For many years, the area of wireless sensor networks (WSN) has been popular for its wide range of time-critical and potential applications. However, it has many challenges that require more attention from the research communities to improve the network’s operational efficiency. However, with consistently rising concerns for energy efficiency and optimized data transmission performance, most current research emphasises minimum power consumption and reliable data transmission aspects. The critical analysis and study of related works exhibit the shortcomings in existing data transmission schemes, which fail to cope with the dynamic conditions of WSNs on a larger scale and do not retain considerable energy performance. The study thereby introduces a unique approach to an energy-efficient and reliable data transmission framework that formulates machine learning-driven functional components to ensure effective data gathering, aggregation, and routing and dissemination strategies to properly balance energy and data transmission performance in WSN under dynamic conditions. The proposed framework's performance evaluation considers multiple metrics, such as analysis of network lifetime, Energy Consumption, Throughput, and Latency performance. The experimental outcome shows that the proposed system outperforms the existing baselines for the above performance metrics.
Ch et al. (Mon,) studied this question.