A Clustering and Neural Network Approach for Energy-Efficient Communication in WSNs

Problem Definition

Based on the research conducted in the field of Wireless Sensor Networks (WSNs), it is evident that there are significant limitations and problems in the existing routing techniques utilized between sensor nodes and the base station (BS). Traditional models have predominantly relied on neural network-based techniques for routing path optimization, with clustering performed post cluster head (CHs) selection. However, these conventional methods are lacking in terms of efficiency and effectiveness, leading to unnecessary complexity and delays in selecting communication channels. Moreover, the current approach to CH selection is inadequate, resulting in a decrease in the overall lifespan of the WSN. It is clear from the literature that there is an urgent need for a novel algorithm that can address these challenges and improve the network's longevity and stability.

By enhancing the mechanism of CH selection and optimizing routing decisions, a more efficient and robust WSN system can be achieved, ultimately improving the overall performance and reliability of the network.

Objective

The objective is to develop a novel algorithm that improves cluster head (CH) selection in Wireless Sensor Networks (WSNs) based on energy efficiency, thereby extending the lifespan of WSNs. By incorporating a neural network into the system to optimize routing paths from CHs to the base station, the aim is to reduce complexity, minimize energy consumption, and enhance network stability. The goal is to achieve efficient data transmission and improve overall performance and reliability of WSNs by addressing the limitations of traditional routing techniques. Through enhanced CH selection mechanisms and neural network-based routing optimizations, the objective is to contribute to advancing WSN technology and filling research gaps in the field.

Proposed Work

To address the research gap identified in the literature survey regarding the optimization of routing paths in WSNs, the proposed work focuses on developing a novel algorithm to improve CH selection and minimize energy consumption. By enhancing the mechanism of CH selection in the network based on the energy efficiency of nodes, the proposed approach aims to extend the lifespan of WSNs. Incorporating a neural network into the system to streamline the decision-making process for routing paths from CHs to the base station will further reduce complexity and optimize network stability. By leveraging technology and algorithms to optimize routing decisions, the proposed work strives to achieve efficient data transmission with minimal energy usage, ultimately enhancing the overall performance of WSNs. The adoption of an ANN-based CH selection technique and the implementation of a more streamlined routing approach in the proposed work are driven by the need to address the limitations of traditional models in WSNs.

By focusing on improving the efficiency of routing paths and minimizing energy consumption, the proposed algorithm aims to overcome the challenges faced by existing techniques. The rationale behind choosing specific algorithms and technology lies in the goal of enhancing network longevity and stability by simplifying decision-making processes and improving the overall performance of WSNs. Through a strategic combination of enhanced CH selection mechanisms and neural network-based routing optimizations, the proposed work seeks to contribute to the advancement of WSN technology and address key research gaps in the field.

Application Area for Industry

This project can be applied in various industrial sectors such as telecommunications, agriculture, smart cities, healthcare, and environmental monitoring. In the telecommunications sector, the proposed solutions can help in optimizing routing paths in wireless sensor networks, leading to improved data transfer efficiency and reduced energy consumption. In agriculture, the project can assist in monitoring soil conditions, crop health, and irrigation systems by enhancing the selection of cluster heads and improving overall network stability. Smart cities can benefit from the implementation of these solutions by enabling better communication between sensors and base stations for efficient management of resources and services. In the healthcare sector, the project can aid in remote patient monitoring and tracking medical equipment through a reliable and energy-efficient network.

Moreover, environmental monitoring can be enhanced through the optimized routing paths, leading to real-time data collection and analysis for better decision-making in areas such as air quality control and waste management. Overall, the proposed solutions can address specific challenges faced by industries in improving network efficiency, reducing energy consumption, and optimizing data transfer, ultimately resulting in increased productivity and effectiveness within various industrial domains.

Application Area for Academics

The proposed project of optimizing routing in Wireless Sensor Networks (WSNs) using an Artificial Neural Network (ANN) has the potential to enrich academic research in the field of networking and data transmission. The project addresses the limitations of traditional techniques by introducing an efficient CH selection mechanism and utilizing neural networks for routing decisions, leading to improved network longevity and stability. In terms of relevance, this project can contribute to innovative research methods by integrating machine learning algorithms like ANN into WSNs for enhanced data transmission. Researchers, MTech students, and PHD scholars in the field of wireless communication, networking, and machine learning can utilize the code and literature from this project to explore new approaches in improving WSN performance and energy efficiency. The proposed project can be applied in educational settings to train students in data analysis, simulation techniques, and developing algorithms for optimizing network performance.

It can serve as a practical example for students to understand the application of machine learning in solving real-world problems in wireless communication. Future scope of this project includes exploring other machine learning algorithms for routing optimization in WSNs, conducting performance evaluations in different network scenarios, and integrating advanced technologies like IoT for enhanced data transmission. This project sets the foundation for further research in the field of WSNs and machine learning, contributing to the advancement of wireless communication technologies.

Algorithms Used

The proposed work in this project aims to address issues in traditional routing models for Wireless Sensor Networks (WSNs) by introducing an optimal technique utilizing an Artificial Neural Network (ANN). This technique focuses on solving routing problems in WSNs by efficiently determining paths from Cluster Heads (CH) to the Base Station (BS) with minimal energy consumption for data transmission from sensor nodes. In the suggested method, two key enhancements are implemented. Firstly, the method improves the CH selection process by assessing the energy efficiency of nodes within clusters and selecting the most efficient nodes as CHs in the network. This optimization helps in balancing energy consumption across the network and improving overall performance.

Secondly, a neural network component is integrated into the system to streamline decision-making processes. The neural network specifically focuses on determining the optimal route only from CHs to the sink, simplifying the routing decision process and reducing computational complexity. By leveraging the neural network to identify the best paths between CHs and the BS, the overall efficiency of the routing algorithm is improved, leading to more effective data transmission within the WSN. Overall, the inclusion of the ANN in the proposed routing algorithm enhances accuracy and efficiency in path selection, contributing to the project's objective of optimizing routing in WSNs and reducing energy consumption for data transmission.

Keywords

sensor networks, route determination, neural networks, intelligent routing, network performance, data routing, network optimization, distributed systems, machine learning, deep learning, pattern recognition, resource allocation, WSNs, CH selection, energy efficiency, QoS parameters, communication channels, routing decisions, network longevity, network stability, optimal technique, minimal energy usage, data transfer, CH to BS path, cluster nodes, sink nodes, decision capability, cluster head, network complexity, optimal path, neural network-based techniques, routing problem, cluster selection, energy efficiency evaluation.

SEO Tags

sensor networks, route determination, neural networks, intelligent routing, network performance, data routing, network optimization, distributed systems, machine learning, deep learning, pattern recognition, resource allocation, WSN, CH selection, QoS parameters, energy efficiency, clustering, communication channels, routing decisions, optimal path, sink nodes, cluster nodes, network longevity, network stability, research proposal, PHD research, MTech project, research scholar, literature survey, academic research, algorithm development, innovative techniques, WSN improvement, research methodology, problem-solving, algorithm optimization.