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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
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Data Offloading in UAV-Assisted Multi-Access Edge Computing Systems: A Resource-Based Pricing and User Risk-Awareness

Giorgos Mitsis1, Eirini Eleni Tsiropoulou2, Symeon Papavassiliou1

  • 1School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athina, Greece.

Sensors (Basel, Switzerland)
|April 30, 2020
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Summary
This summary is machine-generated.

This study introduces a pricing policy for Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) systems. It models user behavior using Prospect Theory to optimize task offloading and resource competition.

Keywords:
UAV-enabled computingdata offloadingmulti-access edge computing systemsresource-based pricingrisk-awareness

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicle (UAV)-assisted Multi-access Edge Computing (MEC) offers dynamic task offloading.
  • Viability requires profit models for UAV MEC server operators.
  • User competition for limited UAV MEC resources needs efficient management.

Purpose of the Study:

  • To develop a usage-based pricing policy for UAV-assisted MEC systems.
  • To model users' risk-aware decision-making in data offloading.
  • To address resource competition using game theory and economic principles.

Main Methods:

  • Applied Prospect Theory to model user risk-seeking and loss aversion.
  • Utilized the Tragedy of the Commons theory for resource exploitation.
  • Formulated a non-cooperative game to maximize users' expected utility.
  • Proved the existence of a Pure Nash Equilibrium (PNE) using submodular game theory.
  • Introduced an iterative algorithm based on best response dynamics.

Main Results:

  • The proposed pricing mechanism encourages more socially optimal user behavior.
  • A distributed algorithm effectively converges to the PNE.
  • Simulations demonstrate the approach's effectiveness and benefits in UAV-assisted MEC environments.

Conclusions:

  • The pricing policy balances user incentives and resource management in UAV MEC.
  • Prospect Theory and game theory provide a robust framework for analyzing user offloading decisions.
  • The developed algorithm offers an efficient solution for decentralized resource allocation.