Can I read K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks on EtoBox?

K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks by Saifullah; Zhi Ren; Khalid Hussain; Muhammad Faheem is a Computer Science article available to read on EtoBox.

What is K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks about?

Unmanned Aerial Vehicles (UAVs) have become a hot topic due to their flexible architecture adopted in many wireless technologies. In UAV ad hoc networks, traditional routing protocols with a fixed topology are ineffective due to dynamic mobility and unstable paths. Therefore, the mobility patterns of UAVs challenge efficient and reliable routing in UAV networks. Traditional routing algorithms are often based on assumptions of static nodes and predetermined network topologies. Which are not suitable for the dynamic and unpredictable nature of UAV mobility patterns. To address this problem, this paper introduces a K-means online learning routing protocol (KMORP) scheme employing a Markov mobility model for UAV ad hoc networks. Initially, the proposed method utilizes a 3D Gauss Markov mobility model to accurately estimate UAV positions, while K-means online learning is adopted for dynamic clustering and load balancing. Designed for real-time data processing, KMORP is well suited for UAV ad hoc networks, quickly adapting to network environmental changes such as UAV mobility, interference, and signal degradation to ensure efficient data transmission and communication. This is achieved w

Who reads K-means online-learning routing protocol (K-MORP) for unmanned aerial vehicles (UAV) adhoc networks?

Author

Saifullah; Zhi Ren; Khalid Hussain; Muhammad Faheem

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Computer Science (Physical Sciences)