A Machine Learning Perspective of Optimal Data Transmission in Wireless Sensor Networks (WSN)

Wireless sensor networks are a major component of many industrial and scientific applications. Coverage and improved data aggregation are the two key factors considered when designing the network. Data from various sensors must first be gathered and summarized before being sent to the sink. The first step in improving data transmission in a wireless sensor network is determining the most efficient way to transport data from the sensors to the central node (WSN). To efficiently transport data in wireless sensor networks from diverse sensor nodes to sink nodes, this paper presents a reinforcement learning approach.