Dragonfly Optimization Algorithm (DA) and Fuzzy C-means (FCM) for Enhanced WSN Longevity and Energy Efficiency

Problem Definition

The challenge of energy consumption in wireless sensor networks (WSN) remains a critical issue that significantly impacts the overall network lifespan. Despite the development of various approaches aimed at reducing energy consumption and increasing network longevity, existing systems have been plagued with limitations that have hindered their performance. One common problematic area observed in the literature is the utilization of protocols such as LEACH and its variants, which fail to take into account the remaining energy levels of nodes when selecting cluster heads (CH) in the network. This oversight leads to inefficient energy usage and ultimately diminishes the effectiveness of these approaches. Moreover, traditional methods for CH selection have been found to be limited in their consideration of quality factors, overlooking the multitude of factors that can influence the process.

In some cases, the selection of CHs has been based on arbitrary threshold energy methods, further contributing to the suboptimal performance of these systems. In light of these challenges, there is a clear need for the development of a highly effective and energy-efficient model that can address these limitations, ultimately reducing energy consumption and enhancing the overall network lifespan.

Objective

The objective of this project is to address the challenge of high energy consumption in wireless sensor networks (WSN) by proposing a new energy-efficient model that overcomes the limitations of existing protocols like LEACH. The proposed model aims to enhance network lifespan by optimizing the selection of cluster heads (CHs) and grid heads (GHs) using the Dragonfly Optimization Algorithm (DA) and the Fuzzy C-means (FCM) algorithm, respectively. By considering factors such as residual energy, distance to GH, and delay for CH selection and quality parameters for GH selection, the model aims to improve energy consumption, network performance, and overall efficiency of WSNs. The objective is to bridge the research gap in existing protocols and contribute to the advancement of energy-efficient WSNs by prioritizing energy efficiency and network optimization.

Proposed Work

In this project, the problem of high energy consumption in wireless sensor networks (WSNs) is addressed by proposing a new energy efficient model to enhance network lifespan. The existing literature highlighted the limitations of current protocols, such as LEACH, in selecting cluster heads (CHs) and grid heads (GHs) effectively, leading to decreased network performance. To improve the overall efficiency, the Dragonfly Optimization Algorithm (DA) is utilized for CH selection, considering factors like residual energy, distance to GH, and delay. By optimizing the CH selection process using DA, the network lifespan can be prolonged as nodes with higher energy levels and lower distance to GH are selected as CHs, improving the network's energy consumption and performance. Moreover, to reduce the workload on CHs and further enhance network efficiency, GHs are selected using the Fuzzy C-means (FCM) algorithm.

The proposed approach takes into account various parameters, including residual energy of CHs and position of the base station, to determine the most suitable node to become GH in each grid. By incorporating these quality of service parameters for both CH and GH selection, the proposed model aims to significantly increase the lifespan of WSNs and improve overall network performance. By leveraging DA and FCM algorithms, the project's approach prioritizes energy efficiency and network optimization, ultimately aiming to address the research gap in existing protocols and contribute to the advancement of energy-efficient WSNs.

Application Area for Industry

This project's proposed solutions can be applied in various industrial sectors where Wireless Sensor Networks (WSNs) are utilized, such as agriculture, environmental monitoring, healthcare, smart grids, and manufacturing. These sectors face challenges related to energy consumption and network lifespan, which can be addressed by implementing the new energy-efficient model proposed in this research. By considering important quality of service (QoS) parameters such as residual energy of nodes, distances, and delay while selecting Cluster Heads (CHs) and Grid Heads (GHs) in the network, the overall performance and efficiency of the WSNs can be greatly improved. The benefits of implementing these solutions include increased network lifespan, reduced energy consumption in CHs and GHs, optimized performance with the Dragonfly algorithm, and improved fitness function by selecting nodes with high energy and low distance to the base station or GH. By using the Fuzzy C-means (FCM) technique to choose GHs and effectively distributing the workload between CHs and GHs, industries can enhance the reliability and longevity of their WSNs.

Overall, the proposed approach offers a more efficient and effective way to manage energy consumption and prolong the lifespan of Wireless Sensor Networks in various industrial applications.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training by introducing a novel and improved energy efficient model for wireless sensor networks (WSNs). This project can provide valuable insights and advancements in the field of WSNs by addressing the limitations of existing protocols and enhancing the network lifespan. Researchers, MTech students, and PhD scholars in the field of wireless communication, networking, and Internet of Things (IoT) can benefit from the codes and literature of this project. By studying the proposed model and algorithms used (FCM and DA), they can explore innovative research methods, simulations, and data analysis techniques within educational settings. This project can open up opportunities for conducting further studies on optimizing energy consumption in WSNs, improving network performance, and extending network longevity.

The relevance of this project lies in its potential applications for enhancing the quality of service (QoS) parameters such as residual energy, distance, and delay in selecting cluster heads (CHs) and grid heads (GHs) in WSNs. By incorporating the Dragonfly Optimization Algorithm (DA) for CH selection and Fuzzy C-means (FCM) technique for GH selection, this project offers a comprehensive approach to reducing energy consumption, minimizing workload on CHs, and increasing network efficiency. In conclusion, the proposed project can contribute significantly to academic research, education, and training in the domain of wireless sensor networks. By implementing a new energy efficient model and utilizing advanced algorithms, this project has the potential to drive innovation, foster collaboration among researchers, and support the development of next-generation wireless communication technologies. The future scope of this project includes further optimization of algorithms, real-world implementation, and integration with other IoT applications for more comprehensive solutions in the field of wireless networks.

Algorithms Used

The project utilizes the Dragonfly Optimization Algorithm (DA) and Fuzzy C-means (FCM) technique to improve energy efficiency in wireless sensor networks (WSNs). DA is employed to select Cluster Heads (CHs) based on parameters such as residual energy, distance between nodes, distance to Grid Heads (GHs), and delay. By optimizing the CH selection process with DA, the network lifespan is prolonged by choosing efficient CHs. Additionally, FCM is used to select GHs by considering parameters like residual energy of CHs, position of base station, and relative distance of CHs. By enhancing the QoS parameters for determining CH and GH in the network, the overall energy consumption is minimized and WSN lifetime is increased.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, WSN, Cluster head selection, Network lifetime enhancement, Energy efficiency, Data aggregation, Data routing, Clustering algorithms, Cluster formation, Network topology, Node selection, Energy conservation, Self-organization, Wireless communication, Sensor nodes, Energy-aware protocols, Network performance, Optimization algorithms, Artificial intelligence, Dragonfly algorithm, Fuzzy C-means, LEACH, QoS parameters, Residual energy, Distance, Delay, Grid Head, Energy consumption, Network lifespan, CH selection, GH selection, Fitness function, Energy consumption reduction, Literature survey, Energy-efficient protocols, Wireless networks, Communication module, Lifespan, Conventional approaches, Research, Limitations, Performance degradation, Quality factors, Arbitrary threshold energy method, Protocol enhancement, Effective results.

SEO Tags

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