A Density based Deceptive Data Detection in VANET
A. Sravya1, K. Dinesh2, S. Shiva Prasad3
1A. Sravya, Department of CSE, Vignan Foundation for Science and Technology, Guntur (A.P), India.
2K. Dinesh, Department of CSE, Vignan Foundation for Science and Technology, Guntur (A.P), India.
3S. Shiva Prasad, Department of CSE, Vignan Foundation for Science and Technology, Guntur (A.P), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 136-140 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10260275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The wireless network is the backbone of the VANET has shown more deceptive data send by malicious node. Those deceptive data may lead to unreliable wireless communication and also inaccurate sensing at the data. Therefore, it is important for detecting the deceptive data and improve the quality of the data in the VANET. So, in order to find those deceptive data in the VANET, there are different types in security aspects and reputation-based approaches, it is not sufficient for managing the quality of data in highly distributed and dynamic environment like VANET hence new algorithm had been proposed for verifying the deceptive data in VANET. The aim of the proposed algorithm is to find the deceptive data about the accident report generated in the VANET. So, as per the VANET mechanism if the accident happened, the accident report is sent from the accident vehicle or node through their sensor to the nearby vehicle and RSU [Road side unit]. The accident report is passed to nearby vehicle through the inter-vehicle communication or vehicle-infrastructure communication. Then the communication is divided into two types such as vehicle to vehicle communication (V2V) and vehicle-infrastructure communication (V2I). The density based deceptive data detection on VANET can be divided into two categories such as dense and spare parts. The dense parts use the clustering technique for finding the deceptive data over the communication whereas the sparse parts utilize the new technique by categories the nodes into two types such as private and public vehicle.
Keywords: Clustering, Deceptive Data Algorithm, Dense and Sparse Data, VANET.
Scope of the Article: Data Analytics