Heterogeneous Optimization Approach for Energy-Efficient Wireless Sensor Networks

Problem Definition

The wireless sensor network domain faces a significant challenge in the form of reduced network lifespan. Despite various techniques proposed by researchers to improve the network's longevity, many of these methods have proven to be complex and prone to getting stuck in local optima. Additionally, the selection of cluster heads in traditional models has been identified as a difficult task requiring frequent updates. The use of homogeneous sensor nodes, where all nodes have the same residual energy, has contributed to rapid battery drainage and further decreased the network's lifespan. Although some researchers have explored the use of heterogeneous nodes to address this issue, the requirement for additional energy sources to provide different energy levels to nodes has made the traditional system inefficient and cumbersome.

These limitations and problems highlight the critical need for a new and effective approach to simplify the network management process and enhance overall performance.

Objective

The objective of this project is to address the challenge of reduced network lifespan in wireless sensor networks by proposing a novel CH selection method based on a hybrid of the WOA and PSO optimization algorithms. By combining these two algorithms, the aim is to improve the network's performance by extracting the best results from each algorithm and avoiding local optima. The proposed HWOAPSO algorithm intends to simplify the network management process, enhance overall performance, and increase the network's lifespan by selecting the most appropriate cluster heads with higher residual energy.

Proposed Work

In this project, we propose a novel CH (Cluster Head) selection method based on a low complexity fitness hybrid of the WOA-PSO. Clustering and selection of CH plays a vital role in WSNs, hence selecting the most appropriate algorithm for clustering is crucial. By combining the WOA and PSO optimization algorithms into a hybrid WOA-PSO approach, we aim to extract the best quality results from both algorithms, thereby enhancing the performance of the system. The hybrid method aims to leverage the exploration capabilities of WOA to direct particles towards their ideal solutions, while utilizing PSO to extract optimal solutions from an unknown search space. This approach not only decreases computational time but also eliminates the problem of stagnation in local optima.

Additionally, the CH selection in our proposed model is based on evaluating the fitness function of all sensor nodes, with the node exhibiting the best fitness value being chosen as the CH with higher residual energy. Overall, the proposed HWOAPSO algorithm intends to significantly increase the lifespan of the wireless network by reducing computational time and selecting the best optimal solution in the network.

Application Area for Industry

This project can be beneficial for various industrial sectors such as healthcare, environmental monitoring, agriculture, manufacturing, and smart cities. In healthcare, the extended network lifespan can ensure continuous monitoring of patients and medical equipment, improving overall efficiency and patient care. In environmental monitoring, the longevity of wireless sensor networks can help in the collection of accurate data for analyzing environmental trends and making informed decisions. In agriculture, the extended network lifespan can assist in monitoring soil quality, weather conditions, and crop health, leading to increased agricultural productivity. For manufacturing industries, the longer network lifespan can optimize production processes, reduce downtime, and enhance overall operational efficiency.

In smart cities, the prolonged lifespan of wireless sensor networks can aid in improving infrastructure management, traffic flow, energy consumption, and public safety. Overall, the proposed solutions in this project can address the challenges industries face regarding network lifespan, complexity, and efficiency, while providing benefits such as improved performance, increased lifespan, and enhanced decision-making capabilities.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of wireless sensor networks. By addressing the issue of reduced network lifespan and offering a more efficient and effective method for clustering and selecting cluster heads, the project can contribute to innovative research methods and simulations within educational settings. Researchers, MTech students, and PhD scholars in the field can utilize the HWOAPSO algorithm to improve their work on wireless sensor networks. By combining the features of WOA and PSO algorithms, the project offers a new approach to optimizing network performance and increasing network lifespan. The novel method of selecting cluster heads based on fitness evaluation provides a more streamlined and effective process for managing sensor nodes.

The code and literature developed from this project can serve as a valuable resource for researchers and students looking to explore optimization algorithms in wireless sensor networks. By studying and implementing the HWOAPSO algorithm, individuals can enhance their understanding of network optimization and develop innovative solutions for improving network performance. The relevance of the proposed project lies in its potential applications in advancing research methods, simulations, and data analysis within the field of wireless sensor networks. By addressing the limitations of traditional models and offering a more efficient and effective approach to network optimization, the project can contribute to the development of cutting-edge technologies and methodologies in the field. Reference future scope: The future scope of the project could involve further optimizing the hybrid WOA-PSO algorithm and exploring its applicability in other domains beyond wireless sensor networks.

Additionally, conducting experiments to evaluate the performance of the proposed method in real-world scenarios could provide valuable insights and validate its effectiveness in practical applications.

Algorithms Used

The proposed method in this project combines two optimization algorithms, WOA and PSO, to improve the clustering and selection of Cluster Heads (CH) in Wireless Sensor Networks (WSNs). The hybrid WOA-PSO algorithm aims to reduce complexity and enhance system performance by leveraging the strengths of both PSO and WOA. WOA directs particles towards their ideal solution, decreasing computational time, while PSO extracts the optimum solution from an unknown search space. By combining these algorithms, the proposed method aims to achieve the desired solution and eliminate the problem of stagnation in local optima. Furthermore, the selection of CH is based on evaluating the fitness function of sensor nodes, with the node having the best fitness value and higher residual energy being selected as the CH.

Overall, the HWOAPSO algorithm is expected to increase the wireless network's lifespan by reducing computational time and selecting the best optimal solution.

Keywords

SEO-optimized keywords: Wireless Sensor Networks, Cluster Head Selection, WOA-PSO Algorithm, Whale Optimization Algorithm, Particle Swarm Optimization, Fitness Hybrid, Energy Efficiency, Multilevel Heterogeneous Routing Protocol, Residual Energy, Initial Energy, Sink Distance, Routing Efficiency, Energy Consumption, Network Performance, Wireless Communication, Sensor Nodes, Network Optimization, Cluster Head Selection Algorithms, Optimization Techniques, Wireless Communication Systems, Sensor Network Management, Routing Algorithms, Routing Efficiency.

SEO Tags

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