Multi-QoS Based Clustering Optimization Using Grey Wolf Optimization for Enhanced Lifespan.

Problem Definition

Various clustering protocols in wireless sensor networks face several challenges which hinder their efficiency. These issues include limited energy resources in sensor nodes, leading to higher energy consumption in the network overall and reducing the network lifetime. Additionally, the small physical size and limited energy storage of sensor nodes restrict their data processing and transmission capabilities. Furthermore, the design of clustering strategies must consider application robustness for an efficient clustering algorithm. Previous research has shown that while clustering and optimization protocols have been used together, the optimization of cluster head selection processes may not have covered all critical features and parameters such as energy and distance.

This highlights the need for a more comprehensive approach to address these limitations and improve the performance of clustering protocols in wireless sensor networks.

Objective

The objective of this study is to improve the performance of clustering protocols in Wireless Sensor Networks (WSN) by developing a novel energy-efficient cluster head selection approach using Grey Wolf Optimization (GWO) technique. The aim is to address the limitations of existing protocols such as limited energy resources, network lifetime, and data processing capabilities of sensor nodes. By optimizing the cluster head selection process considering both energy and distance as key factors, the proposed approach seeks to enhance energy efficiency and overall network performance, ultimately improving the efficiency of clustering protocols in WSNs.

Proposed Work

This study aims to address the limitations of existing clustering protocols in Wireless Sensor Networks (WSN) such as limited energy, network lifetime, limited abilities of sensor nodes, and application dependency. The proposed work involves developing a novel energy-efficient cluster head selection approach using Grey Wolf Optimization (GWO) technique. While reviewing previous researches, it was observed that existing clustering protocols did not cover all major features and parameters for optimizing cluster head selection, such as energy and distance. Therefore, the proposed approach will focus on optimizing cluster head selection process by considering both energy and distance as key factors. In the traditional work, a hybrid clustering mechanism was implemented using clustering, tree-based data aggregation approach, and hybrid optimization techniques like ant colony optimization (ACO) and particle swarm optimization (PSO).

However, this approach faced challenges such as a weak cluster head selection strategy and increased data transmission delay due to a large number of iterations required for processing ACO and PSO. Hence, the proposed solution involves leveraging GWO optimization technique to optimize the cluster head selection process based on the energy and distance of the nodes. By integrating GWO into the clustering protocol, it is expected to enhance energy efficiency and overall network performance, addressing the identified limitations of the traditional approach.

Application Area for Industry

This project can be applied in various industrial sectors such as smart manufacturing, smart agriculture, smart healthcare, and smart city applications. In smart manufacturing, the proposed solutions can help in optimizing energy consumption within the network of sensors, thereby increasing the efficiency of production processes. In smart agriculture, the project can assist in improving the monitoring and management of crops by enhancing data processing and transmission capabilities of sensor nodes. In smart healthcare, the solutions can aid in the development of more reliable and robust clustering algorithms for patient monitoring systems. In smart city applications, implementing the proposed solutions can lead to more energy-efficient and sustainable urban infrastructure management.

The challenges that industries face, such as limited energy, network lifetime, limited node capabilities, and application dependency, can be effectively addressed by the proposed solutions in this project. Implementing these solutions can result in extended network lifetime, improved data processing and transmission capabilities, optimized energy consumption, and enhanced application robustness in a variety of industrial domains. Overall, the benefits of incorporating these solutions include increased operational efficiency, reduced maintenance costs, improved data accuracy, and enhanced overall performance in various industrial sectors.

Application Area for Academics

The proposed project can enrich academic research, education, and training by providing a deeper understanding of energy-efficient clustering protocols in Wireless Sensor Networks (WSNs). By addressing issues such as limited energy, network lifetime, limited abilities, and application dependency in clustering strategies, researchers can gain insights into optimizing cluster head selection processes. The use of Grey Wolf Optimization (GWO) algorithm in the project offers a new perspective on optimizing CH selection in WSNs, considering both energy and distance as major factors. This can open up avenues for innovative research methods, simulations, and data analysis within educational settings. Researchers, MTech students, and PhD scholars in the field of WSNs can utilize the code and literature of this project for their work, exploring new possibilities in energy-efficient protocols and optimization techniques.

The relevance of this project lies in its potential applications in real-world scenarios where WSNs are deployed for various purposes, such as environmental monitoring, smart cities, healthcare, and more. By addressing the challenges faced by existing clustering protocols, the project can contribute significantly to advancements in WSN technology and research. In the future, the scope of the project could include further optimizations of the clustering protocol by incorporating machine learning algorithms or implementing advanced data fusion techniques. This would not only enhance the efficiency of WSNs but also drive forward the development of innovative solutions for various applications in the Internet of Things (IoT) domain.

Algorithms Used

In traditional work, a hybrid clustering mechanism was developed that operated by utilizing the clustering, tree-based data aggregation approach, and hybrid optimization (ant colony optimization and particle swarm optimization). However, issues such as a weak CH selection strategy and delays in data transmission due to a large number of iterations required for processing were observed. To address these issues, a proposal was made to use Grey Wolf Optimization (GWO) protocol to optimize the CH selection process. The energy of the nodes and distance of the nodes are considered as major factors in this approach.

Keywords

SEO-optimized keywords: clustering protocols, energy efficient, sensor node, network lifetime, data processing, data transmission, application robustness, WSN, hybrid clustering mechanism, data aggregation, ant colony optimization, particle swarm optimization, CH selection strategy, Grey Wolf Optimization, wireless network, performance optimization, algorithm enhancement, network optimization, optimization techniques, network parameters, network performance evaluation, resource allocation, network throughput, network latency, optimization algorithms, wireless network management.

SEO Tags

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