Optimized DEMBO Approach for Maximizing Sensor Network Lifespan

Problem Definition

Wireless Sensor Networks (WSNs) play a crucial role in collecting data and facilitating communication in various applications such as environmental monitoring, healthcare, and smart homes. One of the key issues faced in WSNs is the limited energy resources of sensor nodes, which often leads to network failures and reduced performance. To address this challenge, the formation of clusters within WSNs is a common strategy to distribute energy consumption evenly and prolong the network's lifespan. However, the selection of Cluster Heads (CH) within each cluster and the optimal clustering algorithm choice are vital decisions that significantly impact the network's overall efficiency and longevity. Despite the availability of numerous clustering optimization algorithms, the challenge lies in determining the most suitable algorithm and fine-tuning its parameters to achieve the best performance results.

This necessitates the need for advanced research and innovative approaches to enhance the decision-making process and improve the effectiveness of WSNs in various applications.

Objective

The objective of this study is to address the challenge of limited energy resources in Wireless Sensor Networks (WSNs) by optimizing clustering algorithms to prolong the network's lifespan and improve efficiency. The proposed work focuses on implementing the Gravitational Search algorithm (GSA) and Monarchy Butterfly optimization (MBO) algorithm in two phases to select Cluster Heads (CH) effectively. By comparing the results with the traditional LEACH technique, the study aims to determine which algorithm produces more efficient results. Additionally, the integration of the Differential Evolution (DE) algorithm with MBO as DEMBO is proposed to overcome the MBO algorithm's limitations and enhance its performance in solving network problems.

Proposed Work

A large number of optimization algorithms are already available that give good results in clustering. However, one of the biggest challenges faced in WSNs is to decide which optimization algorithm to be selected as well as what parameters needs need to be defined for it. To achieve this, the proposed model works in two phases. In the first phase, two optimization algorithms namely Gravitational Search algorithm (GSA) and Monarchy Butterfly optimization (MBO) algorithm are selected and implemented. The GSA algorithm helps in finding the efficient energy routing protocol and MBO is utilized to select the CH in the wireless sensor network effectively.

The two algorithms are then compared with the traditional LEACH technique to observe which technique is producing more efficient results. The simulation results were obtained for GSA and MBO which shows the MBO is producing slightly better results than traditional LEACH and GSA techniques which are described in the next section. However, the MBO is time consuming and it gets stucked in the local minima. This problem of MBO algorithm can be eliminated by integrating the DE algorithm that can perform search operations efficiently [19]. Inspired from this combined approach of DE and MBO is implemented to solve the network problem in our proposed work.

The main improvement in traditional MBO is that the Differential evolutionary (DE) algorithm is used as an adaption in the MBO algorithm by crossover technique. The performance of the MBO can be enhanced by integrating it with DE algorithm as DEMBO.

Application Area for Industry

This project can be applied in various industrial sectors such as agriculture, environmental monitoring, healthcare, and smart cities where Wireless Sensor Networks (WSNs) are utilized to gather data efficiently. The proposed solutions of incorporating optimization algorithms like Gravitational Search algorithm (GSA), Monarchy Butterfly optimization (MBO), and Differential evolutionary (DE) algorithm address the challenge of optimal clustering and Cluster Head (CH) selection within WSN networks. By integrating these algorithms, industries can enhance the lifespan and performance of their WSN networks, leading to more efficient data collection, improved energy routing protocols, and effective CH selection. The benefits of implementing these solutions include increased network efficiency, optimized resource allocation, reduced energy consumption, and overall improved system performance. This project's innovative approach offers a comprehensive solution to the challenges faced by industries utilizing WSN networks, ensuring optimal operation and longevity.

Application Area for Academics

The proposed project can greatly enrich academic research in the field of Wireless Sensor Networks (WSNs) by addressing the critical challenge of optimal clustering and Cluster Head (CH) selection. By comparing optimization algorithms such as Gravitational Search algorithm (GSA) and Monarchy Butterfly optimization (MBO) with the traditional LEACH technique, researchers can gain insights into which techniques yield more efficient results. Additionally, by integrating the Differential Evolutionary (DE) algorithm into MBO to create DEMBO, the project offers a novel approach to improving the performance of clustering in WSNs. This project has significant relevance in the education and training of researchers, MTech students, and PhD scholars in the field of WSNs. The code and literature generated from this research can serve as a valuable resource for students and scholars looking to explore innovative research methods, simulations, and data analysis within educational settings.

By using the proposed algorithms and techniques, students can gain practical experience in optimizing clustering algorithms for improved performance in WSNs. The project's potential applications extend to various technology and research domains related to WSNs, offering a platform for researchers and students to delve into advanced optimization techniques. Researchers specializing in WSNs can leverage the findings of this project to enhance their studies and develop new approaches for optimizing clustering and CH selection. MTech students can utilize the code and methodologies implemented in this project for their thesis work, while PhD scholars can build upon this research to explore new avenues in the optimization of WSN networks. In terms of future scope, the project opens avenues for further exploration and refinement of clustering algorithms in WSNs.

Researchers can continue to investigate the integration of different optimization techniques to enhance the performance of clustering algorithms. Additionally, the project sets the stage for exploring the application of these optimized algorithms in real-world WSN scenarios, paving the way for practical implementation and deployment.

Algorithms Used

The proposed work incorporates two optimization algorithms, Gravitational Search Algorithm (GSA) and Monarchy Butterfly Optimization (MBO), in the first phase to address the challenge of selecting efficient energy routing protocols and choosing Cluster Heads (CH) in wireless sensor networks (WSNs). GSA is utilized to find the energy routing protocol, while MBO is employed for effective CH selection. The performance of these algorithms is compared with the traditional LEACH technique to determine their efficiency in WSN optimization. Although MBO yields slightly better results compared to LEACH and GSA, it is hampered by time-consuming operations and the risk of getting stuck in local minima. To mitigate these issues, the proposed approach integrates the Differential Evolution (DE) algorithm with MBO to form a combined algorithm called DEMBO.

By incorporating DE through a crossover technique, the performance of MBO is enhanced, allowing for more efficient search operations and improved overall results in solving network optimization problems.

Keywords

Wireless Sensor Networks, WSNs, clustering, Cluster Head selection, optimization algorithms, Gravitational Search algorithm, GSA, Monarchy Butterfly optimization, MBO, LEACH technique, energy routing protocol, efficient CH selection, simulation results, traditional MBO, DE algorithm, Differential evolutionary algorithm, DEMBO, network problem solving, collaborative optimization, metaheuristic algorithms, distributed systems, network performance, resource allocation, quality of service, data aggregation, data routing, energy conservation, sensor node coordination.

SEO Tags

wireless sensor networks, cluster head selection, energy efficiency, collaborative optimization, optimization algorithms, metaheuristic algorithms, distributed systems, network performance, resource allocation, quality of service, data aggregation, data routing, energy conservation, sensor node coordination, Gravitational Search algorithm, Monarchy Butterfly optimization algorithm, LEACH technique, DE algorithm, DEMBO, research study, PHD research, MTech project, research scholar, simulation results.