Detection of Falsified Selfish Node with Optimized Trust Computation Model in Chimp —AODV Based WSN

Project Code :TMMAWS89

Objective

To detect the selfish node using Optimized Trust Computation Model in WSN

Abstract

In Wireless Sensor Networks (WSNs), energy and security are two critical concerns that must be addressed. Because of the scarcity of energy, several security measures are restricted. For secure data routing in WSN, it becomes vital to identify insider packet drop attacks. The trust mechanism is an effective strategy for detecting this assault. Each node in this system validates the trustworthiness of its neighbors before transmitting packets, ensuring that only trust-worthy nodes get packets. With such a trust-aware scheme, however, there is a risk of false alarm. This work develops an adaptive trust computation model (TCM) which is implemented in our already proposed Chimp Optimization Algorithm-based Energy-Aware Secure Routing Protocol (COA-EASRP) for WSN. The proposed technique computes the optimal path using the hybrid combination of COA-EASRP and AODV as well as TCM is used to indicate false alarms in detecting selfish nodes. Our Proposed approach provides the series of Simulation outputs carried out based on various parameters.

Keywords: WSN, Falsified Node, Optimized Trust Computation Model, CHIMP –AODV.

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

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