Edge intelligence can reduce power dissipation to enable power-hungry long-range wireless applications. This work applies edge intelligence to quantify the reduction in power dissipation. We designed a wireless sensor node with a LoRa radio and implemented a decision tree classifier, in situ, to classify behaviors of cattle. We estimate that employing edge intelligence on our wireless sensor node reduces its average power dissipation by up to a factor of 50, from 20.10 mW to 0.41 mW. We also observe that edge intelligence increases the link budget without significantly affecting average power dissipation. / Master of Science / Battery powered sensor nodes have access to a limited amount of energy. However, many applications of sensor nodes such as animal monitoring require energy intensive, long range data transmissions. In this work, we used machine learning to process motion data within our sensor node to classify cattle behaviors. We estimate that transmitting processed data dissipates up to 50 times less power when compared to transmitting raw data. Due to the properties of our transmission protocol, we also observe that transmitting processed data increases the range of transmissions without impacting power dissipation.