Onboard Edge Computing: Optimizing Resource Allocation and Offloading in Mobile Scenarios
Zijian Chen, Hong Zhang, Miao Wang, Liqiang Wang, Lei Zhang · IEEE Internet of Things Journal · 2024
The rapid development of the Internet of Things (IoT) has propelled mobile edge computing (MEC) into the forefront of both academia and industry. Nevertheless, the surge in urban activities driven by economic development is putting a strain on infrastructure like transportation and utilities. Increased demand for computing tasks and server failures from natural disasters can severely strain MEC in a specific region due to its reliance on static edge servers. To address these challenges, we introduce an MEC framework called onboard edge computing (OBEC), which explores onboard servers to provide computational offloading services in mobile scenarios. To determine the end devices that each onboard server will serve, we propose the concept of “hunger value” to accurately measure the resource idleness of an onboard server. We also implement a Genetic Optimization-based Hunting-Predation Algorithm, an onboard server as a predator and a service end device as a prey, to minimize the overall hunger value of the whole system. Taking into account the power consumption of onboard servers and the satisfaction of end devices, we introduce a Stackelberg game to allow each onboard server to select the optimal serviced end devices and efficiently provide the required resources. Since this Stackelberg game lacks an analytical solution, we employ gradient descent to calculate the optimal offloading and resource allocation strategy. Finally, we conduct simulation experiments to demonstrate the superiority of the proposed OBEC framework over other state-of-art methods across various scenarios, underscoring its potential to foster synergistic interactions between servers and end devices.