Abstract
Sensor networks allow us to collect data, such as camera images, over a wide area. Understanding the sensing area by aggregating and processing the data from multiple sensors is promising. Deep Learning (DL) is a powerful method for interpreting data. However, communication cost on the sensor network and computational cost on the server are two substantial problems of aggregating and processing data from multiple sensors. We therefore propose divided processing of the DL between a server and a powerful Edge-Heavy Sensor (EHS). In our study, we reduced transmission data to twelve times lower than the amount of raw input data while maintaining a 4.5% decrease in the DL's recognition accuracy.