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Olowononi, F. O., Rawat, D. B., Liu, C..  2020.  Dependable Adaptive Mobility in Vehicular Networks for Resilient Mobile Cyber Physical Systems. 2020 IEEE International Conference on Communications Workshops (ICC Workshops). :1—6.

Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.

Li, W., Li, L..  2009.  A Novel Approach for Vehicle-logo Location Based on Edge Detection and Morphological Filter. 2009 Second International Symposium on Electronic Commerce and Security. 1:343—345.

Vehicle-logo location is a crucial step in vehicle-logo recognition system. In this paper, a novel approach of the vehicle-logo location based on edge detection and morphological filter is proposed. Firstly, the approximate location of the vehicle-logo region is determined by the prior knowledge about the position of the vehicle-logo; Secondly, the texture measure is defined to recognize the texture of the vehicle-logo background; Then, vertical edge detection is executed for the vehicle-logo background with the horizontal texture and horizontal edge detection is implemented for the vehicle-logo background with the vertical texture; Finally, position of the vehicle-logo is located accurately by mathematical morphology filter. Experimental results show the proposed method is effective.

Slavic, G., Campo, D., Baydoun, M., Marin, P., Martin, D., Marcenaro, L., Regazzoni, C..  2020.  Anomaly Detection in Video Data Based on Probabilistic Latent Space Models. 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). :1—8.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

Hasan, Khondokar Fida, Kaur, Tarandeep, Hasan, Md. Mhedi, Feng, Yanming.  2019.  Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). :1–6.
Over the past few years, we have experienced great technological advancements in the information and communication field, which has significantly contributed to reshaping the Intelligent Transportation System (ITS) concept. Evolving from the platform of a collection of sensors aiming to collect data, the data exchanged paradigm among vehicles is shifted from the local network to the cloud. With the introduction of cloud and edge computing along with ubiquitous 5G mobile network, it is expected to see the role of Artificial Intelligence (AI) in data processing and smart decision imminent. So as to fully understand the future automobile scenario in this verge of industrial revolution 4.0, it is necessary first of all to get a clear understanding of the cutting-edge technologies that going to take place in the automotive ecosystem so that the cyber-physical impact on transportation system can be measured. CIoV, which is abbreviated from Cognitive Internet of Vehicle, is one of the recently proposed architectures of the technological evolution in transportation, and it has amassed great attention. It introduces cloud-based artificial intelligence and machine learning into transportation system. What are the future expectations of CIoV? To fully contemplate this architecture's future potentials, and milestones set to achieve, it is crucial to understand all the technologies that leaned into it. Also, the security issues to meet the security requirements of its practical implementation. Aiming to that, this paper presents the evolution of CIoV along with the layer abstractions to outline the distinctive functional parts of the proposed architecture. It also gives an investigation of the prime security and privacy issues associated with technological evolution to take measures.
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.
Desmoulins, Nicolas, Diop, Aïda, Rafflé, Yvan, Traoré, Jacques, Gratesac, Josselin.  2019.  Practical Anonymous Attestation-based Pseudonym Schemes for Vehicular Networks. 2019 IEEE Vehicular Networking Conference (VNC). :1—8.

Vehicular communication systems increase traffic efficiency and safety by allowing vehicles to share safety-related information and location-based services. Pseudonym schemes are the standard solutions providing driver/vehicle anonymity, whilst enforcing vehicle accountability in case of liability issues. State-of-the-art PKI-based pseudonym schemes present scalability issues, notably due to the centralized architecture of certificate-based solutions. The first Direct Anonymous Attestation (DAA)-based pseudonym scheme was introduced at VNC 2017, providing a decentralized approach to the pseudonym generation and update phases. The DAA-based construction leverages the properties of trusted computing, allowing vehicles to autonomously generate their own pseudonyms by using a (resource constrained) Trusted Hardware Module or Component (TC). This proposition however requires the TC to delegate part of the (heavy) pseudonym generation computations to the (more powerful) vehicle's On-Board Unit (OBU), introducing security and privacy issues in case the OBU becomes compromised. In this paper, we introduce a novel pseudonym scheme based on a variant of DAA, namely a pre-DAA-based pseudonym scheme. All secure computations in the pre-DAA pseudonym lifecycle are executed by the secure element, thus creating a secure enclave for pseudonym generation, update, and revocation. We instantiate vehicle-to-everything (V2X) with our pre-DAA solution, thus ensuring user anonymity and user-controlled traceability within the vehicular network. In addition, the pre-DAA-based construction transfers accountability from the vehicle to the user, thus complying with the many-to-many driver/vehicle relation. We demonstrate the efficiency of our solution with a prototype implementation on a standard Javacard (acting as a TC), showing that messages can be anonymously signed and verified in less than 50 ms.

