Fuzzy logic based trajectory optimization for livestock health monitoring using Internet of Drones (IoD)

The Internet of Drones (IoD) is an architecture created to provide control and access between drones and users via the Internet. In actuality, drones are quickly becoming widely accessible commodities that allow any user to fly a variety of missions in regulated airspace using a variety of drones. Exchanging large amounts of data within a short period of time, typically over low data rate connections, remains a key challenge for IoT networks. One solution could be data classification, which is the method of classifying data into pertinent categories to safeguard and utilize it more effectively. In this paper, we present a fuzzy model to classify animal health condition data, collected by the IoT, for a livestock monitoring application. Fuzzy logic encodes experience-based knowledge that computers can understand in the form of logical rules. Our model takes body temperature, blood oxygen levels, amount of viruses and pathogens, and heart rates as input parameters and provides a crisp output that classifies the overall health conditions of farm animals into three distinct priority levels: excellent, average, and critical. The proposed model has an average accuracy of 85.21, which is higher than the K-nearest neighbor and random forest algorithm. Classifying data based on priority levels limits unwanted data transmission, and assists appropriate decisions for required actions, hence improving the efficiency of the network. This paper highlights our model's utility in IoT data classification and discusses how this can be utilized in the farm animal monitoring scenario. This model can be applied to other scenarios, such as prioritizing human emergency care, with minor changes to the classification model.