Task Offloading of Deep Learning Services for Autonomous Driving in Mobile Edge Computing

Abstract

As the utilization of complex and heavy applications increases in autonomous driving, research on using mobile edge computing and task offloading for autonomous driving is being actively conducted. Recently, researchers have been studying task offloading algorithms using artificial intelligence, such as reinforcement learning or partial offloading. However, these methods require a lot of training data and critical deadlines and are weakly adaptive to complex and dynamically changing environments. To overcome this weakness, in this paper, we propose a novel task offloading algorithm based on Lyapunov optimization to maintain the system stability and minimize task processing delay. First, a real-time monitoring system is built to utilize distributed computing resources in an autonomous driving environment efficiently. Second, the computational complexity and memory access rate are analyzed to reflect the characteristics of the deep learning applications to the task offloading algorithm. Third, Lyapunov and Lagrange optimization solves the trade-off issues between system stability and user requirements. The experimental results show that the system queue backlog remains stable, and the tasks are completed within an average of 0.4231 s, 0.7095 s, and 0.9017 s for object detection, driver profiling, and image recognition, respectively. Therefore, we ensure that the proposed task offloading algorithm enables the deep learning application to be processed within the deadline and keeps the system stable.

DRL-Based Computation Offloading and Resource Allocation in Green MEC-Enabled Maritime-IoT Networks

“…Similar to IoT devices (IoTD), MIoT devices (MIoTD), such as various sensors, smart buoys, unmanned surface vessels (USV), and floats, have stringent computation and energy resource limitations. To reduce the burden of powering and computing for task computation and reduce task processing latency at terminals, Mobile Edge Computing (MEC) is gradually replacing the cloud computing paradigm, envisioned as a promising paradigm, by putting lightweight servers in closer proximity to the terminals [6]. MEC architecture can be employed in a variety of IoT application scenarios, like 6G communication, virtual reality, the Internet of Vehicles (IoV), smart cities, smart factories, and more.…”

Cooperative Environmental Perception Task Offloading for Connected and Autonomous Vehicles

“…In traditional schemes [12][13][14][15][16][17][18][19], once a CAV offloads a task to the edge server, the computation result of the task is returned to the CAV. After that, the CAV transmits the result to the specific CAVs or simply broadcasts the result to its adjacent CAVs, which is inefficient.…”