An Energy-Efficient Sensor Clustering Approach for Improved Network Lifetime in WSNs

Problem Definition

The existing literature on wireless sensor networks (WSNs) has highlighted the limitations of traditional models that heavily rely on clustering-based communication protocols and fuzzy decision models. While these models have shown some success in optimizing energy consumption and routing data to the sink node, there are significant drawbacks that need to be addressed. One key issue is the limited input constraints of traditional fuzzy decision models, which can impact the overall performance of the network. Additionally, these models require human-generated rules that may not always be comprehensive and could lead to skipped factors during processing. As the dependency factors increase, the complexity of the fuzzy-based decision models also grows, causing potential delays in processing.

To overcome these challenges and improve the efficiency of WSNs, a more dynamic approach that avoids fixed fuzzy-based decision models is necessary. By incorporating effective clustering algorithms and innovative techniques, such as dynamic decision-making processes, the performance of WSNs can be greatly enhanced.

Objective

The objective of this study is to enhance the efficiency of wireless sensor networks by proposing a novel technique that combines k-mean clustering and WOA optimization algorithms for CHs selection and cluster formation. By addressing the limitations of traditional models through dynamic decision-making processes and effective clustering algorithms, the aim is to improve communication and optimize energy consumption in WSNs. The proposed models include different phases based on the location of the sink node and aim to create clusters based on network density to enhance communication. Additionally, an energy consumption model is employed to track the transmission of packets through the network.

Proposed Work

To overcome the limitations of the traditional models during CHs selection and cluster formation, a novel technique based on k-mean clustering and WOA optimization algorithm is proposed in this paper. The iterative technique K-means divides an unorganized dataset into k clusters, with each sample belonging to just one group with identical properties, whereas WOA is an optimization approach based on swarms that finds the search agent and gives the most accurate evaluation of a particular on optimization issues. The suggested models contained two phases’ one when the sink node is located at (100, 100), and the other when the sink node is located at (100,250). The major goal of employing the K-means clustering technique is to create clusters based on network density to enhance the communication in the subsequent phases of the WSNs. Moreover, in the suggested system for communication, an energy consumption model is used in which l-packets are transmitted through a distance "d" respectively.

Application Area for Industry

This project can be applied in various industrial sectors such as agriculture, environmental monitoring, smart cities, and industrial automation. In agriculture, the proposed solutions can help in monitoring soil conditions, crop growth, and irrigation systems through WSNs, leading to efficient resource utilization and increased crop yield. In environmental monitoring, the project can aid in tracking air and water quality, weather patterns, and wildlife conservation efforts. For smart cities, the solutions can be used for traffic management, waste management, and energy monitoring to enhance overall city operations. In industrial automation, the project can improve efficiency and productivity by monitoring machine health, optimizing process control, and ensuring worker safety.

The challenges faced by industries, such as limited energy constraints in sensor nodes, inefficient communication protocols, and complex decision-making models, can be addressed by implementing the proposed solutions. By utilizing k-means clustering and WOA optimization algorithm, the project aims to enhance communication, minimize energy consumption, and create dynamic cluster formations based on network density. This will lead to improved performance, increased accuracy in data transmission, and reduced processing delays in various industrial domains. Overall, the benefits of implementing these solutions include enhanced system efficiency, better resource management, and optimized decision-making processes for industries across different sectors.

Application Area for Academics

The proposed project can significantly enrich academic research, education, and training in the field of Wireless Sensor Networks (WSNs) and optimization algorithms. By combining K-means clustering and WOA optimization algorithms, the project offers a novel and innovative approach to improving the performance of WSNs in terms of energy efficiency, data routing, and network communication. Academically, this project holds relevance in the domain of WSNs and optimization techniques, providing researchers with a new methodology to address the limitations of traditional models. Educators can integrate this project into their curriculum to teach students about advanced techniques in network optimization and data analysis in WSNs. Moreover, MTech students and PhD scholars can utilize the code and literature of this project for their research work in the field of WSNs, exploring new avenues for addressing energy constraints and enhancing communication efficiency.

The use of K-means clustering and WOA optimization algorithms can open up new possibilities for innovative research methods, simulations, and data analysis within educational settings. The potential applications of this project extend to various research domains, particularly in the areas of wireless communication, optimization algorithms, and sensor networks. By leveraging the proposed techniques, researchers can conduct experiments, simulations, and data analysis to test the efficiency and performance of the proposed model. In conclusion, the proposed project has the potential to advance academic research, education, and training by offering a novel approach to optimizing WSNs using K-means clustering and WOA algorithms. Researchers, students, and scholars in the field of WSNs can benefit from the innovative methodologies and applications of this project, paving the way for future advancements in the field.

Algorithms Used

The proposed work in this project involves using two key algorithms - K-means clustering and Whale Optimization Algorithm (WOA) to address the challenges in CH selection and cluster formation in Wireless Sensor Networks (WSNs). K-means clustering is utilized to organize the dataset into k clusters, ensuring that each sample belongs to a specific group with similar characteristics. This helps in enhancing communication by creating clusters based on network density. The main objective here is to improve communication efficiency in subsequent phases of the WSNs. On the other hand, WOA is employed as an optimization approach based on swarms to find the search agent and provide more accurate evaluations on optimization issues.

By using WOA, the project aims to optimize the energy consumption model for transmitting data packets through different distances in the WSNs. Overall, the combination of K-means clustering and WOA in this project plays a crucial role in improving the accuracy, efficiency, and performance of CH selection, cluster formation, and communication in WSNs.

Keywords

SEO-optimized keywords: wireless sensor networks, energy efficiency, network longevity, advanced clustering algorithm, data aggregation, routing protocols, network optimization, distributed systems, network performance, resource allocation, quality of service, energy conservation, sensor node coordination, network lifetime, power management, energy-aware protocols, k-mean clustering, WOA optimization algorithm, communication model, fuzzy decision models, clustering-based communication protocols, unequal multi hopping, fuzzy based decision models, optimization approach, swarms, sink node, network density, energy consumption model.

SEO Tags

wireless sensor networks, energy efficiency, network longevity, advanced clustering algorithm, data aggregation, routing protocols, network optimization, distributed systems, network performance, resource allocation, quality of service, energy conservation, sensor node coordination, network lifetime, power management, energy-aware protocols, k-mean clustering, WOA optimization algorithm, clustering-based communication protocols, fuzzy decision models, multi hopping method, fuzzy-based decision models, swarm optimization, energy consumption model, PHD research topic, MTech project, research scholar, literature survey, optimization techniques, sensor node energy constraints, dynamic technique, wireless communication, network density, communication model, sink node location, search agent evaluation, performance enhancement, system complexity, iterative technique.