A wireless sensor node (WSN) system, capable of sensing animal motion and transmitting motion data wirelessly, is an effective and efficient way to monitor pigs' activity. However, the raw sensor data sampling and transmission consumes lots of power such that WSNs' battery have to be frequently charged or replaced. The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data sampling frequency to reduce power consumption. In addition, WSNs can transmit less data by sending RFC predictions instead of raw sensor data to save power. The proposed RFC classifies common animal activities: eating, drinking, laying, standing, and walking with a F-1 score of 93%. The WSN power consumption is reduced by 25% with edge computing intelligence, compare to WSN power that samples and transmits raw sensor data periodically at 10 Hz. / Master of Science / A wireless sensor node (WSN) system that detects animal movement and wirelessly transmits this data is a valuable tool for monitoring pigs' activity. However, the process of sampling and transmitting raw sensor data consumes a significant amount of power, leading to frequent recharging or replacement of WSN batteries. To address this issue, our proposed solution integrates edge computing into WSNs, utilizing a Random Forest Classifier (RFC). The RFC is trained and deployed within the WSNs to predict animal behavior, allowing for adaptive adjustment of data sampling frequency to reduce power consumption. Additionally, by transmitting RFC predictions instead of raw sensor data, WSNs can conserve power by transmitting less data. Our RFC can accurately classify common animal activities, such as eating, drinking, laying, standing, and walking, achieving an F-1 score of 93%. With the integration of edge computing intelligence, WSN power consumption is reduced by 25% compared to traditional WSNs that periodically sample and transmit raw sensor data at 10 Hz.