrast to other approaches, the proposed system is able to provide real-time capable and precise vehicle classification as well as cost-efficient installation and maintenance, privacy preservation and weather independence. The system performance in terms of accuracy and resource-efficiency is evaluated in the field using comprehensive measurements. Using a machine learning based approach, the resulting success ratio for classifying cars and trucks is above 99%.
Ma, Zhaohui, Yang, Yan.  2019.  Optimization Strategy of Flow Table Storage Based on “Betweenness Centrality”. 2019 IEEE International Conference on Power Data Science (ICPDS). :76–79.
With the gradual progress of cloud computing, big data, network virtualization and other network technology. The traditional network architecture can no longer support this huge business. At this time, the clean slate team defined a new network architecture, SDN (Software Defined Network). It has brought about tremendous changes in the development of today's networks. The controller sends the flow table down to the switch, and the data flow is forwarded through matching flow table items. However, the current flow table resources of the SDN switch are very limited. Therefore, this paper studies the technology of the latest SDN Flow table optimization at home and abroad, proposes an efficient optimization scheme of Flow table item on the betweenness centrality through the main road selection algorithm, and realizes related applications by setting up experimental topology. Experiments show that this scheme can greatly reduce the number of flow table items of switches, especially the more hosts there are in the topology, the more obvious the experimental effect is. And the experiment proves that the optimization success rate is over 80%.
Kanemura, Kota, Toyoda, Kentaroh, Ohtsuki, Tomoaki.  2019.  Identification of Darknet Markets’ Bitcoin Addresses by Voting Per-address Classification Results. 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). :154—158.
Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.
Xu, Ye, Li, Fengying, Cao, Bin.  2019.  Privacy-Preserving Authentication Based on Pseudonyms and Secret Sharing for VANET. 2019 Computing, Communications and IoT Applications (ComComAp). :157—162.
In this paper, we propose a conditional privacy-preserving authentication scheme based on pseudonyms and (t,n) threshold secret sharing, named CPPT, for vehicular communications. To achieve conditional privacy preservation, our scheme implements anonymous communications based on pseudonyms generated by hash chains. To prevent bad vehicles from conducting framed attacks on honest ones, CPPT introduces Shamir (t,n) threshold secret sharing technique. In addition, through two one-way hash chains, forward security and backward security are guaranteed, and it also optimize the revocation overhead. The size of certificate revocation list (CRL) is only proportional to the number of revoked vehicles and irrelated to how many pseudonymous certificates are held by the revoked vehicles. Extensive simulations demonstrate that CPPT outperforms ECPP, DCS, Hybrid and EMAP schemes in terms of revocation overhead, certificate updating overhead and authentication overhead.
Xiong, Chen, Chen, Hua, Cai, Ming, Gao, Jing.  2019.  A Vehicle Trajectory Adversary Model Based on VLPR Data. 2019 5th International Conference on Transportation Information and Safety (ICTIS). :903–912.
Although transport agency has employed desensitization techniques to deal with the privacy information when publicizing vehicle license plate recognition (VLPR) data, the adversaries can still eavesdrop on vehicle trajectories by certain means and further acquire the associated person and vehicle information through background knowledge. In this work, a privacy attacking method by using the desensitized VLPR data is proposed to link the vehicle trajectory. First the road average speed is evaluated by analyzing the changes of traffic flow, which is used to estimate the vehicle's travel time to the next VLPR system. Then the vehicle suspicion list is constructed through the time relevance of neighboring VLPR systems. Finally, since vehicles may have the same features like color, type, etc, the target trajectory will be located by filtering the suspected list by the rule of qualified identifier (QI) attributes and closest time method. Based on the Foshan City's VLPR data, the method is tested and results show that correct vehicle trajectory can be linked, which proves that the current VLPR data publication way has the risk of privacy disclosure. At last, the effects of related parameters on the proposed method are discussed and effective suggestions are made for publicizing VLPR date in the future.
Arthi, A., Aravindhan, K..  2019.  Enhancing the Performance Analysis of LWA Protocol Key Agreement in Vehicular Ad hoc Network. 2019 5th International Conference on Advanced Computing Communication Systems (ICACCS). :1070–1074.

