Wireless Sensor Network (WSN) Model Targeting Energy Efficient Wireless Sensor Networks Node Coverage and Routing using ACO Optimization

Project Code :TMMAWS94

Objective

The objective is to develop energy-efficient coverage strategies for wireless sensor networks, optimizing node deployment and enhancing monitoring efficiency.

Abstract

The use cases for wireless sensor networks (WSN) are growing as the Internet of Things gains traction. Application requirements are growing across a range of fields, from agricultural to urban infrastructure monitoring. In order to increase data transmission reliability and energy efficiency, the research focuses on developing and refining energy-efficient coverage strategies for wireless sensor network nodes. By creating an energy-efficient sensor network node coverage model, the paper examines how to guarantee that every monitoring point is covered by at least one sensor node through in-depth investigation and analysis of hierarchical and flat routing protocols. In addition, the study investigates an energy-efficient coverage technique based on the upgraded gray wolf algorithm with the goal of optimizing sensor node deployment and improving the efficiency of the node's coverage. The algorithm obtains 100% coverage of monitoring target locations and performs substantially better in network coverage optimization, according to research data.
The enhanced gray wolf algorithm exhibits the lowest standard deviation and outstanding average performance under the 30-dimensional condition. In comparison to other algorithms, the enhanced gray wolf algorithm achieves 100% coverage performance more energy-efficiently and increases the coverage rate by 5.08% when there are 40 nodes. The investigation of energy-efficient wireless sensor network models will contribute to the advancement of intelligent monitoring and control in the future, enhance the effectiveness of resource consumption, lower maintenance costs, and support the long-term growth of wireless sensor networks. And at last, the routing will be done using ACO optimization to improve the node’s lifetime and minimizing energy consumption.

Keywords: Wireless Sensor Network, Node Coverage, Improved Grey Wolf Algorithm, ACO Routing Protocol, Monitoring Area.

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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