Decision-Driven Approach Using BAT, Fuzzy Logic, and FCM for Efficient Network Clustering in Wireless Sensor Networks

Problem Definition

The existing literature on network lifetime enhancement reveals that while several approaches have been successful in improving the efficiency of networks, some conventional algorithms have fallen short in properly utilizing resources. These algorithms have shown complexity or have failed to consider important Quality of Service (QoS) factors, leading to limitations in network lifespan. Recent research has turned towards clustering and developing Cluster Head (CH) selection models, with techniques such as fuzzy c mean or k-mean algorithms being used for clustering and energy or distance-based criteria for CH selection. However, it has been noted that other parameters could also play a significant role in network longevity. By incorporating metaheuristic approaches for CH selection, the complexity of these models can be reduced.

Thus, there is a need for an advanced energy-efficient protocol that considers various QoS factors including residual energy, number of nodes in the cluster, and distance from the cluster center to extend the lifespan of the network.

Objective

The objective of this project is to develop an advanced energy-efficient protocol that enhances the lifespan of a network by deploying nodes uniformly in the sensing region to optimize energy efficiency. This protocol will utilize Fuzzy c-means clustering and BAT optimization in collaboration with a fuzzy logic algorithm for improved cluster head selection. The goal is to consider various Quality of Service (QoS) factors such as residual energy, number of nodes in the cluster, and distance to the cluster center to increase the network lifespan. By incorporating metaheuristic approaches and advanced algorithms, the complexity of the models can be reduced, leading to a more efficient network setup. The proposed approach aims to deploy nodes uniformly, use Fuzzy c-means clustering, and employ BAT-Fuzzy combined optimization algorithm for effective cluster head selection to extend the network's lifespan.

Additionally, the simulation setup in MATLAB will consider a 100x100m2 area with 100 nodes distributed randomly within grids formed by FCM. The selection of GHs and CHs will be based on fitness values calculated using a proposed fuzzy model and BAT optimization algorithm.

Proposed Work

In order to address the research gap identified in the literature review, an advanced energy-efficient protocol is proposed in this project to enhance the network lifespan. The main objective is to deploy nodes uniformly in the sensing region to optimize energy efficiency. This involves developing a Fuzzy c-means clustering protocol and utilizing BAT optimization in collaboration with a fuzzy logic algorithm for improved cluster head selection. The proposed approach aims to consider various QoS factors such as residual energy, number of nodes in the cluster, and distance to the cluster center to increase the network lifespan. By utilizing metaheuristic approaches and advanced algorithms, the complexity of the models can be reduced, leading to a more efficient network setup.

The proposed work includes deploying nodes uniformly in the network to ensure optimal coverage of the sensing area, avoiding any coverage issues. Fuzzy c-means clustering of nodes and the collaboration of BAT-Fuzzy combined optimization algorithm are employed for effective cluster head selection, ultimately extending the network's lifespan. The simulation setup in MATLAB considers a 100x100m2 area with 100 nodes distributed randomly within grids formed by FCM. The selection of GHs and CHs is based on fitness values calculated using a proposed fuzzy model and BAT optimization algorithm. The fuzzy model considers key QoS parameters and processes them through defined rules to determine the fitness value of each node, with the BAT algorithm selecting the cluster head based on the highest fitness value.

Overall, the proposed approach aims to improve energy efficiency and network lifespan through optimal node deployment and advanced clustering and optimization algorithms.

Application Area for Industry

This project can be applied in various industrial sectors such as agriculture, environmental monitoring, smart cities, and healthcare. In agriculture, the proposed solutions can help in monitoring crop conditions, optimizing irrigation processes, and increasing agricultural productivity. In environmental monitoring, the project can assist in tracking pollution levels, monitoring natural disasters, and preserving wildlife habitats. In smart cities, the solutions can be used for traffic management, waste management, and energy efficiency. In healthcare, the project can aid in remote patient monitoring, emergency response systems, and improving healthcare services delivery.

The specific challenges that industries face, such as limited network lifespan, inefficient use of resources, and complex algorithms, can be addressed by implementing the proposed solutions. By deploying nodes uniformly in the network, utilizing fuzzy c-means clustering, and applying the BAT-Fuzzy optimization algorithm for CH selection, industries can enhance network lifespan, improve data collection efficiency, and reduce energy consumption. The benefits of implementing these solutions include increased network stability, optimized data transmission, and enhanced overall system performance, leading to improved decision-making processes and better operational efficiency across various industrial domains.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of wireless sensor networks. By addressing the limitations of existing techniques and introducing an enhanced approach for maximizing network lifespan, researchers, MTech students, and PhD scholars can explore innovative research methods, simulations, and data analysis within educational settings. The use of Fuzzy c-means based clustering of nodes and the BAT-Fuzzy combined optimization algorithm in the proposed protocol opens up avenues for exploring new research methods in the domain of wireless sensor networks. The simulation setup in MATLAB provides a practical platform for conducting experiments, generating data, and analyzing results in an educational context. The code and literature of this project can be utilized by field-specific researchers, MTech students, and PhD scholars to understand the implementation of advanced energy efficient protocols, CH selection models, and optimization algorithms in wireless sensor networks.

By studying the proposed methods and algorithms, researchers can further enhance their work in improving network lifespan, optimizing resource utilization, and enhancing data transmission efficiency. The project can benefit researchers and students in the field of wireless sensor networks by providing a foundation for exploring new technologies, conducting simulations, and analyzing data in a controlled environment. This can lead to advancements in research methods, innovative solutions, and novel approaches for addressing challenges in network design and operation. In the future, the project can be extended to incorporate additional parameters, optimization techniques, and advanced algorithms for further enhancing the performance of wireless sensor networks. This will contribute to the ongoing development of cutting-edge solutions and methodologies in the field, offering new opportunities for academic research, education, and training.

Algorithms Used

BAT: BAT algorithm is used for optimizing the selection of Cluster Heads (CH) in the network. It utilizes a random population of nodes and selects the node with the highest fitness value as the CH. This helps in improving the efficiency of the network by ensuring that the most suitable nodes are selected as CHs. Fuzzy Logic: Fuzzy Logic is used in the proposed model to create a fuzzy interface system that takes into account three Quality of Service (QoS) parameters such as residual energy, number of nodes in a cluster, and Euclidean distance from the centroid. These parameters are processed using 27 defined rules to produce a weightage value, which acts as the fitness value of a node in the network.

This helps in enhancing the accuracy of CH selection by considering multiple factors. FCM (Fuzzy c-means): FCM algorithm is employed for clustering nodes in the network. It helps in forming clusters based on the location of nodes, which aids in organizing the network efficiently. By dividing the network into grids and forming clusters within those grids, FCM contributes to achieving the project's objectives of uniform node deployment and effective CH selection.

Keywords

wireless sensor networks, clustering protocol, Fuzzy-BAT, group formation, energy efficiency, network optimization, data aggregation, routing protocols, fuzzy logic, distributed systems, network performance, resource allocation, quality of service, energy conservation, sensor node coordination, network lifetime enhancement, metaheuristic approaches, CH selection model, generic algorithm, simulation setup, MATLAB simulation, node deployment, sensing area coverage, coverage issue, Fuzzy c-means algorithm, BAT-Fuzzy algorithm, grid formation, cluster formation, GH selection, CH selection, fuzzy model, BAT optimization algorithm, QoS parameters, residual energy, Euclidean distance, fitness value, multi-hopping communication, sensor node, sink node, network setup, data transmission.

SEO Tags

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