Road accidents are challenging threat in the present scenario. In India there are 5, 01,423 road accidents in 2015. A day 400 hundred deaths are forcing to India to take car safety sincerely. The common cause for road accidents is driver's distraction. In current world the people are dominated by the tablet PC and other hand held devices. The VANET technology is a vehicle-to-vehicle communication; here the main challenge will be to deliver qualified communication during mobility. The paper proposes a standard new restricted lightweight authentication protocol utilizing key agreement theme for VANETs. Inside the planned topic, it has three sorts of validations: 1) V2V 2) V2CH; and 3) CH and RSU. Aside from this authentication, the planned topic conjointly keeps up mystery keys between RSUs for the safe communication. Thorough informal security analysis demonstrates the planned subject is skilled to guard different malicious attack. In addition, the NS2 Simulation exhibits the possibility of the proposed plan in VANET background.

Hayward, Jake, Tomlinson, Andrew, Bryans, Jeremy.  2019.  Adding Cyberattacks To An Industry-Leading CAN Simulator. 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C). :9–16.
Recent years have seen an increase in the data usage in cars, particularly as they become more autonomous and connected. With the rise in data use have come concerns about automotive cyber-security. An in-vehicle network shown to be particularly vulnerable is the Controller Area Network (CAN), which is the communication bus used by the car's safety critical and performance critical components. Cyber attacks on the CAN have been demonstrated, leading to research to develop attack detection and attack prevention systems. Such research requires representative attack demonstrations and data for testing. Obtaining this data is problematical due to the expense, danger and impracticality of using real cars on roads or tracks for example attacks. Whilst CAN simulators are available, these tend to be configured for testing conformance and functionality, rather than analysing security and cyber vulnerability. We therefore adapt a leading, industry-standard, CAN simulator to incorporate a core set of cyber attacks that are representative of those proposed by other researchers. Our adaptation allows the user to configure the attacks, and can be added easily to the free version of the simulator. Here we describe the simulator and, after reviewing the attacks that have been demonstrated and discussing their commonalities, we outline the attacks that we have incorporated into the simulator.
Lai, Chengzhe, Du, Yangyang, Men, Jiawei, Zheng, Dong.  2019.  A Trust-based Real-time Map Updating Scheme. 2019 IEEE/CIC International Conference on Communications in China (ICCC). :334—339.

The real-time map updating enables vehicles to obtain accurate and timely traffic information. Especially for driverless cars, real-time map updating can provide high-precision map service to assist the navigation, which requires vehicles to actively upload the latest road conditions. However, due to the untrusted network environment, it is difficult for the real-time map updating server to evaluate the authenticity of the road information from the vehicles. In order to prevent malicious vehicles from deliberately spreading false information and protect the privacy of vehicles from tracking attacks, this paper proposes a trust-based real-time map updating scheme. In this scheme, the public key is used as the identifier of the vehicle for anonymous communication with conditional anonymity. In addition, the blockchain is applied to provide the existence proof for the public key certificate of the vehicle. At the same time, to avoid the spread of false messages, a trust evaluation algorithm is designed. The fog node can validate the received massages from vehicles using Bayesian Inference Model. Based on the verification results, the road condition information is sent to the real-time map updating server so that the server can update the map in time and prevent the secondary traffic accident. In order to calculate the trust value offset for the vehicle, the fog node generates a rating for each message source vehicle, and finally adds the relevant data to the blockchain. According to the result of security analysis, this scheme can guarantee the anonymity and prevent the Sybil attack. Simulation results show that the proposed scheme is effective and accurate in terms of real-time map updating and trust values calculating.

Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder.  2019.  Trusted Autonomous Vehicle: Measuring Trust using On-Board Unit Data. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :787—792.

Vehicular Ad-hoc Networks (VANETs) play an essential role in ensuring safe, reliable and faster transportation with the help of an Intelligent Transportation system. The trustworthiness of vehicles in VANETs is extremely important to ensure the authenticity of messages and traffic information transmitted in extremely dynamic topographical conditions where vehicles move at high speed. False or misleading information may cause substantial traffic congestions, road accidents and may even cost lives. Many approaches exist in literature to measure the trustworthiness of GPS data and messages of an Autonomous Vehicle (AV). To the best of our knowledge, they have not considered the trustworthiness of other On-Board Unit (OBU) components of an AV, along with GPS data and transmitted messages, though they have a substantial relevance in overall vehicle trust measurement. In this paper, we introduce a novel model to measure the overall trustworthiness of an AV considering four different OBU components additionally. The performance of the proposed method is evaluated with a traffic simulation model developed by Simulation of Urban Mobility (SUMO) using realistic traffic data and considering different levels of uncertainty.

