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

Software: Matlab 2020a or above
Hardware:
Operating Systems:
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
· 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