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Title: An efficient decentralised multi layer architecture for unmanned aerial vehicle assisting vehicular adhoc networks
Researcher: Vanitha N
Guide(s): Padmavathi G
Keywords: Engineering and Technology
Computer Science
Computer Science Interdisciplinary Applications
University: Avinashilingam Institute for Home Science and Higher Education for Women
Completed Date: 2021
Abstract: Unmanned Aerial Vehicles (UAV) have gained a prominent role in this pandemic era. UAV assisting Vehicular Ad-hoc Network architecture holds U2V/V2U communication, applied in enormous applications, most notably in post-disaster operations. These UAVs can hover alone in the air layer or function remotely without carrying any operator. UAV ad-hoc networks have developed an evolving research field consisting of a collection of small UAVs linked in ad-hoc mode. Those type of networks is distinguished by high mobility and regular topology fluctuations, which establishes networking problems. newlineTo solve such problems, selecting a suitable communication architecture and reliable routing protocols is compulsory to validate robust communication among the UAVs. This research work aims to devise a suitable decentralised architecture for Unmanned Aerial Vehicle assisting Vehicular Ad-hoc Networks with improved Quality of Service and security. This work explores the existing architecture of UAVs for post-disaster scenarios. Furthermore, this work proposes a Decentralised Multi-layer UAV assisting VANET (DMUAV) architecture with enhanced Quality of Service and security newlineAdditionally, the delay-tolerant nodes are employed in the DMUAV architecture to prevent delays and packet losses. As a result, there will be no packet losses in the Delay tolerant DMUAV(DDMUAV) architecture. Further, to improve the throughput and minimize the delays due to the flooding nature of the Epidemic routing protocol, an enhanced Binary Spray and Wait (BSnW) routing protocol with controlled replication is proposed. This model provides communication efficiency. The BSnW protocol with controlled replication offers better throughput, low delay and minimum packet loss ratio and also it is scalable. Finally, traffic is monitored and analyzed using the Enhanced Deep Feed-Forward Neural Networks with a Backpropagation (EDFFNNBP) algorithm to provide security. This feature handles the false data injection attacks with a reasonable detection rate.
Pagination: 166
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File176.84 kBAdobe PDFView/Open
02_certificate.pdf383.4 kBAdobe PDFView/Open
03_acknowledgement.pdf181.55 kBAdobe PDFView/Open
04_contents.pdf392.95 kBAdobe PDFView/Open
05_list tables, fiugres and abbreviations.pdf481.74 kBAdobe PDFView/Open
06_chapter 1.pdf851.75 kBAdobe PDFView/Open
07_chapter 2.pdf859.2 kBAdobe PDFView/Open
08_chapter 3.pdf679.5 kBAdobe PDFView/Open
09_chapter 4.pdf1.16 MBAdobe PDFView/Open
10_chapter 5.pdf970.96 kBAdobe PDFView/Open
11_chapter 6.pdf1.23 MBAdobe PDFView/Open
12_chapter 7.pdf1.11 MBAdobe PDFView/Open
13_chapter 8.pdf565.39 kBAdobe PDFView/Open
14_chapter 9.pdf558.6 kBAdobe PDFView/Open
15_references.pdf621.42 kBAdobe PDFView/Open
16_annexures.pdf1.42 MBAdobe PDFView/Open
80_recommendation.pdf457.05 kBAdobe PDFView/Open
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