Trading Data Size and CNN Confidence Score for Energy Efficient CPS Node Communications

In a context of Cyber-Physical Systems (CPS), energy-efficiency is a critical factor to achieve long operational life-time. The constraint of using battery-powered devices adds degrees of complexity, especially in a hard to reach environment with scarce network and energy resources. The reporting of data consumes large amount of energy, reducing the life-time of both individual nodes and the CPS as a whole. One way to reduce the energy cost of communication is to reduce the number of Bits to transmit. However, this is a viable approach only if the transmitted data remain suitable for further analysis.In this paper, we report on the effect of reducing the image dimensions on the confidence score computed by a convolutional neural network (CNN) determining the species of animals present in images. We also report on the energy consumption of transmitting full vs. reduced dimensions of images. CPS devices and CNNs developed by the Distributed Arctic Observatory (DAO) project are used as experimental platforms.The results show that the energy needed to report the images can be reduced by up to 98% while only reducing the average confidence of determining the species correctly by 0.10%.