Abstract
An edge computing system is a distributed computing framework that provides execution resources such as computation and storage for applications involving networking close to the end nodes. An unmanned aerial vehicle (UAV)-aided edge computing system can provide a flexible configuration for mobile ground nodes (MGN). However, edge computing systems still require higher guaranteed reliability for computational task completion and more efficient energy management before their widespread usage. To solve these problems, we propose an energy efficient UAV-based edge computing system with energy harvesting capability. In this system, the MGN makes requests for computing service from multiple UAVs, and geographically proximate UAVs determine whether or not to conduct the data processing in a distributed manner. To minimize the energy consumption of UAVs while maintaining a guaranteed level of reliability for task completion, we propose a stochastic game model with constraints for our proposed system. We apply a best response algorithm to obtain a multi-policy constrained Nash equilibrium. The results show that our system can achieve an improved life cycle compared to the individual computing scheme while maintaining a sufficient successful complete computation probability.
Highlights
For unmanned aerial vehicle (UAV)-aided edge computing, UAV-aided architectures have been proposed [5,6,7], where a UAV serves as a node that is involved in various tasks—for instance, an edge computing server that executes the computational tasks of nodes, and a relay station to offload computational tasks [8]
We studied our energy efficient edge computing system which has a reliability guarantee via multiple UAVs located near an mobile ground node (MGN), selectively executing computational tasks
We propose a UAV-based edge computing system for MGNs which leverages spatial correlation to reduce unnecessary computation and transmission of the MGN’s data
Summary
Cloud and edge computing have been used in a rapidly growing variety of applications. In a distributed computing framework, an edge computing system provides efficient data computation at the edge of the network This ensures that processing occurs in real-time without the need for the cloud or external data centers, as vehicles such as trains, planes, and connected cars can support the edge computing service [2]. UAVs provide some advantages to edge computing systems, such systems still need guaranteed reliability to enhance the computational task completion and efficient energy management of the system [9,10]. Consider an autonomous vehicle system involving safety production monitoring, automatic driving, and a cooperative-intelligent transport system in a smart city [13,14,15] To operate such a system, the mobile ground node (MGN) should collect and compute data from multiple sources (traffic signals, roadside stations, autonomous vehicles, etc.). Even if the MGN requests a computing service from a UAV, as mentioned above, there are still issues with the reliability of computational task completion and the energy management of such a system
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