Cluster based UAV Path Planning Using White Shark Optimizer (WSO) Algorithm for LoRaWAN

Abstract

Introduction: LoRaWAN defines the network architecture and communication protocol for Long-distance devices among Low-power Wide Area Network (LPWAN). Wide-ranging coverage and great mobility of Unmanned Aerial Vehicles (UAVs) provide up new possibilities for data collection. The main problem in UAV data collection is to tackle the path planning.

Objectives: The main objective of this work is to determine the UAV trajectory path to minimize the UAV flying distance and data gathering time.

Methods: An effective Cluster based UAV Path Planning (CPP) using White Shark Optimizer (WSO) algorithm is proposed. The proposed algorithm is based on distance among the cluster head (CH) and the UAV Data Collection Point (DCP), energy level of the nodes and sensor’s data generation rate.

Results: By simulation results, it has been shown that CPP-WSO attains lesser data collection delay and packet drop rate with higher packet delivery ratio and average residual energy.

Conclusion: The proposed CPP-WSO enhances the lifetime of both sensor nodes as well as UAV.