jaidane, Emna, Hamdi, Mohamed, Aguili, Taoufik, Kim, Tai-hoon.  2019.  A new vehicular blackbox architecture based on searchable encryption. 2019 15th International Wireless Communications Mobile Computing Conference (IWCMC). :1073–1078.
Blackboxes are being increasingly used in the vehicular context to store and transmit information related to safety, security and many other applications. The plethora of sensors available at the different parts of the vehicle can provide enriched gathering of the data related to these applications. Nonetheless, to support multiple use cases, the blackbox must be accessible by various actors (e.g. vehicle owner, insurance company, law enforcement authorities). This raises significant challenges regarding the privacy of the data collected and stored in the blackbox. In fact, these data can often lead to tracing back accurate facts about the behaviour of the owner of the vehicle. To cope with this problem, we propose a new blackbox architecture supporting searchable encryption. This feature allows multiple users who are not able to decipher the content of the blackbox to validate properties such as path traceback and velocity. To illustrate the implementation of the proposed technique in practice, we discuss a case study related to post-accident processing by insurance companies.
Zhang, Xuefei, Liu, Junjie, Li, Yijing, Cui, Qimei, Tao, Xiaofeng, Liu, Ren Ping.  2019.  Blockchain Based Secure Package Delivery via Ridesharing. 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP). :1–6.

Delivery service via ridesharing is a promising service to share travel costs and improve vehicle occupancy. Existing ridesharing systems require participating vehicles to periodically report individual private information (e.g., identity and location) to a central controller, which is a potential central point of failure, resulting in possible data leakage or tampering in case of controller break down or under attack. In this paper, we propose a Blockchain secured ridesharing delivery system, where the immutability and distributed architecture of the Blockchain can effectively prevent data tampering. However, such tamper-resistance property comes at the cost of a long confirmation delay caused by the consensus process. A Hash-oriented Practical Byzantine Fault Tolerance (PBFT) based consensus algorithm is proposed to improve the Blockchain efficiency and reduce the transaction confirmation delay from 10 minutes to 15 seconds. The Hash-oriented PBFT effectively avoids the double-spending attack and Sybil attack. Security analysis and simulation results demonstrate that the proposed Blockchain secured ridesharing delivery system offers strong security guarantees and satisfies the quality of delivery service in terms of confirmation delay and transaction throughput.

Tahir, Faiza, Nasir, Samra, Khalid, Zainab.  2019.  Privacy-Preserving Authentication Protocol based on Hybrid Cryptography for VANETs. 2019 International Conference on Applied and Engineering Mathematics (ICAEM). :80–85.
The key concerns in VANET communication are the security and privacy of the vehicles involved, but at the same time an efficient way to provide non-repudiation in the ad-hoc network is an important requirement. Most schemes proposed are using public key infrastructure (PKI) or symmetric key encryption to achieve security in VANET; both individually lack in serving the required purpose of providing privacy preservation of the involved On-Board Units (OBUs) (while still being able to offer non-repudiation) and amount to very sizeable overheads in computation. This paper proposes a privacy-preserving authentication protocol that employs hybrid cryptography, using the best features of PKI and symmetric cryptography to form a protocol that is scalable, efficient and offers services of integrity, non-repudiation, conditional privacy, and unlinkability; while still keeping the computational overhead at a reasonable level. The performance and security analysis of this scheme is provided to support the propositions.
Sun, M., Li, M., Gerdes, R..  2018.  Truth-Aware Optimal Decision-Making Framework with Driver Preferences for V2V Communications. 2018 IEEE Conference on Communications and Network Security (CNS). :1-9.

