Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/468730
Title: | Human welfare and disaster Emergency alert system using Wireless sensor network |
Researcher: | Karthik, V |
Guide(s): | Suja, S |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems Markov Decision Process Markov chain model Byesian State Estimation |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | Wireless Sensor Network (WSN) is a promising method to afford the newlinecommunication to people by sensors, which people can safely evacuate newlinethrough a proposed framework during emergency scenario in building or newlineshopping mall. The proposed framework is designed with algorithms (a) newlineMarkov Decision system which is used to track the location of the pedestrian newlineand (b) Geographic Map-based Path method is used to locate the shortest path newlineof network to give an alert to the pedestrian in disaster affected building. The newlinemain intention of this technique is used to track the correct facts between newlinepedestrian and avoid heavy congestion when pedestrian discharge. The newlineoverall performance of proposed work provides the higher result in terms of newlinepacket delivery ratio and throughput. newlineThe next projected method is developed by Markov chain version with newlinePedestrian Signal Timing model for pedestrian escape route. This method newlinehelps to reduce the overhead occurred in geographic map-based path newlinediscovery during evacuation. Markov Decision Process is used to identify the newlineplace of pedestrian and not for the studies of pedestrian behaviour. In Markov newlinechain version, six level(S1 - standing, S2 - crawling, S3 - walking, S4 - newlineleaping, S5 - jogging and S6 - running) are carried out to describe the newlinebehavioural characteristics of pedestrian. The transition of a pedestrian is newlinedefined by the eight predominant moving directions (Directions - East, West, newlineSouth, North, Southeast, Northeast, Southwest and Northwest). At the same newlinetime, this work analyses the behaviour of pedestrian from two views, i.e., newlinepedestrian state and direction of pedestrian moving. In Pedestrian signal newlinetiming model, pedestrian crossing time is divided into response time and newlinecongestion time. newline |
Pagination: | xix,161p. |
URI: | http://hdl.handle.net/10603/468730 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 31.31 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 3.07 MB | Adobe PDF | View/Open | |
03_content.pdf | 527.2 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 9.54 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 491.42 kB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 156.13 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 364.6 kB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 875.24 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.05 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 705.59 kB | Adobe PDF | View/Open | |
11_chapter 7.pdf | 1.03 MB | Adobe PDF | View/Open | |
12_chapter 8.pdf | 436.49 kB | Adobe PDF | View/Open | |
13_annexures.pdf | 99.69 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 66.39 kB | Adobe PDF | View/Open |
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