A Centered Clustering and Weighted Scheme for Enhanced Mobility Support in Wireless Sensor Networks

Problem Definition

From the information gathered through literature review, it is evident that the existing routing techniques in Wireless Sensor Networks (WSN) have certain limitations and problems. The traditional models rely on energy-efficient routing techniques where the selection of cluster head is based on rotation and probability threshold values. However, these techniques face challenges when it comes to communication, particularly when the sink is located outside the cluster. This results in shorter network lifespan and higher power consumption due to the increased distance that the sink has to travel. Additionally, the traditional schemes suffer from network instability as cluster head selection is based on weightage computation using factors like residual energy and distances from the sink.

There is a pressing need for a novel routing scheme that can address these limitations and ensure successful data transmission, ultimately increasing the network's lifespan and stability.

Objective

The objective of this research is to introduce a more intelligent and optimized routing scheme for Wireless Sensor Networks (WSN) by implementing the Fuzzy C-means clustering algorithm for selecting Cluster Heads (CH). The primary goal is to improve the overall lifespan and stability of WSNs by efficiently choosing CH nodes based on their distance from the sink and other nodes in the network. By considering factors like residual energy, distance metrics, and average distance from cluster neighbors, the proposed technique aims to overcome the limitations of traditional routing models and enhance communication efficiency, reduce energy consumption, and increase the network's longevity. This innovative solution seeks to bridge the existing research gap in selecting optimal CHs for improved network performance in WSNs.

Proposed Work

To address the limitations of traditional models in Wireless Sensor Networks (WSN), a novel technique based on Fuzzy C-means clustering is proposed in this research paper. The primary objective is to efficiently select Cluster Heads (CH) in the network to improve the overall lifespan of WSNs. The proposed approach focuses on selecting nodes based on a combination of their distance from the sink and their distance from other nodes in the network. This dual criterion for CH selection aims to enhance routing efficiency and network stability. The Fuzzy C-means clustering algorithm is chosen for this purpose due to its ability to handle overlapping data sets better than the traditional k-means algorithm.

Additionally, the proposed technique considers the average distance of candidate CH nodes from their cluster neighbors as another key quality factor. By factoring in these parameters, such as residual energy, distance from the sink, and distance between cluster and CH nodes, the proposed approach aims to overcome the challenges faced by conventional models in terms of energy conservation and network longevity. This research work intends to introduce a more intelligent and optimized routing scheme for WSNs by implementing the Fuzzy C-means algorithm for CH selection. By incorporating a comprehensive evaluation of various parameters, the proposed technique strives to achieve a balanced distribution of data transmission distances among nodes in the network. As a result, the proposed approach is expected to enhance communication efficiency, reduce energy consumption, and ultimately increase the lifespan of WSNs.

Through this innovative solution, the study aims to contribute to the advancement of routing protocols in WSNs and address the existing research gap regarding the selection of optimal CHs for improved network performance.

Application Area for Industry

This project's proposed solutions can be applied in various industrial sectors that utilize Wireless Sensor Networks (WSN) for data collection and communication. Industries such as manufacturing, agriculture, healthcare, transportation, and environmental monitoring can benefit from the novel technique based on Fuzzy C means clustering. The challenges faced by these industries include energy efficiency, network stability, and lifespan of the network. By implementing the proposed scheme, industries can overcome these challenges by selecting cluster heads based on criteria that consider not only distance from the sink but also distance from other nodes in the network. This approach improves communication efficiency, reduces power consumption, and enhances network lifespan, ultimately leading to a more reliable and sustainable WSN infrastructure across different industrial domains.

Application Area for Academics

The proposed project focusing on enhancing routing conditions in Wireless Sensor Networks (WSN) can greatly enrich academic research, education, and training in the field of network communication and data transmission. By introducing a novel technique based on Fuzzy C Means clustering for selecting cluster heads, researchers and students can explore new methodologies for improving network performance and energy efficiency. This project has the potential to provide valuable insights into network communication strategies, data transmission optimization, and energy conservation in WSNs. By incorporating Fuzzy C Means clustering, students and researchers can explore advanced clustering algorithms and their applications in real-world scenarios. The relevance of this project lies in its potential applications for innovative research methods, simulations, and data analysis within educational settings.

Researchers, MTech students, and PHD scholars can utilize the code and literature of this project to further their work in network communication, routing protocols, and data transmission optimization. The specific technology and research domain covered in this project include Fuzzy C Means clustering, energy-efficient routing techniques, and network communication in WSNs. By delving into these areas, researchers and students can gain valuable insights into the challenges and opportunities in optimizing network performance. In terms of future scope, this project could pave the way for further research in energy-efficient routing techniques, cluster head selection algorithms, and network optimization strategies. By building upon the findings of this project, researchers and students can explore new avenues for enhancing network performance and energy conservation in WSNs.

Algorithms Used

Fuzzy C Mean is used in the proposed work to address the shortcomings of traditional models by selecting nodes as candidates for cluster head selection in a network. This algorithm is chosen for its ability to handle overlapping data sets better than k-means clustering. By considering factors such as distance from the sink and from other nodes in the network, the algorithm helps in enhancing routing efficiency. The proposed technique also evaluates the average distance of candidate cluster head nodes from their cluster neighbors to ensure equal data transmission distances for all nodes. This approach improves energy conservation, prolongs the network's lifespan, and enhances overall network performance.

Keywords

wireless sensor networks, routing, enhanced routing, fuzzy C-mean clustering, quality factor analysis, fuzzy logic, clustering algorithms, quality of service, network optimization, energy efficiency, data aggregation, data fusion, data routing, network performance, network reliability, network coverage, novel routing technique, energy-efficient routing, cluster head selection, communication, network instability, network lifespan, FCM clustering, k-means algorithm, overlapping data sets, candidate CH node, network routing enhancement, residual energy, distance with sink, average distance, network parameters, energy conservation, network lifespan increase.

SEO Tags

wireless sensor networks, routing techniques, energy-efficient routing, cluster head selection, communication in WSN, sink distance, network lifespan, network instability, novel routing scheme, Fuzzy C means clustering, node selection, soft clustering algorithm, k-means algorithm, quality factors in routing, residual energy, distance optimization, network performance analysis, energy conservation, data aggregation in WSN, network reliability, network coverage optimization.