Trajectory Design for UAV-Based Data Collection Using Clustering Model in Smart Farming.

Tariq Qayyum,Asad Malik,Zouheir Trabelsi,Kadhim Hayawi

doi:10.3390/s22010037

Abstract

Unmanned aerial vehicles (UAVs) play an important role in facilitating data collection in remote areas due to their remote mobility. The collected data require processing close to the end-user to support delay-sensitive applications. In this paper, we proposed a data collection scheme and scheduling framework for smart farms. We categorized the proposed model into two phases: data collection and data scheduling. In the data collection phase, the IoT sensors are deployed randomly to form a cluster based on their RSSI. The UAV calculates an optimum trajectory in order to gather data from all clusters. The UAV offloads the data to the nearest base station. In the second phase, the BS finds the optimally available fog node based on efficiency, response rate, and availability to send workload for processing. The proposed framework is implemented in OMNeT++ and compared with existing work in terms of energy and network delay.

Highlights

The rapid advancement in wireless sensor networks (WSN) has emerged with an assortment of curiosity [1,2]
The sensors are deployed randomly across the farm; there exist base stations at the boundary of the farm, and dedicated fog nodes are connected with each BS
The size of the data packet is given in the table for processing, and the data packet is translated into million instructions per second (MIPS)

Summary

Introduction

The rapid advancement in wireless sensor networks (WSN) has emerged with an assortment of curiosity [1,2]. The most common applications of UAV in data collection are covered in [4,5]. The clustering schemes on WSN are used for data collection to preserve energy and reduce communication overhead [6]. Sometimes, these locations include hilly areas and large farms where nature hurdles such as trees, small mountains, or unexpected slopes cause signal distortion. These locations include hilly areas and large farms where nature hurdles such as trees, small mountains, or unexpected slopes cause signal distortion In such cases, it is difficult to gather data. The use of UAVs in such areas can reduce the cost and increase the efficiency of data collection [7]

Methods

Results

Conclusion