Memory, Channel and Process Utilization for Fuzzy based Congestion Detection and Avoidance Scheme in Flying Ad Hoc and IoT Network

Abstract

UAVs are flying in the air at different speeds and continuously forwarding the collected information to other UAVs or IoT devices in FANET. UAVs are playing an important role in data collection from places where humans can’t reach them easily. The UAVs are intelligent devices, and these devices have sufficient bandwidth and memory for data forwarding and storing. The role of UAVs is specific, and they have the reflexibility to change the battery and control the data interval to control the congestion in network. The IoT devices with FANET can transfer the valuable data to other IoT devices for verification and matching. The proper utilization of bandwidth, memory, energy and processing capability are able to increase the Quality of Service (QoS) in FANET. In this paper, proposed the Memory, channel and Process utilization for Fuzzy based (MCPFB) for congestion detection and avoidance scheme to improve bandwidth utilization, energy consumption in FANET with the IoT network. primarily aims to identify and prevent network congestion, which is crucial for maintaining the QoS requirements and ensuring reliable communication. Congestion is a phenomenon that arises when the volume of data transmitted across a network exceeds its capacity. These factors can lead to disruptions, reduced efficiency, and potential data loss in communication networks such as Flying Ad Hoc Networks (FANETs). To effectively handle congestion in FANET and provide reliable communication in challenging and dynamic environments, it is crucial to employ efficient resource management, intelligent algorithms, and adaptable protocols. The process of designing fuzzy rules for Flying Ad Hoc Networks (FANET) entails developing a set of guidelines that utilize fuzzy logic to make decisions pertaining to different parts of the network. The MCPFB is better than the previous BARS approach in terms of different performance metrics.