A Lora-Based and Maintenance-Free Cattle Monitoring System for Alpine Pastures and Remote Locations
<|reference_start|>A Lora-Based and Maintenance-Free Cattle Monitoring System for Alpine Pastures and Remote Locations: The advent of the Internet of Things (IoT) is boosting the proliferation of sensors and smart devices in industry and daily life. Continuous monitoring IoT systems are also finding applications in agriculture, particularly in the realm of smart farming. The adoption of wearable sensors to record the activity of livestock has garnered increasing interest. Such a device enables farmers to locate, monitor, and constantly assess the health status of their cattle more efficiently and effectively, even in challenging terrain and remote locations. This work presents a maintenance-free and robust smart sensing system that is capable of tracking cattle in remote locations and collecting activity parameters, such as the individual's grazing- and resting time. To support the paradigm of smart farming, the cattle tracker is capable of monitoring the cow's activity by analyzing data from an accelerometer, magnetometer, temperature sensor, and Global Navigation Satellite System (GNSS) module, providing them over Long Range Wide Area Network (LoRaWAN) to a backend server. By consuming 511.9 J per day with all subsystems enabled and a data transmission every 15 minutes, the custom-designed sensor node achieves a battery lifetime of 4 months. When exploiting the integrated solar energy harvesting subsystem, this can be even increased by 40% to up to 6 months. The final sensing system's robust operation is proven in a trial run with two cows on a pasture for over three days. Evaluations of the experimental results clearly show behavior patterns, which confirms the practicability of the proposed solution.<|reference_end|>
@article{schulthess2024a,
    title={A Lora-Based and Maintenance-Free Cattle Monitoring System for Alpine
  Pastures and Remote Locations},
    author={Lukas Schulthess, Fabrice Longchamp, Christian Vogt, Michele Magno},
    journal={arXiv preprint arXiv:2406.06245},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06245},
    primaryClass={eess.SY cs.SY eess.SP}
}