# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 1 Introduction
There are novel modulations such as LoRa that can be used on mesh topologies to extend range, although it is usually used in star topology. Given the benefits such as long range, low-power and cost savings, LoRa is ideal for cattle monitoring [11; 12; 13]. Another appealing alternative is the use of UAVs, which is an emerging application in the field of precision agriculture [14; 15; 16]. Specific usage of UAVs for cattle monitoring is described in [17; 18; 19; 20] which relay on image processing techniques for identify animals and account for them. However, that approach have some limitations when we are concerned about tracking specific individuals (a shepherd would be interested in locating his animals only) and does not allow continuous tracking as GNSS solutions do. Combined usage of UAVs and GNSS is also proposed in [21], which consider drones for easing the retrieve of the tracking information acquired by GPS collars worn by the animals. Nevertheless, a number of technical and administrative issues arise that still possess some difficulties for using UAVs in extensive cattle monitoring, such as the cost or the need of specialist operators (it is required mid/high-size UAVs for covering big extensions), which may also require a flying license and special permission in some countries [22]. Besides localization tracking of the animals using GNSS, registering their status and activities can be of interest to shepherds [23]. Activity detection can be performed with different strategies such as radio frequency identification (RFID) to infer cattle drinking behavior and water intake by detecting proximity to specific places [24]. Using temperature [25] and other biosensors [26] can help to detect a fever state and to assert animal well-being. The most common are inertial sensors based on accelerometers and gyroscopes that are able to identify animal activities such as resting, walking, mating or feeding among others [27; 28]. Figure 2: Coverage provided by SigFox (up) and NB-IoT by Vodafone (down, and probably the best one in Spain) in the Pyrenees area in Spain (source websites from SigFox and Vodafone, December 2020). Using inertial data to extract activities is a very powerful, cheap, energy-efficient and, consequently, broadly adopted strategy. Nevertheless, inertial sensors cannot be directly used, due to the high throughput required: a 12-bit 3-axis accelerometer sampling at 10 Hz generates more than 1.25 Mb of data every hour. Sending this amount of data through LPWAN is too costly in both economic and energetic terms, sometimes violates regional restrictions (e.g., LoRaWAN) or it is just technically impossible (e.g., Sigfox). Thus, raw data needs to be processed in order to extract the most important features, which also helps saving and sending way less data than generated. There are two main strategies that can be applied. Custom data processing techniques to specifically detect the required activities performed by animals [29] and humans [30] is a trending research topic. It is also possible to use data compression strategies to reduce the amount of information to be sent and then decompress and process it in the cloud. Usual techniques for accomplishing data compression are Principal Component Analysis (PCA) [31; 32; 33; 34], Sequential Forward Selection (SFS) [35], Random Subset Feature selection (RSFS) [35], Independent Component Analysis (ICA) [34], I-PCA [34], Vector Quantization (VQ) [36], and Frequency Sensitive Competitive Learning (FSCL) [37; 38]. In this context, we aim to monitor animals in extensive farming in the north part of Spain (in the Pyrenees area, near the border with France). We have two main objectives: Firstly, we need to track the movement of the animals to evaluate their impact on the plant biodiversity. This could be done just by using a device fitted with a GNSS receiver that would collect the positions during the time the animals are in the mountain and download them when they are returned to the barns.