A Hybrid Fault-Tolerant Routing based on Gaussian Network for Wireless Sensor Network
Objective
The primary goal is to design a robust and efficient routing protocol that enhances the fault tolerance capabilities of WSNs
Abstract
In this paper, we have proposed a hybrid fault-tolerant routing to solve fault-tolerant issue in wireless sensor networks (WSNs) based on hierarchical topology. The hierarchical topology is a combination of clustering and the labeling of sensor nodes as Gaussian integers. Accordingly, the network area is divided into small square grids, the cluster head of each grid is represented by a Gaussian integer. These cluster heads are connected together to create a Gaussian network. Through node symmetry, and the shortest distance in the Gaussian network, as well as the advantages of multi-path routing, this paper proposes a hybrid fault-tolerant clustering routing protocol based on Gaussian network for wireless sensor network (FCGW). The purpose of FCGW is to improve fault tolerance, increase data reliability and reduce energy consumption for wireless sensor networks. The experimental results of the proposed scheme show that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes about 48% of the energy in the network, while other protocols consume 70% more energy.
Keywords: wireless sensor networks (WSNs), Gaussian Network, Hybrid Fault-Tolerant Routing.
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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