Taking Drones to Next Level: The Performance Evaluation of MAA with BCO Routing Protocols for Flying Adhoc Networks (FANETs)

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Abstract

Flying Ad-hoc Network (FANET) is a special member/class of Mobile Ad-hoc Network (MANET) in which the movable nodes are known as by the name of Unmanned AerialVehicles (UAVs) that are operated from a long remote distance in which there is no human personnel involved. It is an ad-hoc network in which the UAVs can more in 3-D ways simultaneously in the air without any onboard pilot. In other words, this is a pilot-free ad-hoc network also known as Unmanned Aerial System (UAS) and the component introduced for such a system is known as UAV. There are many single UAV applications but using multiple UAVs system cooperating can be helpful in many ways in the field of wireless communication. Deployments of these small UAVs are quick and flexible which overcome the limitation of traditional ad hoc networks. FANETs differ from other kinds of ad-hoc networks and are envisioned to play an important role where infrastructure operations are not available and assigned tasks are too dull, dirty, or dangerous for humans. Moreover, setting up to bolster the range and performance of small UAV in ad-hoc network lead to emergent evolution with its high stability, quick deployment, and ease-of-use for the formation of the network. Routing and task allocation are the challenging research areas of the network with ad hoc nodes. The paper overview is based on the study of biological-inspired routing protocols Moth-and-Ant (MAA) and Bee Colony Optimization (BCO) routing protocol with performance evaluation. The proposed evaluation’s simulations were conducted by performance parameters Packet Delivery Ratio, End to End Delay, Transmission Loss and Network Lifetime.

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APA

Hussain, A., Khan, H. U., Nazir, S., Hussain, T., & Ullah, I. (2021). Taking Drones to Next Level: The Performance Evaluation of MAA with BCO Routing Protocols for Flying Adhoc Networks (FANETs). Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 321–337. https://doi.org/10.14201/ADCAIJ2021104321337

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