Energy-Efficient Data Aggregation in Wireless Sensor Networks using Neural Network-Based Prediction Models

Abstract

In this study, we examine how neural networkbased prediction models can be used in Wireless Sensor Networks (WSNs) to build energy-efficient data aggregation strategies. With their widespread use in a variety of applications like smart cities, health care, and environmental monitoring, WSNs are known for their energy-related problems. This study's major goal is to prolong sensor network lifespans while preserving data communication accuracy and dependability. We provide a unique framework that combines data aggregation techniques with predictive modelling, making use of neural networks to predict sensor data and minimise redundant transmissions. Our approach minimises energy consumption related to data transmission, resulting in more sustainable operation of WSNs by accurately forecasting data trends and patterns. Simulations are used to assess the suggested methodology, and the results show significant gains in terms of energy savings, network throughput, and overall system performance. This work adds to the continuing attempts to create WSN architectures that are more intelligent and effective and that can function well in contexts with limited resources.