Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/545086
Title: Energy efficient design on MANET IOT routing using deepsense algorithm
Researcher: Suresh C
Guide(s): Srihari K
Keywords: Communication deep neural networks DeepSense
Computer Science
Computer Science Information Systems
DeepSense
Engineering and Technology
Internet of things
Internet protocol
MANETs
University: Anna University
Completed Date: 2021
Abstract: The Mobile Adhoc Network (MANET) generally represents a predefined topology with high-mobility sensor nodes moving in a random motion across the cross-section area opts for optimal communication from the data collection node to the sink node with proper dynamic communication links. The routing algorithm plays a major role in maintaining the efficacy of MANETs and conventional methods operates on disabling the problem of congestion and network faults by proper selection of transmission paths for better delivery of data packets with high packet delivery ratio. However, these methods undergo high level of overhead formation due to control and packet overheads prior sending the data packets and this prominently affects the efficiency of routing. The advancement in high-speed data acquisitions using Internet of Things (IoT) allows optimal data collection based on an interaction with the environment. It is further allowed to get interfaced with other type network like MANETs for data transmission. Interfacing IoT with MANETs forms a new network topology and the design should composed of high scalability and flexibility with its optimal deployment cost. newline
Pagination: xiv, 115p.
URI: http://hdl.handle.net/10603/545086
Appears in Departments:Faculty of Information and Communication Engineering

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02_prelim pages.pdf1.01 MBAdobe PDFView/Open
03_content.pdf507.66 kBAdobe PDFView/Open
04_abstract.pdf333.99 kBAdobe PDFView/Open
05_chapter 1.pdf383.41 kBAdobe PDFView/Open
06_chapter 2.pdf83.97 kBAdobe PDFView/Open
07_chapter 3.pdf1.1 MBAdobe PDFView/Open
08_chapter 4.pdf434.03 kBAdobe PDFView/Open
09_annexures.pdf84.75 kBAdobe PDFView/Open
80_recommendation.pdf74.73 kBAdobe PDFView/Open
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