The Congestion Control and Performance Improvement by Fuzzy techniques in FANET with IoT: A Survey

Now a day's More and more people use UAV-to-UAV communications to send information from one place to another in the air. UAVs are able to communicate with other UAVs and directly transfer the information to the tower through the base station (BS) or directly. The BS is only there to collect all the information from different UAVs and forward that information outside the network. The role of the Internet of Things (IoT) with FANET is to control the information of UAVs and also send feedback to UAVs related to data forwarding. IoT nodes are capable of controlling the working capabilities, of devices but do not affect their actual functioning. The need for an IoT device is only to control the data it receives and sends over the network. Using the idea of Artificial Intelligence (AI), we present a study of strategies that aim to increase FANET performance and reduce congestion using the Internet of Things (IoT). AI concepts like fuzzy logic, machine learning, etc. are able to improve the accuracy of the output after applying learning and training concepts. BSs are not capable of giving instructions to UAVs, and in normal communication, BSs do not check the data to see if it is appropriate or inappropriate. In this survey, also focus on the routing protocols and different issues in FANET and IoT with applications. IoT devices can select only the data from a larger set that is required or requested by the receiver. Data that is received by nodes is transferred to the IoT to check whether the data is valuable or not. The control of device data has been made possible by establishing standards on IoT nodes.