Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/421890
Title: | Meta heuristic approaches for data Aggregation with secured routing in Wireless sensor networks |
Researcher: | Ashwinth J |
Guide(s): | Dhananjay Kumar |
Keywords: | Engineering and Technology Computer Science Computer Science Information Systems Aggregation Wireless sensor |
University: | Anna University |
Completed Date: | 2021 |
Abstract: | Wireless Sensor Networks are light weight heterogeneous sensor devices connected via adhoc links that enable dynamic configuration and reconfiguration Lack of power is a significant limitation while running a sensor newlinenode Sensors conceptually rely on a static energy level that is not recharged newlineand replaced in many cases Hence there is a significant restriction on the factors like energy-efficient design cooperative storage and retrieval of data network identification and coordination Meta heuristic optimization newlinetechniques are involved for aggregation in WSNs since they require lesser newlinecomputational effort and also render better optimization results in comparison newlineto conventional optimization techniques The location of sensor nodes is a newlinecritical problem for sensor network management and operation The goal of newlinelocalization is to determine the physical position of the sensors whose location newlineinformation is unknown Security is another concern in such optimized routing newlineapproaches since data conservation and attack mitigation are of paramount newlineimportance newlineThe proposed work aims to develop data aggregation techniques on newlineoptimization the task of data aggregation in WSN Three different methods are newlinehence developed namely Localization Based Evolutionary Routing (LOBER) newlineImproved Bee Colony Optimization Routing (IBCOR) and Insider Attack newlineDetection by Fuzzy logic (IADF) newlineA Localization Based Evolutionary Routing (LOBER) method is newlinedeveloped which is an energy-aware system to optimize the process of routing newlineIt uses a geometrical method of localizing the nodes and an evolutionary newlineoptimization algorithm that employs meta-heuristic techniques to optimize the newlinerouting process newline newline |
Pagination: | xiii,109p. |
URI: | http://hdl.handle.net/10603/421890 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 80.75 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | 2.45 MB | Adobe PDF | View/Open | |
03_content.pdf | 84.41 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 66.06 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 14.8 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 11.24 MB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 8.95 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 4.74 MB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 5.74 MB | Adobe PDF | View/Open | |
10_annexures.pdf | 4.81 MB | Adobe PDF | View/Open | |
80_recommendation.pdf | 2.03 MB | Adobe PDF | View/Open |
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