New Bargaining Game Based Computation Offloading Scheme for Flying Ad-hoc Networks

Sungwook Kim

doi:10.1109/access.2019.2946363

Abstract

The past few years have witnessed a tremendous increase on the use of unmanned aerial vehicles (UAVs) in a wide range of civilian and commercial applications. UAVs are expected to be an important component for 5G and beyond 5G networks. However, there are many challenges associated with the development of UAV networks and applications. In this study, we focus on the heavy computation tasks of UAVs, and design a new and novel task offloading scheme for UAV networks. To achieve the best possible tradeoff between communication delay and computation cost, we adopt the basic concept of $\left ({\alpha, \beta }\right)$ -bargaining solution, and formulate a cooperative bargaining game model to solve the UAV computation offloading problem. According to the characteristics of $\left ({\alpha,\beta }\right)$ -bargaining solution, the main advantage of our approach is to provide an axiom-based strategic solution for the task offloading problem while dynamically responding to the current UAV conditions. Extensive simulations are performed in order to confirm the performance superiority of our proposed scheme compared to the existing state-of-the-art protocols. Numerical results show that our approach achieves in average about 10% and 20% better performance results in terms of system throughput, task failure probability, and energy efficiency ratio of UAVs. Finally, a few of open problems are outlined and identified as possible future research directions.

Highlights

Unmanned aerial vehicles (UAVs) are a class of aircrafts that can fly without the onboard presence of human pilots in order to control their motions
SIMULATION RESULTS AND DISCUSSION the performance of our proposed scheme is evaluated via simulation, and compare it with that of the existing ELHO[4], GTEC [1] and Sequential Game based Computation-Offloading (SGCO)[3] schemes in terms of system throughput, task failure probability, and energy efficiency ratio of UAVs
SUMMARY AND CONCLUSION Thanks to the recent technological advances, flying ad hoc networks (FANETs) system is becoming a promising solution for different application scenarios involving multiple UAVs, which can automatically fly without human help

Summary

INTRODUCTION

Unmanned aerial vehicles (UAVs) are a class of aircrafts that can fly without the onboard presence of human pilots in order to control their motions. S. Kim: New Bargaining Game Based Computation Offloading Scheme for Flying Ad-hoc Networks capabilities of UAVs. Usually, computation-intensive applications require high processing and energy resources, which affect real-time operations and the life-time of an UAV system or even might eventually impact on a mission success [3]–[4]. We focus on a cooperative game model for the UAV task partition problem. By adopting an effective cooperative bargaining solution, we design a new UVA computation offloading scheme for FANETs; our approach can preserve the novelty of bargaining game such as self-adaptability and real-time effectiveness while ensuring relevant tradeoff between efficiency and fairness. Motivated by the above discussion, our proposed UAV task offloading scheme is designed based on the (α, β)-bargaining solution.

RELATED WORK

THE UAV COMPUTATION TASK OFFLOADING ALGORITHM

SIMULATION RESULTS AND DISCUSSION

SUMMARY AND CONCLUSION