In Vehicle-to-Vehicle (V2V) communications, malicious actors may spread false information to undermine the safety and efficiency of the vehicular traffic stream. Thus, vehicles must determine how to respond to the contents of messages which maybe false even though they are authenticated in the sense that receivers can verify contents were not tampered with and originated from a verifiable transmitter. Existing solutions to find appropriate actions are inadequate since they separately address trust and decision, require the honest majority (more honest ones than malicious), and do not incorporate driver preferences in the decision-making process. In this work, we propose a novel trust-aware decision-making framework without requiring an honest majority. It securely determines the likelihood of reported road events despite the presence of false data, and consequently provides the optimal decision for the vehicles. The basic idea of our framework is to leverage the implied effect of the road event to verify the consistency between each vehicle's reported data and actual behavior, and determine the data trustworthiness and event belief by integrating the Bayes' rule and Dempster Shafer Theory. The resulting belief serves as inputs to a utility maximization framework focusing on both safety and efficiency. This framework considers the two basic necessities of the Intelligent Transportation System and also incorporates drivers' preferences to decide the optimal action. Simulation results show the robustness of our framework under the multiple-vehicle attack, and different balances between safety and efficiency can be achieved via selecting appropriate human preference factors based on the driver's risk-taking willingness.

Rayavel, P., Rathnavel, P., Bharathi, M., Kumar, T. Siva.  2018.  Dynamic Traffic Control System Using Edge Detection Algorithm. 2018 International Conference on Soft-Computing and Network Security (ICSNS). :1-5.

As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.

Li, W., Li, S., Zhang, X., Pan, Q..  2018.  Optimization Algorithm Research of Logistics Distribution Path Based on the Deep Belief Network. 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES). :60-63.

Aiming at the phenomenon that the urban traffic is complex at present, the optimization algorithm of the traditional logistic distribution path isn't sensitive to the change of road condition without strong application in the actual logistics distribution, the optimization algorithm research of logistics distribution path based on the deep belief network is raised. Firstly, build the traffic forecast model based on the deep belief network, complete the model training and conduct the verification by learning lots of traffic data. On such basis, combine the predicated road condition with the traffic network to build the time-share traffic network, amend the access set and the pheromone variable of ant algorithm in accordance with the time-share traffic network, and raise the optimization algorithm of logistics distribution path based on the traffic forecasting. Finally, verify the superiority and application value of the algorithm in the actual distribution through the optimization algorithm contrast test with other logistics distribution paths.

Houmer, M., Hasnaoui, M. L., Elfergougui, A..  2018.  Security Analysis of Vehicular Ad-hoc Networks based on Attack Tree. 2018 International Conference on Selected Topics in Mobile and Wireless Networking (MoWNeT). :21–26.

Nowadays, Vehicular ad hoc network confronts many challenges in terms of security and privacy, due to the fact that data transmitted are diffused in an open access environment. However, highest of drivers want to maintain their information discreet and protected, and they do not want to share their confidential information. So, the private information of drivers who are distributed in this network must be protected against various threats that may damage their privacy. That is why, confidentiality, integrity and availability are the important security requirements in VANET. This paper focus on security threat in vehicle network especially on the availability of this network. Then we regard the rational attacker who decides to lead an attack based on its adversary's strategy to maximize its own attack interests. Our aim is to provide reliability and privacy of VANET system, by preventing attackers from violating and endangering the network. to ensure this objective, we adopt a tree structure called attack tree to model the attacker's potential attack strategies. Also, we join the countermeasures to the attack tree in order to build attack-defense tree for defending these attacks.

Baragchizadeh, A., Karnowski, T. P., Bolme, D. S., O’Toole, A. J..  2017.  Evaluation of Automated Identity Masking Method (AIM) in Naturalistic Driving Study (NDS). 2017 12th IEEE International Conference on Automatic Face Gesture Recognition (FG 2017). :378–385.

Identity masking methods have been developed in recent years for use in multiple applications aimed at protecting privacy. There is only limited work, however, targeted at evaluating effectiveness of methods-with only a handful of studies testing identity masking effectiveness for human perceivers. Here, we employed human participants to evaluate identity masking algorithms on video data of drivers, which contains subtle movements of the face and head. We evaluated the effectiveness of the “personalized supervised bilinear regression method for Facial Action Transfer (FAT)” de-identification algorithm. We also evaluated an edge-detection filter, as an alternate “fill-in” method when face tracking failed due to abrupt or fast head motions. Our primary goal was to develop methods for humanbased evaluation of the effectiveness of identity masking. To this end, we designed and conducted two experiments to address the effectiveness of masking in preventing recognition and in preserving action perception. 1- How effective is an identity masking algorithm?We conducted a face recognition experiment and employed Signal Detection Theory (SDT) to measure human accuracy and decision bias. The accuracy results show that both masks (FAT mask and edgedetection) are effective, but that neither completely eliminated recognition. However, the decision bias data suggest that both masks altered the participants' response strategy and made them less likely to affirm identity. 2- How effectively does the algorithm preserve actions? We conducted two experiments on facial behavior annotation. Results showed that masking had a negative effect on annotation accuracy for the majority of actions, with differences across action types. Notably, the FAT mask preserved actions better than the edge-detection mask. To our knowledge, this is the first study to evaluate a deidentification method aimed at preserving facial ac- ions employing human evaluators in a laboratory setting.

