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Energy Optimization of Routing Protocol in Wireless Sensor Network
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Author(s): Vishwajit K. Barbudhe (SITRC, Nashik, India), Shraddha N. Zanjat (School of Engineering and Technology, Sandip University, Nashik, India)and Bhavana S. Karmore (G.H. Raisoni University, Amaravati, India)
Copyright: 2025
Pages: 14
Source title:
Machine Learning for Environmental Monitoring in Wireless Sensor Networks
Source Author(s)/Editor(s): Parikshit N. Mahalle (Department of Artificial Intelligence and Data Science, Vishwakarma Institute of Technology, Pune, India), Dattatray G. Takale (Vishwakarma Institute of Information Technology, India), Sachin Sakhare (Vishwakarma Institute of Information Technology, India)and Ganesh B. Regulwar (Vardhaman College of Engineering, India)
DOI: 10.4018/979-8-3693-3940-4.ch014
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Abstract
In many modern situations, wireless sensor networks play a crucial role, used for monitoring things like environmental conditions, transactions, and various statuses. These networks gather a large amount of data, sending it to a central hub for analysis. However, a significant problem is that traditional wireless sensor networks heavily rely on energy, and this limits how long they can operate. This chapter develops an optimization approach for a variable clustering routing protocol to address the issue. The objective is to enhance the cluster structure in wireless sensor networks while reducing energy loss in cluster heads. The first step involves employing a dynamic estimation method for clustering to determine cluster heads, utilizing core concentration to establish the head within the cluster radius. The authors also introduce a fuzzy logic algorithm to handle uncertainties in selecting cluster heads. The residual energy of the cluster's head nodes, maintaining a balanced distribution of cluster heads, and optimizing node use of energy are all taken into account by this fuzzy logic approach. An ant colony algorithm-based technique for optimizing inter-cluster transport is presented in this outline. The primary objective is to optimize energy consumption while simultaneously reducing the data communication overhead among cluster heads. Chaotic mapping is utilized by this technique to update and perturb pheromones, ensuring optimal performance. Energy utilization among cluster heads is optimized by selecting the optimal path based on considerations of energy dispersion parameters and distance coefficients. Our experiments show that compared to traditional algorithms, Researchers provide a non-uniform clustering approach for route optimization that dramatically extends the network lifespan by 75% and lowers overall energy consumption by about 20%. This effectively optimizes network energy utilization and significantly extends the network lifetime, demonstrating the practical effectiveness of our method.
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