Bundalo, Zlatko, Veljko, Momčilo, Bundalo, Dušanka, Kuzmić, Goran, Sajić, Mirko, Ramakić, Adnan.  2019.  Energy Efficient Embedded Systems for LED Lighting Control in Traffic. 2019 8th Mediterranean Conference on Embedded Computing (MECO). :1–4.
The paper considers, proposes and describes possibilities and ways for application, design and implementation of energy efficient microprocessor based embedded systems for LED lighting control in the traffic. Using LED lighting technology and appropriate designed embedded systems it is possible to implement very efficient and smart systems for very wide range of applications in the traffic. This type of systems can be widely used in many places in the traffic where there is needed quality lighting and low energy consumption. Application of such systems enables to increase energy consumption efficiency, quality of lighting and security of traffic and to decrease total costs for the lighting. Way of design and use of such digital embedded system to effectively increase functionality and efficiency of lighting in the traffic is proposed and described. It is also proposed and described one practically designed and implemented simple and universal embedded system for LED lighting control for many applications in the traffic.
Tiennoy, Sasirom, Saivichit, Chaiyachet.  2018.  Using a Distributed Roadside Unit for the Data Dissemination Protocol in VANET With the Named Data Architecture. IEEE Access. 6:32612–32623.
Vehicular ad hoc network (VANET) has recently become one of the highly active research areas for wireless networking. Since VANET is a multi-hop wireless network with very high mobility and intermittent connection lifetime, it is important to effectively handle the data dissemination issue in this rapidly changing environment. However, the existing TCP/IP implementation may not fit into such a highly dynamic environment because the nodes in the network must often perform rerouting due to their inconsistency of connectivity. In addition, the drivers in the vehicles may want to acquire some data, but they do not know the address/location of such data storage. Hence, the named data networking (NDN) approach may be more desirable here. The NDN architecture is proposed for the future Internet, which focuses on the delivering mechanism based on the message contents instead of relying on the host addresses of the data. In this paper, a new protocol named roadside unit (RSU) assisted of named data network (RA-NDN) is presented. The RSU can operate as a standalone node [standalone RSU (SA-RSU)]. One benefit of deploying SA-RSUs is the improved network connectivity. This study uses the NS3 and SUMO software packages for the network simulator and traffic simulator software, respectively, to verify the performance of the RA-NDN protocol. To reduce the latency under various vehicular densities, vehicular transmission ranges, and number of requesters, the proposed approach is compared with vehicular NDN via a real-world data set in the urban area of Sathorn road in Bangkok, Thailand. The simulation results show that the RA-NDN protocol improves the performance of ad hoc communications with the increase in data received ratio and throughput and the decrease in total dissemination time and traffic load.
Ostrovskaya, Svetlana, Surnin, Oleg, Hussain, Rasheed, Bouk, Safdar Hussain, Lee, JooYoung, Mehran, Narges, Ahmed, Syed Hassan, Benslimane, Abderrahim.  2018.  Towards Multi-metric Cache Replacement Policies in Vehicular Named Data Networks. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). :1–7.
Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.
Tahir, Muhammad Usman, Rehman, Rana Asif.  2018.  CUIF: Control of Useless Interests Flooding in Vehicular Named Data Networks. 2018 International Conference on Frontiers of Information Technology (FIT). :303–308.
Now-a-days vehicular information network technology is receiving a lot of attention due to its practical as well as safety related applications. By using this technology, participating vehicles can communicate among themselves on the road in order to obtain any interested data or emergency information. In Vehicular Ad-Hoc Network (VANET), due to the fast speed of the vehicles, the traditional host centric approach (i.e. TCP/IP) fails to provide efficient and robust communication between large number of vehicles. Therefore, Named Data Network (NDN) newly proposed Internet architecture is applied in VANET, named as VNDN. In which, the vehicles can communicate with the help of content name rather than vehicle address. In this paper, we explored the concepts and identify the main packet forwarding issues in VNDN. Furthermore, we proposed a protocol, named Control of Useless Interests Flooding (CUIF) in Vehicular Named Data Network. In which, it provides the best and efficient communication environment to users while driving on the highway. CUIF scheme reduces the Interest forwarding storm over the network and control the flooding of useless packets against the direction of a Producer vehicle. Our simulation results show that CUIF scheme decreases the number of outgoing Interest packets as well as data download time in the network.