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

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01_title.pdfAttached File80.75 kBAdobe PDFView/Open
02_prelim pages.pdf2.45 MBAdobe PDFView/Open
03_content.pdf84.41 kBAdobe PDFView/Open
04_abstract.pdf66.06 kBAdobe PDFView/Open
05_chapter 1.pdf14.8 MBAdobe PDFView/Open
06_chapter 2.pdf11.24 MBAdobe PDFView/Open
07_chapter 3.pdf8.95 MBAdobe PDFView/Open
08_chapter 4.pdf4.74 MBAdobe PDFView/Open
09_chapter 5.pdf5.74 MBAdobe PDFView/Open
10_annexures.pdf4.81 MBAdobe PDFView/Open
80_recommendation.pdf2.03 MBAdobe PDFView/Open
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