CLUSTERING OF NODES USING SMO AND SELFISH NODE DETECTION WITH OPTIMIZED TRUST COMPUTATION MODEL AND ROUTING WITH CHIMP - AODV BASED WSN
Objective
In a WSN, optimal cluster formation is crucial for balanced energy consumption. Using SMO-based clustering and Chimp AODV routing, we aim to reduce energy imbalance during packet transmission. A trust computation model identifies and isolates selfish nodes, improving network performance and reliability.
Abstract
In WSN, energy consumption won't balance until clusters are formed in the best possible way. Therefore, in WSN, it is necessary to establish optimal clusters. Using this method, we will apply SMO-based clustering to create ideal clusters and reduce imbalanced energy usage in a wireless sensor network. Additionally, in order to lower the energy usage during packet transmission, we want to deploy routing using Chimp AODV. We are creating a trust computation model in order to detect the selfish nodes that exist in the WSN and negatively impact the network's performance and trust. By tracking a node's behaviour over time, the trust computation model determines whether it is a normal or selfish node based on its reputation, or trust. A node that has been determined to be selfish will never longer receive packets from other nodes; instead, it will only send its own packets, and no other node will ever again relay messages to it. Keeping the network trustworthy and reducing packet loss in a manner.
Keywords: Wireless Sensor Network, Internet of Things, SMO Clustering Protocol, Chimp AODV, Network Lifetime.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
Software: Matlab 2020a or above
Hardware:
Operating Systems:
- Windows 10
- Windows 7 Service Pack 1
- Windows Server 2019
- Windows Server 2016
Processors:
Minimum: Any Intel or AMD x86-64 processor
Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support
Disk:
Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation
Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space
RAM:
Minimum: 4 GB
Recommended: 8 GB
Learning Outcomes
· Introduction to Matlab
· What is EISPACK & LINPACK
· How to start with MATLAB
· About Matlab language
· Matlab coding skills
· About tools & libraries
· Application Program Interface in Matlab
· About Matlab desktop
· How to use Matlab editor to create M-Files
· Features of Matlab
· Basics on Matlab
· What is Communication?
· About Communication
· Introduction to Communication
· How Communication Works?
· Importing the System Design, Characterization and Visualization
· Analyzing of BER tool
· Analyzing of Error Rate Test Console
· Generation of WSN
· WSN network creation
· Nodes Communication
· Clustering
· Routing
· Convolutional
· Equalization and Synchronization etc.,
· How to extend our work to another real time applications
· Project development Skills
o Problem analyzing skills
o Problem solving skills
o Creativity and imaginary skills
o Programming skills
o Deployment
o Testing skills
o Debugging skills
o Project presentation skills
o Thesis writing skills
Paper Publishing
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