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Summary
This summary is machine-generated.

This study introduces a smart farm data framework using unmanned aerial vehicles (UAVs) for efficient data collection and fog computing for timely processing, reducing network delay.

Keywords:
IoTclusteringfog computingsensorssmart farmingswarm UAVs

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Agricultural Technology

Background:

  • Remote data collection in smart farms is crucial for real-time decision-making.
  • Existing methods face challenges with data processing delays in remote areas.
  • Unmanned aerial vehicles (UAVs) offer mobility for data acquisition in inaccessible locations.

Purpose of the Study:

  • To propose a novel data collection and scheduling framework for smart farms utilizing UAVs and fog computing.
  • To optimize data collection trajectories and fog node selection for efficient processing.
  • To reduce network delay and energy consumption in smart farm data management.

Main Methods:

  • A two-phase framework: data collection and data scheduling.
  • IoT sensors clustered by Received Signal Strength Indicator (RSSI).
  • UAVs calculate optimal trajectories for data gathering and offload to base stations (BS).
  • BS selects fog nodes based on efficiency, response rate, and availability for workload processing.
  • Implementation and simulation using OMNeT++.

Main Results:

  • The proposed framework demonstrates reduced network delay compared to existing methods.
  • The framework shows improved energy efficiency in data collection and processing.
  • Optimized UAV trajectories and fog node scheduling contribute to performance gains.

Conclusions:

  • The developed framework effectively addresses data processing delays in smart farms through UAVs and fog computing.
  • The approach offers a scalable and efficient solution for data management in remote agricultural settings.
  • Further research can explore advanced AI/ML techniques for enhanced decision-making within the framework.