Battery Powered Edge Computing Acceleration for Smart Agriculture Applications: A Use Case for Resonant Ultrasound Spectroscopy

Abstract

Edge computing, using battery-powered devices, presents a viable solution for the real-time data processing for smart agriculture solutions. This paper explores the application of edge computing acceleration for smart agriculture, focusing on the use case of resonant ultrasound spectroscopy (RUS) for grape leaf analysis and monitoring. A methodology for estimating the utilization and performance of both edge and cloud data processing devices is proposed here. The effectiveness of edge and cloud data processing systems is analyzed in terms of data processing waiting time, cost, and battery life of edge devices as a function of intensity of data processing requests and load distribution in various scenarios. The analysis considers such factors as data processing capabilities, equipment cost, and energy consumption to provide insights into the optimal deployment of edge and cloud resources for smart agriculture applications, considering critical waiting and battery time criteria.