A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Anonymous authentication mechanism based on group signature and pseudonym public key infrastructure for safety application of vechicular ad hoc network

Safety applications of Vehicular Ad hoc Network (VANET) demand delay intolerant and are vulnerable to attacks due to the mobility of nodes and wireless nature of their communications. These applications require an integrated security mechanism, which provides message integrity, anonymity, non-repudiation, revocation, availability, and location authentication services. This mechanism should provide acceptable message delay with or without dependency to Road Side Units (RSUs). Realizing the importance of VANET security, two mechanisms are proposed and evaluated in this research. The mechanisms are aimed at fulfilling the VANET security requirements for safety applications with acceptable message delay. Two new lightweight security mechanisms, RSU-Aided Anonymous Authentication (RAAA) and Group Signature-based Anonymous Authentication (GSAA) have been proposed. These mechanisms are based on Group Signature (GS) and Pseudonym Public Key Infrastructure (PPKI). GS scheme was applied to ensure anonymity, non-repudiation and revocation, whereas PPKI was applied to achieve authentication and message integrity. Additionally, a novel function for location verification was proposed to guarantee availability and location authentication. Simulations were performed using NS2 to verify and evaluate the efficiency of the mechanisms for urban and highway scenarios with various traffic conditions. Simulation results showed that RAAA and GSAA outperformed Group Signature and Identity-based Signature (GSIS), and Short-Term Linkable Group Signatures with Categorized Batch Verification (STLGSCBV). In comparison to GSIS and STLGSCBV, the results indicated improvements of at least 5.26% and 7.95% in terms of vehicle density impact on message delay, and at least 11.65% and 11.22% in the case of vehicle density impact on message loss ratio. Furthermore, the simulated RAAA and GSAA methods resulted in approximately 11.09% and 10.71% improvement in message delay during signature verification in comparison to GSIS and STLGSCBV. Additionally, RAAA and GSAA proved to achieve at least 13.44% enhancement by considering signature verification on message loss ratio in comparison to GSIS and 7.59% in comparison to STLGSCBV. The simulation results also demonstrated that less than 20ms message delay was achieved by RAAA and GSAA mechanisms in the case of less than 90 vehicles within the communication range. This is an acceptable message delay and hence, the proposed mechanisms have a great potential to be used in safety critical applications