# InoT Sensor Network for Wild-Animal Detection near Roads
## 1 Introduction
In terms of improper mounting, the aforementioned system that uses passive infrared sensors, for example, because of wind, may simply become less sensitive, but finer sensibility does not have a significant impact on obtaining and processing appropriate data. As for unconventional, manifold types of systems, it is worth mentioning that, when using cables, such systems are invasive and require people or special equipment to lay the cables. By adding more sensors, although the probability of errors in detection is reduced, the analysis of the incoming data and interpretation of the results becomes more challenging. When placing collars on animals to detect their appearance in certain areas, it is necessary to arrange the required number of collars, as well as to install special stations that can receive the data broadcasted when the animal with a collage-type system approaches within a certain distance. In more recent animal-monitoring systems, animal detection with cameras is becoming more popular, and various methods, models, digital image processing, and machine- and deep-learning algorithms are used for image processing [8; 16; 17; 18; 19]. The most common type of artificial neural networks used in systems processing images obtained by cameras is convolutional neural networks, which are used for image analysis [8; 16; 19]. In the photos submitted to the networks for animal recognition, specific characteristics of animals are discovered with the help of feature descriptors; thus, the artificial intelligence model is trained with a preprepared and labeled data set. Some of the used popular feature descriptors are the oriented gradient histogram, where features are recognized by normalizing the radiometric image and looking for the brightness and gradient of each pixel; a popular algorithm--a transformation of invariant scale features, which determines certain key points and provides them with quantitative information--assigns descriptors to them [16; 17]. Another variant of feature descriptors is described by [PERSON] et al. in their research [17]; the authors propose an ASFAR system that includes operation on images from a \"watching device\" and calculation algorithms in the main computation unit. In the described methodology, a SURF Key Points Detector is used to find key points in the image, and SIFT, SURF, and opponent colors descriptors are used to describe key points by the n-dimensional feature vectors. The chosen methods and algorithms also depend on the field of use of the system in development. In a methodology proposed by [PERSON] et al. in their research [8], livestock monitoring is done via an image-processing algorithm based on shape-constraint classifiers and determining the weight and other characteristics of farmed animals based on predictable fixed dimensions of animal composition. Other systems, suited to monitor animals in the wild by studying their lifestyle and migration routes, use a network of cameras [19; 20]. Taking into consideration the fact that animal monitoring requires minimal intervention in the animal's habitat area and the minimum time spent in it for maintenance and/or repair of the equipment. One of the proposed methods by [PERSON] et al. in their research [19] is to use cameras that automatically trigger video capture, capturing images when animals appear in them, in this way reducing the number of images sent and increasing the camera's lifetime. Another method described by [PERSON] [20] is to set the intervals at which images can be captured, as well as reducing the amount of data transmitted and keeping wildlife tracking efficient and stable, and this is done by using an age-of-information metric for evaluating the recency of data by measuring its delay from the perspective of the recipient. IoT technology offers flexibility in terms of system configuration, network structure, sensors' implementation, and performance optimization. IoT technology is recognizable in recent research works and mainly is being applied for animal monitoring. [PERSON] et al.