Optimal Data Transmission and Improve Efficiency through Machine Learning in Wireless Sensor Networks - Park, Hyunjoo; Jeon, Junheon (Society of Visual Informatics, 2022)
Each sensor node in WSN is typically equipped with a limited capacity small battery. Energy-efficient communication is therefore considered a key component of network life extension. In addition, as the utilization of the sensor network increases, duplicate data and abnormal data is also collected to reduce the accuracy of the data in various environments. AI is used to recognize data anomaly values and increase packets' accuracy by removing out-of-range data. This can improve performance through optimal data transmission, resulting in increased network life, energy efficiency, and reliability. This paper proposes a protocol called MLQ-MAC that reflects the above. MLQ-MAC uses AI techniques to consider different types of data packets. The data collected by the sensor removes the measurement anomaly and duplicate data and stores it in a different transmission queue by priority. Efficient data transfer is possible by using an AI Discriminator for accurate classification before being stored on a transmission queue. The AI-Discriminator classifies a variety of factors, including the collection environment, characteristics of network applications, and so on. It also uses two new technologies: self-adaptation and scheduling for efficient transmission. In the protocol, the receiver adjusts the duty cycle according to to transmit urgency to improve network QoS. Finally, the simulation results show that the MLQ-MAC protocol reduces energy consumption at the receiver by up to 3.4% and per bit by up to 2.3% and improves packet delivery accuracy by up to 3%.