Aqel, S., Aarab, A., Sabri, M. A..  2016.  Shadow detection and removal for traffic sequences. 2016 International Conference on Electrical and Information Technologies (ICEIT). :168–173.

This paper address the problem of shadow detection and removal in traffic vision analysis. Basically, the presence of the shadow in the traffic sequences is imminent, and therefore leads to errors at segmentation stage and often misclassified as an object region or as a moving object. This paper presents a shadow removal method, based on both color and texture features, aiming to contribute to retrieve efficiently the moving objects whose detection are usually under the influence of cast-shadows. Additionally, in order to get a shadow-free foreground segmentation image, a morphology reconstruction algorithm is used to recover the foreground disturbed by shadow removal. Once shadows are detected, an automatic shadow removal model is proposed based on the information retrieved from the histogram shape. Experimental results on a real traffic sequence is presented to test the proposed approach and to validate the algorithm's performance.

Paone, J., Bolme, D., Ferrell, R., Aykac, D., Karnowski, T..  2015.  Baseline face detection, head pose estimation, and coarse direction detection for facial data in the SHRP2 naturalistic driving study. 2015 IEEE Intelligent Vehicles Symposium (IV). :174–179.

Keeping a driver focused on the road is one of the most critical steps in insuring the safe operation of a vehicle. The Strategic Highway Research Program 2 (SHRP2) has over 3,100 recorded videos of volunteer drivers during a period of 2 years. This extensive naturalistic driving study (NDS) contains over one million hours of video and associated data that could aid safety researchers in understanding where the driver's attention is focused. Manual analysis of this data is infeasible; therefore efforts are underway to develop automated feature extraction algorithms to process and characterize the data. The real-world nature, volume, and acquisition conditions are unmatched in the transportation community, but there are also challenges because the data has relatively low resolution, high compression rates, and differing illumination conditions. A smaller dataset, the head pose validation study, is available which used the same recording equipment as SHRP2 but is more easily accessible with less privacy constraints. In this work we report initial head pose accuracy using commercial and open source face pose estimation algorithms on the head pose validation data set.

Mukherjee, M., Edwards, J., Kwon, H., Porta, T. F. L..  2015.  Quality of information-aware real-time traffic flow analysis and reporting. 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). :69–74.

In this paper we present a framework for Quality of Information (QoI)-aware networking. QoI quantifies how useful a piece of information is for a given query or application. Herein, we present a general QoI model, as well as a specific example instantiation that carries throughout the rest of the paper. In this model, we focus on the tradeoffs between precision and accuracy. As a motivating example, we look at traffic video analysis. We present simple algorithms for deriving various traffic metrics from video, such as vehicle count and average speed. We implement these algorithms both on a desktop workstation and less-capable mobile device. We then show how QoI-awareness enables end devices to make intelligent decisions about how to process queries and form responses, such that huge bandwidth savings are realized.

Chuan, T. H., Zhang, J., Maode, M., Chong, P. H. Joo, Labiod, H..  2015.  Secure Public Key Regime (SPKR) in vehicular networks. 2015 International Conference on Cyber Security of Smart Cities, Industrial Control System and Communications (SSIC). :1–7.

