Trajectory design for uav-based data collection using clustering model in smart farming

Tariq Qayyum, Zouheir Trabelsi, Asad Malik, Kadhim Hayawi

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Article number37
Issue number1
Publication statusPublished - Jan 1 2022


  • Clustering
  • Fog computing
  • IoT
  • Sensors
  • Smart farming
  • Swarm UAVs

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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