Biblio

Filters: Author is Ghanshala, Kamal Kumar  [Clear All Filters]
2020-10-19
Sharma, Sachin, Ghanshala, Kamal Kumar, Mohan, Seshadri.  2019.  Blockchain-Based Internet of Vehicles (IoV): An Efficient Secure Ad Hoc Vehicular Networking Architecture. 2019 IEEE 2nd 5G World Forum (5GWF). :452–457.
With the transformation of connected vehicles into the Internet of Vehicles (IoV), the time is now ripe for paving the way for the next generation of connected vehicles with novel applications and innovative security measures. The connected vehicles are experiencing prenominal growth in the auto industry, but are still studded with many security and privacy vulnerabilities. Today's IoV applications are part of cyber physical communication systems that collect useful information from thousands of smart sensors associated with the connected vehicles. The technology advancement has paved the way for connected vehicles to share significant information among drivers, auto manufacturers, auto insurance companies and operational and maintenance service providers for various applications. The critical issues in engineering the IoV applications are effective to use of the available spectrum and effective allocation of good channels an opportunistic manner to establish connectivity among vehicles, and the effective utilization of the infrastructure under various traffic conditions. Security and privacy in information sharing are the main concerns in a connected vehicle communication network. Blockchain technology facilitates secured communication among users in a connected vehicles network. Originally, blockchain technology was developed and employed with the cryptocurrency. Bitcoin, to provide increased trust, reliability, and security among users based on peer-to-peer networks for transaction sharing. In this paper, we propose to integrate blockchain technology into ad hoc vehicular networking so that the vehicles can share network resources with increased trust, reliability, and security using distributed access control system and can benefit a wider scope of scalable IoV applications scenarios for decision making. The proposed architecture is the faithful environment for information sharing among connected vehicles. Blockchain technology allows multiple copies of data storage at the distribution cloud. Distributed access control system is significantly more secure than a traditional centralized system. This paper also describes how important of ad hoc vehicular networking in human life, possibilities in real-world implementation and its future trends. The ad hoc vehicular networking may become one of the most trendy networking concepts in the future that has the perspective to bring out much ease human beneficial and secured applications.
2020-11-02
Sharma, Sachin, Ghanshala, Kamal Kumar, Mohan, Seshadri.  2018.  A Security System Using Deep Learning Approach for Internet of Vehicles (IoV). 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :1—5.

The Internet of Vehicles (IoV) will connect not only mobile devices with vehicles, but it will also connect vehicles with each other, and with smart offices, buildings, homes, theaters, shopping malls, and cities. The IoV facilitates optimal and reliable communication services to connected vehicles in smart cities. The backbone of connected vehicles communication is the critical V2X infrastructures deployment. The spectrum utilization depends on the demand by the end users and the development of infrastructure that includes efficient automation techniques together with the Internet of Things (IoT). The infrastructure enables us to build smart environments for spectrum utilization, which we refer to as Smart Spectrum Utilization (SSU). This paper presents an integrated system consisting of SSU with IoV. However, the tasks of securing IoV and protecting it from cyber attacks present considerable challenges. This paper introduces an IoV security system using deep learning approach to develop secure applications and reliable services. Deep learning composed of unsupervised learning and supervised learning, could optimize the IoV security system. The deep learning methodology is applied to monitor security threats. Results from simulations show that the monitoring accuracy of the proposed security system is superior to that of the traditional system.