# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 1 Introduction  
Extensive livestock farming is a typical technique in many places around the world. It occurs on 25% of global land surface and supports around 200 million subsistence pastoral households [1]. In Africa, 40% of the land is dedicated to extensive pastoralism [2]. Many habitats important for biodiversity conservation have been created by and are still inherently linked to extensive livestock production, in particular grazing. For instance, extensive grazing is considered vital for maintaining many biodiversity-rich habitats in Europe. It is suggested as optimum management of de-intensified grassland to enhance biodiversity [3]. Extensive grazing was reported to positively influence sward species composition and structure which, in turn, provided favourable conditions for colonizing fauna. Furthermore, extensive livestock and the related grazing is also critical for maintaining many of Europe's cultural landscapes and sustaining rural communities. Over the centuries, pastoralism, and transhumance (seasonal movement of livestock between grazing areas) created a wide variety of specific cultural landscapes. The largest remaining extensive pastoral systems on permanent wood pastures in Europe are Dehesa in Spain and Montado in Portugal [4]. Grazing and transhumance are of particular importance for the preservation of open landscapes in the European mountains. In addition, societal concern over thewelfare of farm animals has recently increased and a growing number of citizens in many countries think that it is important to protect the welfare of farm animals [5]; extensive livestock is believed to be advantageous in terms of animal welfare. Extensive livestock farming is, in most cases in Europe, unsupervised. Cattle, mainly bovine, are free in large enclosed remote areas for most of the year. Although shepherds do control the cattle periodically, whenever the weather allows it, animals are collected only for one month to take a veterinary control. This implies moving to the area where the animals are living and looking for them. We are talking about hundreds of hectares, in most cases without any kind of road. Consequently, it is usual that shepherds spend most of their time walking through the field while they are looking for the animals. In order to assist them in this task, some Global Navigation Satellite System (GNSS)-based solutions have been developed for controlling the movements of the animals and reporting them to the farmers. For instance, [PERSON] et al. [6] use a GPRS-based system for monitoring lidia cattle, meanwhile [PERSON] et al. [7] use a SigFox-based one. In Europe, this type of livestock farming is usually restricted to remote areas with low population density. This makes these areas less interesting for telecommunication service provider (TSP), to offer high wand-wind services: high investment for small return (see Figures 1 and 2). Even for technologies developed for the improvement of Internet of Things (IoT) cannot offer coverage for all these remote areas (see Figure 2). There are several other alternatives to cellular networks for IoT monitoring. These can be mesh networks, where link quality indicators could be used to locate animals without GNSS [8]. This kind of deployment is heavily dependent of the gateway which usually becomes the bottleneck and, depending on the orography, requires a router infrastructure that needs considerable amount of energy [9]. There are times where the link between the wireless sensor network (WSN) and the internet is a Wi-Fi transceiver, which is meant to increase the range in zones without GPRS coverage [10]. Figure 1: 3G coverage provided by the main telecommunication service provider (TSP) in the Pyrenees area in Spain (source websites from Movistar, Orange, Vodafone and MasMovil, December 2020). Low-Power Wide Area Networks (LPWAN) are also suitable options, bringing together the low-power qualities of WSN and long-range capacities of cellular networks.