Monitoring sheep activity can be crucial for improving productivity and animal welfare. This work presents the design, manufacture, and test of a collar-type device to monitor sheep behavior. The device consists of an MSP-EXP432P401R microcontroller from Texas Instruments, a Bosch Sensortec’s BMI160 3-axis accelerometer, and a narrowband-IoT BG96 modem from Quectel that includes a global positioning system. The device has two operating modes: 1) validation mode (VM) to test and validate algorithms for characterizing sheep activity and 2) research mode (RM) to support multiday animal experiments to study their behavior. In VM, it sends accelerometer data, the animal’s state (run, walk, stand, or head down), and the location to the Central System every 20 s. VM has an autonomy of 51 h. In RM, the device transmits the animal’s state and the location every 2 or more minutes to extend the autonomy to more than ten days. The microcontroller identifies the sheep’s states (every 5 s) using real-time accelerometer data processed with an algorithm based on the linear discriminant analysis method. We trained a classifier on a PC using a public dataset, and then we ported it to the microcontroller. Preliminary tests show that the sheep’s state identification has a prediction success rate of 88%, opening exciting possibilities for developing an applicable device.