An Innovative Approach using Grey Wolf Optimization for Enhanced CH Selection in WSN

Problem Definition

The existing literature on enhancing the efficiency of wireless sensor networks has highlighted the need for improved methods to decrease energy consumption. Previous studies have relied on optimization algorithms for cluster head selection, taking into account various quality of service parameters such as energy and node degree. However, traditional models have been found to have limitations in network distribution, as nodes were randomly distributed, leading to communication challenges for cluster heads. Additionally, these models struggled to effectively address complex issues within the network. This gap in existing research underscores the necessity for a new approach that can overcome the shortcomings of traditional techniques and improve the overall performance of wireless sensor networks.

Objective

The objective is to develop an energy-efficient protocol for wireless sensor networks using the Grey Wolf Optimization algorithm to optimize cluster head selection and improve overall network performance. This approach aims to overcome communication challenges, extend the lifespan of WSNs, and enhance network efficiency by revamping the network formation model and evaluating factors like energy consumption balance and the number of surviving nodes. The goal is to offer a practical and sustainable solution that addresses the limitations of traditional methods and ensures optimal network performance.

Proposed Work

To address the issues identified in the problem definition, the proposed work aims to develop an energy-efficient protocol for wireless sensor networks (WSNs) using the Grey Wolf Optimization algorithm (GWO). The GWO algorithm was chosen due to its high convergence rate and superior performance compared to other optimization algorithms. By utilizing GWO, the proposed model seeks to optimize cluster head selection based on various quality of service (QoS) parameters such as energy and node degree, ultimately extending the lifespan of WSNs. Additionally, the network formation model will be revamped to distribute sensor nodes uniformly, reducing network capacity issues and improving network grouping. By deploying the proposed scheme and evaluating factors such as network energy consumption balance, total energy consumption, and the number of surviving nodes, the effectiveness of the model will be assessed comprehensively.

In conclusion, the proposed approach combines innovative technology, such as the GWO algorithm, with a strategic network formation model to address the limitations of traditional methods and enhance the efficiency of WSNs. By focusing on energy optimization and network distribution, the project aims to overcome communication challenges and improve the overall performance of the network. The rationale behind choosing specific techniques like GWO lies in their proven effectiveness and ability to outperform other optimization algorithms. Through thorough evaluation and experimentation, the proposed work seeks to offer a practical and sustainable solution for extending the lifespan of WSNs while ensuring optimal network performance.

Application Area for Industry

This project can be applied in various industrial sectors such as manufacturing, agriculture, healthcare, and environmental monitoring. In the manufacturing sector, the proposed solutions can help in optimizing energy consumption for wireless sensor networks, leading to more efficient production processes. In agriculture, the project can assist in monitoring soil conditions, irrigation needs, and crop health, ultimately increasing crop yields. For healthcare, the project can be utilized to monitor patient vitals and ensure effective communication within medical facilities. In environmental monitoring, the solutions can aid in tracking pollution levels, wildlife habitats, and weather patterns for better conservation efforts.

By implementing the proposed model with the GWO algorithm and improving network formation strategies, these industries can benefit from increased energy efficiency, improved system reliability, and enhanced data collection capabilities, ultimately leading to cost savings and better operational performance.

Application Area for Academics

The proposed project on optimizing energy consumption in wireless sensor networks using the Grey Wolf Optimization algorithm has the potential to enrich academic research, education, and training in the field of networking and optimization. By employing a sophisticated optimization algorithm like GWO, researchers can explore new avenues for enhancing network efficiency and overcoming challenges faced by traditional methods. This project can serve as a learning tool for students in academic settings, providing them with hands-on experience in implementing advanced algorithms for solving real-world problems. MTech students and PHD scholars working in the domain of wireless sensor networks can benefit from the code and literature of this project to further their research and develop innovative solutions. The relevance of this project lies in its potential applications for optimizing energy consumption in WSNs, which is a critical issue in the field of IoT and sensor networks.

By focusing on cluster head selection and network formation, the project addresses key challenges faced by network designers and operators. In pursuing innovative research methods, simulations, and data analysis, researchers can leverage the GWO algorithm to optimize network performance and enhance the longevity of sensor nodes. The project's focus on evaluating factors such as network energy consumption balance analysis and total energy consumption can provide valuable insights for researchers looking to improve network efficiency. Future scope for this project includes exploring the application of GWO in other networking scenarios and expanding the optimization framework to address additional performance metrics. By continuing to refine and enhance the proposed model, researchers can contribute to the advancement of optimization techniques in wireless sensor networks and open up new possibilities for academic research and innovation.

Algorithms Used

The Grey Wolf Optimization (GWO) algorithm is utilized in this project to address the limitations of conventional approaches. GWO is chosen for its high convergence rate and superior performance compared to other optimization algorithms. The algorithm is used to optimize the network formation model, ensuring the uniform installation of sensor nodes to minimize network capacity issues. This approach facilitates effective network grouping and creates a systematic operating environment for the nodes. The performance of the proposed scheme will be evaluated post-deployment, considering factors such as network energy consumption balance, total energy consumption, and the number of surviving nodes in the Wireless Sensor Network (WSN).

Keywords

SEO-optimized keywords: wireless sensor networks, optimization algorithms, Grey Wolf Optimization, GWO algorithm, network efficiency, energy consumption, QoS parameters, cluster head selection, network distribution, communication challenges, network grouping, sensor nodes, network capacity, network performance evaluation, energy efficiency analysis, metaheuristic algorithms, swarm intelligence, data transmission, data aggregation, routing protocols, resource allocation.

SEO Tags

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