Optimizing Multi-Factor Weight Assignment in Wireless Networks with GWO

Problem Definition

In Wireless Sensor Networks (WSNs), the process of Cluster Head (CH) selection and classification plays a crucial role in ensuring the efficient operation of the network and effective management of data. However, the existing approaches in this domain often fall short of considering essential factors, leading to suboptimal performance and inefficiencies within the network infrastructure. Moreover, the classification models employed in WSNs are oftentimes limited in their effectiveness, impeding accurate data classification and decision-making processes. A comprehensive literature review has brought to light several proposed techniques, with the Enhanced Overlapping Set Reduction (EOSR) technique, as outlined in [19], showing promise in enhancing efficiency within WSNs. Nonetheless, despite its potential advantages, shortcomings have been identified in the EOSR approach, necessitating the development of enhancements and improvements to address these limitations effectively.

It is evident from the existing research that there is a pressing need to optimize CH selection and classification models within WSNs to enhance network performance, improve data management, and bolster decision-making capabilities.

Objective

The objective of this research is to enhance the efficiency of Wireless Sensor Networks (WSNs) by addressing the limitations in Cluster Head (CH) selection and classification. The proposed approach includes considering additional factors such as distance between nodes, trust factor, residual energy, and hop count, in addition to using the Grey Wolf Optimization (GWO) algorithm to determine optimal weight values for these factors. By incorporating these enhancements, the aim is to improve network performance, data management, and decision-making capabilities in WSNs.

Proposed Work

Therefore, a novel approach is proposed in this paper that takes into consideration the previous limitations. As stated earlier that conventional work consists of only three factors which are not sufficient enough and thus it is required to consider a more efficient factor. In the proposed work, another factor i.e. distance between nodes is taken into account.

It is a very significant factor as it will determine the quality of the system. The energy also depends on the distance factor in such a way that with the increase in distance between nodes, the nodes require to travel more to reach the destination node and thus it consumes more energy. Thus, the proposed work consists of a total of four factors which are: Distance between nodes, Trust factor, Residual energy, and Hop Count. Now, with the increase in the number of parameters, it is required to determine the weight value for increased factors also. Instead of defining the weight values statically (as in the previous approach), the proposed approach automates the system for which an optimization algorithm is used.

In the proposed work, the Grey Wolf Optimization (GWO) algorithm is used, which will automatically make decisions on what weight value of the four factors should be taken. It will help to choose the optimal weight value so that the packet delivery ratio and network throughput can be enhanced. The GWO algorithm is used in this because of its simplicity, flexibility, derivative-free and local minimal prevention features. Therefore, the proposed approach with GWO optimization and an enhanced number of parameters can help achieve efficient system performance.

Application Area for Industry

This project can be applied in various industrial sectors such as smart manufacturing, agriculture, healthcare, and environmental monitoring. In smart manufacturing, the optimized Cluster Head selection and classification models can improve the efficiency of data collection and decision-making processes in sensor networks, leading to better production management and cost savings. In agriculture, the enhanced network performance can help in monitoring soil conditions, crop health, and irrigation systems more effectively, leading to increased yields and reduced water usage. In the healthcare sector, the optimized network operation can improve patient monitoring systems and ensure timely data transmission for better diagnosis and treatment planning. Additionally, in environmental monitoring, the efficient data management and decision-making capabilities can aid in predicting natural disasters, monitoring air and water quality, and preserving ecosystems.

Overall, the implementation of this project's proposed solutions can address specific challenges industries face in managing sensor networks, leading to improved operational efficiency, enhanced data accuracy, and better decision-making capabilities across various industrial domains.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of Wireless Sensor Networks (WSNs). By addressing the limitations in existing Cluster Head (CH) selection and classification models, the project can offer insights into improving network performance, data management, and decision-making capabilities in WSNs. The inclusion of factors such as distance between nodes, trust factor, residual energy, and hop count in the proposed approach enhances the complexity of the model, leading to a more comprehensive and accurate CH selection process. This approach allows for a more sophisticated analysis of network dynamics and energy consumption, ultimately contributing to the advancement of research in WSNs. The utilization of the Grey Wolf Optimization (GWO) algorithm in the proposed work further enhances its impact by automating the determination of weight values for the factors considered.

By optimizing the weight values, the project aims to improve packet delivery ratio and network throughput, offering a more efficient and reliable system performance. Researchers, MTech students, and PHD scholars working in the field of WSNs can leverage the code and literature of this project for their own work. They can explore the implementation of the GWO algorithm in optimizing CH selection and classification models, as well as understanding the impact of including additional factors in the analysis. The relevance of this project extends to the development of innovative research methods, simulations, and data analysis techniques within educational settings. By exploring the potential applications of the proposed approach, educators can provide students with practical insights into WSNs and optimization algorithms, fostering a deeper understanding of complex network systems.

In the future, the project could serve as a foundation for further research and advancements in WSNs, paving the way for the development of new algorithms and techniques to enhance network efficiency and performance. The integration of emerging technologies and research domains can offer exciting opportunities for academic exploration and practical applications in the field of WSNs.

Algorithms Used

The GWO algorithm is used in the proposed work to automatically determine weight values for four factors - distance between nodes, trust factor, residual energy, and hop count. This optimization algorithm helps improve system performance by selecting optimal weight values, enhancing packet delivery ratio and network throughput. GWO is chosen for its simplicity, flexibility, optimal discovery and exploitation capabilities, and ability to prevent local minima. It is a more efficient approach compared to conventional methods, as it considers additional factors and automates the decision-making process to achieve better results in the system.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, WSNs, Cluster Head selection, data management, classification models, Enhanced Overlapping Set Reduction, EOSR, network performance, decision-making processes, optimization, novel approach, distance between nodes, trust factor, residual energy, hop count, weight values, optimization algorithm, Grey Wolf Optimizer, GWO algorithm, packet delivery ratio, network throughput, multi-factor weight assignment, routing protocols, wireless communication, quality of service, resource allocation, traffic load balancing, energy efficiency, latency reduction, network congestion, efficient system performance.

SEO Tags

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