# InoT Sensor Network for Wild-Animal Detection near Roads
## 5 Discussion and Conclusions  
During the trial of the system, which lasted from July 2021 to April 2022, the trial operation of the DSN nodes and the registration node was carried out in the designated road section.  
Figure 14: A series of thermal imaging images captured on 6 April at 5 a.m. 54 min, indicating a time-recorded dangerous incident.  
Figure 13: Thermovision photos of the detected wildlife: (**a**) The group of animals crossing the road from left to right; (**b**) One animal crossing the road from right to left.  
During the first months of testing, several different DSN design options were tested: several different mounting angles of the side PIR sensor lenses and the impact of the rain canopy.  
After analyzing the performance of the first-generation DSN sensors, the design of the second-generation DSN was modified: a combined design of side PIR sensors and an LED signaling node with white Fresnel lenses was selected; the rear PIR sensor was removed since its activations both near the forest and near the meadow/field are more misleading due to impact of plants, bushes, and branches moving in the wind.  
A modified unit for registration and verification of DSN triggers was proposed and implemented. The first version with a hunter camera responding to DSN triggers and photographing in optical range with short-wave infrared illumination was rejected due to the covered distance being too small. It was replaced with a thermal imaging camera. The registration unit itself has been moved to a roadside-marker post. This allowed for a much more reliable assessment of the activations of the installed DSNs in the viewing area of this node reaching up to 300 m.  
During the tests, even at the darkest time of the year, lasting from November to January, the autonomous power supply of the DSN from the integrated solar panels and batteries was sufficient. In an untypical situation due to a statistically excessive number of sensor activations or drained battery, DSN nodes could switch to safe-operation mode, where the main priority is energy saving/accumulation in the battery up to a level suitable for operating.  
It was found that the DSN practically always reacted to the larger animals (e.g., deer) running into the monitored roadside area.  
The main causes of false DSN sensor triggers were those caused by nonanimals. They were due to passing large trucks and buses, most likely driving on wet road surfaces.  
Single and hard-to-explain, repeating less than a few times per day, activations were mostly observed in foggy conditions and sometimes during rain and snowfall. In the warm season, grassy plants growing near the DSN sensors and arachnids moving in the sensor aperture area can also cause false activations.  
Analysis of the IR images from April 2022, when many animals were recorded each night, showed that, on some nights, the animals stayed in the meadow next to the road for quite a long time, lasting up to several hours. In these cases, there may be a problem with warning the drivers about them through the DSN light signal because of excessive energy consumption from the autonomous DSN battery. The monitoring system proposed in the paper has a LoRa gateway which collects data from the DSN nodes. The same gateway is connected to the IoT network using a 4G modem. All collected data can be sent/processed via cloud services. End users who will approach near the monitoring area can be informed via a mobile app that animals were spotted nearby.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.