Fuzzy sets and their variants
Sahabul Alam; Joydeep Kundu; Shivnath Ghosh; Arindam Dey
Abstract
Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in network topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Ad-hoc NETworks (FANETs) to conduct effective ad hoc missions. Therefore, ...
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Unmanned Aerial Vehicles (UAVs) bring both potential and difficulties for emergency applications, including packet loss and changes in network topology. UAVs are also quickly taking up a sizable portion of the airspace, allowing Flying Ad-hoc NETworks (FANETs) to conduct effective ad hoc missions. Therefore, building routing protocols for FANETs is difficult due to flight restrictions and changing topology. To solve these problems, a bio-inspired route selection technique is proposed for FANET. A combined trustworthy and bioinspired-based transmission strategy is developed as a result of the growing need for dynamic and adaptable communications in FANETs. The fitness theory is used to assess direct trust and evaluate credibility and activity to estimate indirect trust. In particular, assessing UAV behavior is still a crucial problem in this field. It recommends fuzzy logic, one of the most widely utilized techniques for trusted route computing, for this purpose. Fuzzy logic can manage complicated settings by classifying nodes based on various criteria. This method combines geocaching and unicasting, anticipating the location of intermediate UAVs using 3-D estimates. This method guarantees resilience, dependability, and an extended path lifetime, improving FANET performance noticeably. Two primary features of FANETs that shorten the route lifetime must be accommodated in routing. First, the collaborative nature necessitates communication and coordination between the flying nodes, which uses a lot of energy. Second, the flying nodes' highly dynamic mobility pattern in 3D space may cause link disconnection because of their potential dispersion. Using ant colony optimization, it employs trusted leader drone selection within the cluster and safe routing among leaders. a fuzzy‐based UAV behavior analytics is presented for trust management in FANETs. Compared to existing protocols, the simulated results demonstrate improvements in delay routing overhead in FANET.
