A Neural Network-Based Model of Radio Frequency Energy Harvesting Characteristics in a Self-Powered Food Monitoring System

Most radio frequency (RF) energy harvesters exhibit extremely nonlinear behavior during the harvesting phase, which renders the simulation of these circuits most challenging in design. This paper proposes a method of efficient modeling this nonlinearity based on empirically achieved characteristics of RF energy harvesting. A model using a multi-layer artificial neural network (ANN) is introduced to approximate this behavior. A harvester prototype that uses the far-field RF energy harvesting technique at 915 MHz is developed to verify the proposed model. The results highlight good agreement between the predicted and the observed outcomes of the harvester. With its flexibility, the proposed model can be further applied to characterize any nonlinear device. In addition, a self-powered system embedded with a fully-passive sensor device to monitor food storage profile is presented in the paper. An analysis of the power harvesting performance shows that the device can self-start its operation where the RF power is as low as −10.5 dBm, and the maximum achievable power conversion efficiency is 43%. To demonstrate the applicability of the proposed self-powered food monitoring system, an experiment is carried out using pork. Support vector machine (SVM) classifiers with two types of features are investigated to compare their performance of classifying three levels of food condition. As a result, the best classification accuracy of 95.7% is achieved.