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Energy-Efficient Cluster-Based Data Collection by a UAV with a Limited-Capacity Battery in Robotic Wireless Sensor

Omer Melih Gul1, Aydan Muserref Erkmen1

  • 1Department of Electrical and Electronics Engineering, Middle East Technical University (METU), Cankaya, 06800 Ankara, Turkey.

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|October 21, 2020
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Summary

This study introduces an energy-efficient data collection strategy for unmanned aerial vehicles (UAVs) in robot networks. The optimized approach minimizes UAV energy consumption and ensures network lifetime by intelligently selecting cluster heads for data collection.

Keywords:
cluster-based routingenergy efficient routingrobotic networkunmanned aerial vehicle (UAV)wireless sensor network (WSN)

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Area of Science:

  • Robotics
  • Wireless Sensor Networks
  • Energy Efficiency

Background:

  • Mobile data collection in robot networks using unmanned aerial vehicles (UAVs) presents energy constraints.
  • Cluster heads (CH) manage data collection within robot clusters, posing challenges for UAVs with limited battery capacity.

Purpose of the Study:

  • To develop an energy-efficient data collection strategy for a mobile sink (UAV) in a clustered robot network.
  • To minimize total UAV energy consumption and data collection costs by optimally selecting CH robots to visit.
  • To analytically derive an optimal approach considering the UAV's constant battery capacity.

Main Methods:

  • Formulating an optimization problem to minimize UAV energy consumption and data transmission costs.
  • Developing a strategy for the UAV to select a subset of CH robots based on location and battery constraints.
  • Incorporating data transmission path optimization for CH robots not visited by the UAV.
  • Deriving an analytical solution for the energy-efficient data collection problem.

Main Results:

  • The proposed strategy effectively minimizes UAV energy consumption and associated data collection costs.
  • The analytical approach provides an optimal solution considering the UAV's finite battery capacity.
  • Numerical simulations demonstrate superior performance compared to existing methods in terms of total energy consumed by CH robots.

Conclusions:

  • The developed strategy significantly enhances energy efficiency in UAV-based data collection within robot networks.
  • The approach contributes to extending network lifetime by optimizing energy usage among CH robots.
  • This work offers a novel analytical solution for energy-constrained mobile data collection in clustered robotic systems.