In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real-time investigation forms the vibrant asset for self-driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real-time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance-focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real-time processing for an AV.
In general the term surveillance delineates monitoring or observing the behavior or activity. In view of this, with respect to transportation network the role of effective intelligent surveillance system is indispensable. While referring to transportation system, the task of surveillance system application should not be limited to vehicle behavior analysis. However, it should be extended to provide notification and safety measures for vehicles. In order to achieve the above criterion, a hardware module has been designed for the vehicles which shares the real time traffic related information to Traffic Management Center (TMC). The real time information can be used to VANET (vehicular ad hoc network) applications. Furthermore, a quantitative analysis was made in order to calculate the traffic management in TMC. In addition, we test the results by simulating it in NS2. In this respect we have considered three different cases, Emergency vehicles, VIP vehicles and normal vehicles. It is inferred from the results that end-to-end delay for emergency vehicles in vehicular environment is considerably less as compared to VIP and normal vehicles.Index Terms -Surveillance system, TMC, Embedded device, VANET, V2I and I2I.
Utilizing cloud computing, users can avail a compelling and effective approach for information sharing between collective individuals in the cloud with the facility of less administration cost and little maintenance. Security in cloud computing refers to procedures, standards and processes created to provide assurance for security of information in the cloud environment. In this paper, we project a secure data sharing method in cloud for dynamic members by producing keys for users using Logic Key Hierarchy (LKH) model, i.e., a tree-based key generation technique. We have generated this key using reverse hashing and one way hash-based technique so that no exiled user can predict the new key and new users cannot predict the old keys of the network group. From numerous experiments, this work is proved to be the best in maintaining forward secrecy, backward secrecy and group compromise attacks and consumes less computation cost compared to any other hash-based key generation techniques.
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