Energy-Efficient Clustering and Routing Optimization in Wireless Sensor Networks Using STSA Algorithm with Fuzzy Logic.

Problem Definition

The literature survey highlights several key limitations, problems, and pain points existing within the domain of Wireless Sensor Networks (WSNs). One major challenge is the limited energy supply of small battery-powered devices used in WSNs, which hinders widespread implementation due to the inability to recharge or replace these devices. To address this issue and prolong network lifespan, reducing energy consumption of nodes is crucial. Clustering has been identified as an effective strategy for enhancing network lifespan by grouping nodes together based on certain attributes. However, existing clustering approaches have not yielded desired results, largely due to the complex nature of clustering as a multi-objective optimization problem that requires optimal optimization algorithms.

Previous research has also overlooked the location of base or sink nodes, leading to hot spot problems in multi-hop systems. Additionally, the plethora of optimization algorithms available complicates the decision-making process for selecting the most suitable algorithm for clustering. Moreover, current optimization algorithms used in clustering approaches suffer from limitations such as getting trapped in local minima and slow convergence rates, further impacting system performance. These findings underscore the critical need for developing a new clustering approach to address the identified challenges and improve the overall effectiveness of WSNs.

Objective

The objective of this research is to develop a new clustering approach using the Sine-Tree Seed Algorithm (STSA) to address the energy consumption issues in Wireless Sensor Networks (WSNs). By effectively clustering nodes and selecting cluster heads (CHs) using the STSA optimization algorithm, the goal is to reduce the distance between nodes, minimize energy consumption, and ultimately enhance the lifespan of the WSN network. The proposed model involves grid formation, GH selection, clustering, and communication phases, with a focus on improving the overall performance of WSNs through efficient clustering techniques. The STSA algorithm is chosen for its ability to effectively solve continuous optimization problems, achieve high convergence rates, and improve the exploration and exploitation phases in search for optimal solutions.

Proposed Work

In this research, a new advanced clustering and routing approach is proposed in order to address the issued faced in conventional clustering approaches. The main objective of this work is to decrease the energy consumption by nodes which in turn will enhance the lifespan of the entire WSN network. The proposed algorithm is based on the advanced variant of Tree Seed Algorithm (TSA), named as, Sine-Tree Seed Algorithm (STSA), which is basically a hybridized model including TSA and Sine-Cosine Algorithm (SCA). The proposed model works in three phases, Grid formation and GH selection, clustering and CH selection and finally communication phase. However, clustering is the main focus of this research as effective clustering reflects enhanced performance of wireless sensor networks.

The distance between source node and destination node is reduced by forming clusters effectively using STSA optimization algorithm along with suitable GH and CH selection. By doing so the nodes need not to travel longer distances which reduces their energy consumption and automatically enhances the network lifespan. In the proposed network, the grid is formed by using the Fuzzy C means (FCM) techniques and GH are selected by using the mathematical model of FCM. After this, clusters are formed in the network by using the STSA technique and CHs are selected by using the fuzzy based approach. The main reason for choosing the STSA algorithm for clustering purpose in the proposed work is that it solves the continuous optimization problems effectively and has high convergence rate than other optimization algorithms.

Moreover, the exploration and exploitation phase for finding the optimal solution in STSA is improved by the incorporation of the SCA. The tree-seed algorithm is built on the tree, seed, and maintaining an inverse association between exploration and exploitation all through searching.

Application Area for Industry

This project can be applied in various industrial sectors such as healthcare, surveillance and defense, smart home automation systems, and any other sectors that rely on wireless sensor networks (WSNs) for data collection and communication. The proposed advanced clustering and routing approach aims to reduce the energy consumption of nodes in WSNs, consequently enhancing the overall network lifespan. By effectively clustering nodes using the Sine-Tree Seed Algorithm (STSA), the distance between source and destination nodes is minimized, leading to lower energy consumption and improved network performance. The benefits of implementing this solution in different industrial domains include increased network efficiency, prolonged network lifespan, and optimized energy consumption. This project addresses specific challenges faced by industries using WSNs, such as the need for effective clustering approaches, optimized node communication, and the selection of Cluster Heads (CHs).

By utilizing the STSA optimization algorithm along with Fuzzy C means (FCM) techniques for grid formation and GH selection, this project offers a comprehensive solution to improve the performance of WSNs across various industrial sectors.

Application Area for Academics

The proposed research project on advanced clustering and routing in Wireless Sensor Networks (WSNs) has the potential to enrich academic research, education, and training in the field of network optimization and energy efficiency. By addressing the energy consumption issues faced by conventional clustering approaches, the project aims to enhance the lifespan of WSNs and improve network performance. Researchers in the field of WSNs, MTech students, and PHD scholars can leverage the code and literature of this project for their work by exploring the advanced variant of Tree Seed Algorithm (TSA) known as Sine-Tree Seed Algorithm (STSA). The STSA algorithm, which incorporates elements from TSA and Sine-Cosine Algorithm (SCA), offers a more effective approach to clustering in WSNs by reducing the distance between nodes and optimizing energy consumption. The inclusion of Fuzzy C means (FCM) techniques for grid formation and GH selection, along with the mathematical model of FCM for CH selection, adds a layer of sophistication to the proposed clustering approach.

By utilizing the STSA optimization algorithm for clustering and CH selection, researchers can achieve improved network performance and energy efficiency in WSNs. The project's focus on solving multi-objective optimization problems in clustering, exploring the effectiveness of different optimization algorithms, and addressing hot spot issues in multi hop systems provides a rich source of research material for academics and students in the field of network optimization. The proposed work opens up avenues for exploring innovative research methods, simulations, and data analysis within educational settings, ultimately contributing to the advancement of knowledge and technology in the domain of WSNs. In future research, the project could be extended to explore the application of STSA algorithm in other domains beyond WSNs, further expanding its potential impact on academic research and technological innovation.

Algorithms Used

STSA is a hybridized algorithm that combines Tree Seed Algorithm and Sine-Cosine Algorithm. It is used in this research for grid formation, GH selection, clustering, and CH selection, aiming to reduce energy consumption and extend the lifespan of WSN networks. Fuzzy C-Means technique is employed for grid formation and GH selection, while STSA is used for clustering and CH selection. The high convergence rate and effectiveness in solving continuous optimization problems make STSA a suitable choice for clustering in this project. The FCM model is also utilized for selecting GHs.

Keywords

sensor networks, communication optimization, CH selection, data filtering, Fuzzy S-Tree, optimization algorithms, seed optimization, data aggregation, distributed systems, network performance, resource allocation, quality of service, energy efficiency, sensor node coordination, network optimization, WSN, energy consumption, clustering, Tree Seed Algorithm, Sine-Tree Seed Algorithm, hybridized model, Sine-Cosine Algorithm, GH selection, grid formation, Fuzzy C means, clustering optimization, exploration and exploitation, wireless sensor networks, network lifespan, base node location, multi hop systems, optimization algorithm, local minima, convergence rate, hot spot problems.

SEO Tags

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