Public Key Regime (PKR) was proposed as an alternative to certificate based PKI in securing Vehicular Networks (VNs). It eliminates the need for vehicles to append their certificate for verification because the Road Side Units (RSUs) serve as Delegated Trusted Authorities (DTAs) to issue up-to-date public keys to vehicles for communications. If a vehicle's private/public key needs to be revoked, the root TA performs real time updates and disseminates the changes to these RSUs in the network. Therefore, PKR does not need to maintain a huge Certificate Revocation List (CRL), avoids complex certificate verification process and minimizes the high latency. However, the PKR scheme is vulnerable to Denial of Service (DoS) and collusion attacks. In this paper, we study these attacks and propose a pre-authentication mechanism to secure the PKR scheme. Our new scheme is called the Secure Public Key Regime (SPKR). It is based on the Schnorr signature scheme that requires vehicles to expend some amount of CPU resources before RSUs issue the requested public keys to them. This helps to alleviate the risk of DoS attacks. Furthermore, our scheme is secure against collusion attacks. Through numerical analysis, we show that SPKR has a lower authentication delay compared with the Elliptic Curve Digital Signature (ECDSA) scheme and other ECDSA based counterparts.

Oliveira Vasconcelos, R., Nery e Silva, L.D., Endler, M..  2014.  Towards efficient group management and communication for large-scale mobile applications. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2014 IEEE International Conference on. :551-556.

Applications such as fleet management and logistics, emergency response, public security and surveillance or mobile workforce management use geo-positioning and mobile networks as means of enabling real-time monitoring, communication and collaboration among a possibly large set of mobile nodes. The majority of those systems require real-time tracking of mobile nodes (e.g. vehicles, people or mobile robots), reliable communication to/from the nodes, as well as group communication among the mobile nodes. In this paper we describe a distributed middleware with focus on management of context-defined groups of mobile nodes, and group communication with large sets of nodes. We also present a prototype Fleet Tracking and Management system based on our middleware, give an example of how context-specific group communication can enhance the node's mutual awareness, and show initial performance results that indicate small overhead and latency of the group communication and management.

Tong Liu, Qian Xu, Yuejun Li.  2014.  Adaptive filtering design for in-motion alignment of INS. Control and Decision Conference (2014 CCDC), The 26th Chinese. :2669-2674.

Misalignment angles estimation of strapdown inertial navigation system (INS) using global positioning system (GPS) data is highly affected by measurement noises, especially with noises displaying time varying statistical properties. Hence, adaptive filtering approach is recommended for the purpose of improving the accuracy of in-motion alignment. In this paper, a simplified form of Celso's adaptive stochastic filtering is derived and applied to estimate both the INS error states and measurement noise statistics. To detect and bound the influence of outliers in INS/GPS integration, outlier detection based on jerk tracking model is also proposed. The accuracy and validity of the proposed algorithm is tested through ground based navigation experiments.

Kumar, A., Sinha, M..  2014.  Overview on vehicular ad hoc network and its security issues. Computing for Sustainable Global Development (INDIACom), 2014 International Conference on. :792-797.

Vehicular ad-hoc networks (VANETs) provides infrastructure less, rapidly deployable, self-configurable network connectivity. The network is the collection vehicles interlinked by wireless links and willing to store and forward data for their peers. As vehicles move freely and organize themselves arbitrarily, message routing is done dynamically based on network connectivity. Compared with other ad-hoc networks, VANETs are particularly challenging due to the part of the vehicles' high rate of mobility and the numerous signal-weakening barrier, such as buildings, in their environments. Due to their enormous potential, VANET have gained an increasing attention in both industry and academia. Research activities range from lower layer protocol design to applications and implementation issues. A secure VANET system, while exchanging information should protect the system against unauthorized message injection, message alteration, eavesdropping. The security of VANET is one of the most critical issues because their information transmission is propagated in open access (wireless) environments. A few years back VANET has received increased attention as the potential technology to enhance active and preventive safety on the road, as well as travel comfort Safekeeping and privacy are mandatory in vehicular communications for a grateful acceptance and use of such technology. This paper is an attempt to highlight the problems occurred in Vehicular Ad hoc Networks and security issues.

Jia-Lun Tsai.  2014.  An Improved Cross-Layer Privacy-Preserving Authentication in WAVE-Enabled VANETs. Communications Letters, IEEE. 18:1931-1934.

