Enhancing WSN Lifespan with Improved Grasshopper Optimization Algorithm and TLBO

Problem Definition

After reviewing existing literature on the enhancement of Wireless Sensor Network lifespan, it is evident that while numerous models have been proposed by researchers, there remains a need for improvement in this domain. Many current models focus primarily on the selection of Cluster Heads (CH) as a means of prolonging network lifespan, neglecting the crucial aspect of uniform node distribution within the sensing region. This oversight suggests a gap in the existing approaches, highlighting the need for a new routing approach that considers the importance of node distribution for network longevity. Furthermore, existing optimization algorithms used for CH selection suffer from slow convergence rates and a tendency to become trapped in local minima, ultimately leading to increased processing time and diminished overall performance of the models. Addressing these limitations is imperative for the development of an effective and efficient Wireless Sensor Network routing approach.

Objective

The objective of the project is to develop a new approach for Wireless Sensor Networks (WSNs) that addresses existing limitations by focusing on uniform node deployment and efficient Cluster Head (CH) selection. By utilizing the Delaunay algorithm for holes detection, the Teaching Learning based Optimization (TLBO) algorithm for node deployment, and the Improved Grasshopper Optimization Algorithm (IGOA) for CH selection, the model aims to improve energy efficiency and communication performance. Through the incorporation of advanced optimization algorithms, the project aims to overcome issues such as slow convergence rates and local minima traps observed in existing models. The goal is to provide a more efficient and effective solution for enhancing WSN lifespan by optimizing energy consumption and network performance through improved node distribution and CH selection strategies.

Proposed Work

In this project, the goal is to address the existing limitations in Wireless Sensor Networks (WSNs) by developing a new approach that focuses on enhancing network lifespan through uniform node deployment and efficient Cluster Head (CH) selection. The problem definition highlights the research gap in the existing literature, where current models mainly concentrate on CH selection parameters to improve network longevity. However, the proposed work aims to utilize the Delaunay algorithm for holes detection, the Teaching Learning based Optimization (TLBO) algorithm for uniform node deployment, and the Improved Grasshopper Optimization Algorithm (IGOA) for enhanced CH selection, inspired by the LEACH protocol. By focusing on factors such as Residual energy, neighboring node distance, node degree, and distance to sink, the model calculates the fitness function to achieve energy efficiency and communication improvement. By incorporating advanced optimization algorithms and utilizing the strengths of each one in the context of WSN routing, the proposed approach aims to overcome the issues of slow convergence rates and local minima traps that have been observed in existing models.

The new model's approach of node distribution, cluster formation, CH selection, and communication phase is designed to optimize energy consumption and enhance the overall network performance. Ultimately, the goal of this project is to provide a more efficient and effective solution for enhancing the lifespan of WSNs, by addressing the critical factors related to node distribution and CH selection through the utilization of state-of-the-art optimization techniques.

Application Area for Industry

This project can be applied in various industrial sectors such as agriculture, environmental monitoring, smart cities, and manufacturing. In agriculture, the proposed solutions can help in optimizing irrigation systems by efficiently monitoring soil moisture levels. For environmental monitoring, the project can assist in tracking air quality and pollution levels with the help of the distributed sensor network. In smart cities, the solutions can be used for managing traffic flow, monitoring waste management, and enhancing overall urban infrastructure. In the manufacturing sector, the project can help in optimizing energy consumption, monitoring equipment performance, and improving overall productivity.

By deploying nodes uniformly in the sensing region and utilizing optimization algorithms for CH selection, the proposed solutions can address the challenges of network lifespan, energy efficiency, and processing time in various industrial domains. The benefits of implementing these solutions include increased network longevity, reduced energy consumption, improved data accuracy, and enhanced overall performance of industrial processes.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training by providing a new and effective energy-efficient approach to enhancing the lifespan of Wireless Sensor Networks (WSN). By addressing the limitations of existing routing efficiency protocols and focusing on deploying nodes uniformly in the sensing region, the project offers a novel solution for reducing energy consumption and improving the overall performance of WSNs. Researchers, MTech students, and PhD scholars in the field of wireless communication and network optimization can benefit from the code and literature of this project for conducting innovative research methods, simulations, and data analysis within educational settings. The utilization of Improved Grasshopper Optimization Algorithm (IGOA) and Teaching Learning based Optimization (TLBO) algorithms in the proposed model provides a unique opportunity for researchers to explore and implement advanced optimization techniques in their work. The integration of algorithms such as LEACH and Delaunay triangulation in the project offers a comprehensive framework for addressing the challenges of CH selection and network longevity in WSNs.

By considering parameters such as residual energy, average distance between neighboring nodes, node degree, and distance to sink, the proposed model aims to optimize the network structure and enhance its performance. In conclusion, the project's relevance lies in its potential to advance the field of wireless sensor networks through the development of an efficient and sustainable routing approach. The future scope of this work includes further optimization of algorithms, experimentation with real-world data, and collaboration with industry partners for practical implementation.

Algorithms Used

TLBO is utilized for deploying nodes uniformly and selecting Cluster Heads (CHs) in the network. IGOA enhances the network's lifespan by optimizing the parameters of Residual energy, Average distance between neighboring nodes, Node degree, and Distance to sink through calculating the fitness function. LEACH is employed for Cluster Formation and CH selection. Delaunay triangulation assists in the task of Node Distribution. All these algorithms work together to improve the efficiency and accuracy of the routing protocol, contributing to achieving the project's objectives of enhancing network lifespan and reducing energy consumption.

Keywords

SEO-optimized keywords: Sensor networks, Cluster head selection, Network stability, Advanced approach, Energy efficiency, Network lifetime, Data aggregation, Data routing, Clustering algorithms, Cluster formation, Network topology, Network performance, Node selection, Self-organization, Energy conservation, Wireless communication, Artificial intelligence, Improved Grasshopper Optimization Algorithm, Teaching Learning based Optimization, Wireless sensor network, Energy efficient approach, Node distribution, Residual energy, Average distance between neighboring nodes, Node degree, Distance to sink, Fitness function, Communication Phase

SEO Tags

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