# InoT Sensor Network for Wild-Animal Detection near Roads
## 1 Introduction
in their research [21] present a smart dairy concept with attachable sensor nodes used for cattle coordinate tracking and live monitoring of body parameters, such as temperature, heart rate, etc. [PERSON] et al. in their publication [22] present the structure of the IoT network for livestock (particularly sheep)-monitoring collars to acquire the states of eating off the ground, standing, walking, running, etc. The appearance of wildlife in urban areas is quite rare in Lithuania; the majority of vehicle accidents with wild animals are recorded on trunk roads at night. In Lithuania, from January to October 2023, 4764 accidents with wild animals were reported. This makes up 26% of all traffic accidents [23]. This percentage is constantly increasing, and it doubled since 2017. The population of wild ungulates in Lithuania is large and is increasing. About 19,000 moose, 68,000 deer, and 169,000 doe-deer populations were reported in 2022 by hunting associations. The wild boar population in Lithuania, according to data from 2022, is about 30,000 individuals [24]. Various solutions of modulations as OFDM/FDMA DBPSK (Sigfox) or LoRa [25; 26], the technologies used in wireless LPWAN [27; 28; 29; 30], enable the technical background diversity and usability in various scenarios for wireless animal tracking purposes. The published research works that are aimed at animal tracking utilize the concept of wireless network solutions. Bluetooth low energy (BLE) and LoRa are referred to by [PERSON] et al. in their publications [31; 32]. The approach of wild-animal tracking in this scientific work is based on dual radio-network architecture to realize collar communication as the gateway for wildlife monitoring purposes. A similar approach to tracking wild animals with collars via a LoRa network is described by [PERSON] et al. in their publication [33]. The tracking of individual animals is possible and applicable for small herds of big animals, but, in road safety applications, animals without collars cannot be tracked. The method of animal tracking using image data is proposed by [PERSON] et al. In their publication [34], they offer an optical range image analysis and recognition method within the LoRa network to implement animal recognition and classification between wild and domestic animals. This method is suitable for farming applications but requires high processing capabilities (Raspberry pi) for solution. In road safety applications, where most of the animal appearances on the road occur during the dark period of the day, this method is considered not prospective for the end product. A similar method was proposed by [PERSON] et al. in their publication [35] by classification of the optical range camera captured images by machine-learning algorithms and alerting via LoRa communication. It is very difficult to get the photographic quality of the images and also for the animal detection system to operate autonomously; the image capturing has to be activated during the presence of the animal to reduce energy and processing expenses, which was not addressed in works [34; 35]. The image capturing is foreseen as an additional information source and the authors consider also thermal imaging assistance for the identification of wildlife movement in the dark period of the day. The overviewed methods [31; 32; 33; 34; 35] are considered to have drawbacks (due to the necessity of attaching collars to animals or processing optical range images). The concept of wildlife detection proposed by the authors of this paper is a low-power and autonomous stationary sensing network with visual signaling functionality. For quality of service, Sigfox and LoRa utilize unlicensed frequencies; NB-IoT uses LTE-licensed bands and can provide the same QoS as guaranteed by mobile operators. Regarding the reliable measurement devices' data transfers, the NB-IoT is a better choice compared to LoRa or Sigfox.