In 2013, Biswas and Misic proposed a new privacy-preserving authentication scheme for WAVE-based vehicular ad hoc networks (VANETs), claiming that they used a variant of the Elliptic Curve Digital Signature Algorithm (ECDSA). However, our study has discovered that the authentication scheme proposed by them is vulnerable to a private key reveal attack. Any malicious receiving vehicle who receives a valid signature from a legal signing vehicle can gain access to the signing vehicle private key from the learned valid signature. Hence, the authentication scheme proposed by Biswas and Misic is insecure. We thus propose an improved version to overcome this weakness. The proposed improved scheme also supports identity revocation and trace. Based on this security property, the CA and a receiving entity (RSU or OBU) can check whether a received signature has been generated by a revoked vehicle. Security analysis is also conducted to evaluate the security strength of the proposed authentication scheme.

Kurian, N.A., Thomas, A., George, B..  2014.  Automated fault diagnosis in Multiple Inductive Loop Detectors. India Conference (INDICON), 2014 Annual IEEE. :1-5.

Multiple Inductive Loop Detectors are advanced Inductive Loop Sensors that can measure traffic flow parameters in even conditions where the traffic is heterogeneous and does not conform to lanes. This sensor consists of many inductive loops in series, with each loop having a parallel capacitor across it. These inductive and capacitive elements of the sensor may undergo open or short circuit faults during operation. Such faults lead to erroneous interpretation of data acquired from the loops. Conventional methods used for fault diagnosis in inductive loop detectors consume time and effort as they require experienced technicians and involve extraction of loops from the saw-cut slots on the road. This also means that the traffic flow parameters cannot be measured until the sensor system becomes functional again. The repair activities would also disturb traffic flow. This paper presents a method for automating fault diagnosis for series-connected Multiple Inductive Loop Detectors, based on an impulse test. The system helps in the diagnosis of open/short faults associated with the inductive and capacitive elements of the sensor structure by displaying the fault status conveniently. Since the fault location as well as the fault type can be precisely identified using this method, the repair actions are also localised. The proposed system thereby results in significant savings in both repair time and repair costs. An embedded system was developed to realize this scheme and the same was tested on a loop prototype.

Toukabri, T., Said, A.M., Abd-Elrahman, E., Afifi, H..  2014.  Cellular Vehicular Networks (CVN): ProSe-Based ITS in Advanced 4G Networks. Mobile Ad Hoc and Sensor Systems (MASS), 2014 IEEE 11th International Conference on. :527-528.

LTE-based Device-to-Device (D2D) communications have been envisioned as a new key feature for short range wireless communications in advanced and beyond 4G networks. We propose in this work to exploit this novel concept of D2D as a new alternative for Intelligent Transportation Systems (ITS) Vehicle-to-Vehicle/Infrastructure (V2X) communications in next generation cellular networks. A 3GPP standard architecture has been recently defined to support Proximity Services (ProSe) in the LTE core network. Taking into account the limitations of this latter and the requirements of ITS services and V2X communications, we propose the CVN solution as an enhancement to the ProSe architecture in order to support hyper-local ITS services. CVN provides a reliable and scalable LTE-assisted opportunistic model for V2X communications through a distributed ProSe architecture. Using a hybrid clustering approach, vehicles are organized into dynamic clusters that are formed and managed by ProSe Cluster Heads which are elected centrally by the CVN core network. ITS services are deemed as Proximity Services and benefit from the basic ProSe discovery, authorization and authentication mechanisms. The CVN solution enhances V2V communication delays and overhead by reducing the need for multi-hop geo-routing. Preliminary simulation results show that the CVN solution provides short setup times and improves ITS communication delays.

Jing Li, Ming Chen.  2014.  On-Road Multiple Obstacles Detection in Dynamical Background. Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2014 Sixth International Conference on. 1:102-105.

Road In this paper, we focus on both the road vehicle and pedestrians detection, namely obstacle detection. At the same time, a new obstacle detection and classification technique in dynamical background is proposed. Obstacle detection is based on inverse perspective mapping and homography. Obstacle classification is based on fuzzy neural network. The estimation of the vanishing point relies on feature extraction strategy, which segments the lane markings of the images by combining a histogram-based segmentation with temporal filtering. Then, the vanishing point of each image is stabilized by means of a temporal filtering along the estimates of previous images. The IPM image is computed based on the stabilized vanishing point. The method exploits the geometrical relations between the elements in the scene so that obstacle can be detected. The estimated homography of the road plane between successive images is used for image alignment. A new fuzzy decision fusion method with fuzzy attribution for obstacle detection and classification application is described. The fuzzy decision function modifies parameters with auto-adapted algorithm to get better classification probability. It is shown that the method can achieve better classification result.