# InoT Sensor Network for Wild-Animal Detection near Roads
## 1 Introduction
Passive area-coverage systems detect animals only from the received signal. Infrared and video cameras are the most popular choices. Algorithms for the data processing of systems using the beam-interruption method are simpler than those of the area-coverage type since the processed signals possess an ordinary form and the number of signals received by the sensors directly depends on the number of sensors. In systems using area-coverage methodologies, digital image processing and machine- and deep-learning algorithms are often used for data analysis, and the system can not only process data in real time and store it but also learn from those records [8; 9]. Other types of systems can use cables buried underground; seismic, acoustic, magnetic, ultrasonic, and passive infrared sensors; and the methodology of systems worn on the body of an animal [10; 11; 12; 13; 14]. In cable-based systems, double-cable sensors of a certain length (several hundred meters) are dug underground and are used to detect large and medium-sized animals crossing the cable along its entire length [10]. [PERSON] et al. propose the aforementioned cable-based system in their research [10]. The methodology can be used to detect large- and medium-sized animals and is implemented with 300 m of dual-cable dug underground. When the detection field created above the cables is disturbed, an alarm is set off, and it is determined where the animal has crossed the field. In sensor (ultrasonic, seismic, acoustic) systems, using data processing and merging algorithms, the combination of parts of the results are obtained from several data sensors which help to distinguish and recognize animals crossing/limiting the field of vision of the sensor [11]. [PERSON] et al. [11] propose in their research a solution for animal and personnel detection consisting of processing several sensors individually and evaluating the results of the analysis in a hierarchy. It is also possible to process several sensors individually (passive infrared, seismic sensors); the results from both sensors can be compared, and/or one sensor complements the other. For example, passive infrared sensors that passively receive infrared radiation are less sensitive to nonanimal environment changes than seismic sensors that are located on the ground [12]. Various types of sensors (movement, temperature, humidity, etc.) can be used in systems worn on animal bodies. The received data are processed and used to determine the location of the animal by sending signalswirelessly [15]. In addition, to increase the accuracy of such systems, the number of animals carrying the devices in a conventional area of their residence is periodically updated [14]. Using the beam-termination method, the maximum distance at which the system can detect an animal is generally much greater than that of the covered area systems; in reviewed studies, the systems based on the area-coverage method can detect the animal at a maximum distance of 61-165 m, while the beam-termination systems can detect the animal at 27-402 m maximum distance [1; 3]. In beam-termination systems, it is impossible to specify the direction in which the animal is moving. Animal detection might be considered inadequate depending on what the system is used for because the system detects an animal that has crossed the signal beam, both when entering and leaving the monitored area. A system using such methodology must also be fixed particularly firmly because the beam of the signals used in it, and therefore the detection field of the system itself, is narrow, and, if affected by the environment provided it was not fixed properly, the equipment might issue incorrect data. Meanwhile, in area-coverage systems, it is possible to recognize the direction of animal movement by storing previously received signals and comparing them.