Computation Offloading for Vehicular Environments: A Survey

Abstract

With significant advances in communication and computing, modern day vehicles are becoming increasingly intelligent. This gives them the ability to contribute to safer roads and passenger comfort through network devices, cameras, sensors, and computational storage and processing capabilities. However, to run new and popular applications, and to enable vehicles operating autonomously requires massive computational resources. Computational resources available with the current day vehicles are not sufficient to process all these demands. In this situation, other vehicles, edge servers, and servers in remote data centers can help the vehicles by lending their computing resources. However, to take advantage of these computing resources, computation offloading techniques have to be leveraged to transfer tasks or entire applications to run on other devices. Such computation offloading can lead to improved performance and Quality of Service (QoS) for applications and for the network. However, computation offloading in a highly dynamic environment such as vehicular networks is a major challenge. Therefore, this survey aims to review and organize the computation offloading literature in vehicular environments. In addition, we demystify some concepts, propose a taxonomy with the most important aspects and classify most works in the area according to each category. We also present the main tools, scenarios, subjects, strategies, objectives, etc., used in the works. Finally, we present the main challenges and future directions to guide future research in this active research area.

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

“…However, this requires the AV to process huge amount of data [15]. Currently, AVs do not possess such computational resources; therefore, computational offloading may be an appropriate solution [73].…”

A context-oriented framework for computation offloading in vehicular edge computing using WAVE and 5G networks

“…GTT decides whether offloading is worthwhile and, if so, decides where to send the tasks. This decision is also the most challenging part of the framework because finding the best way to distribute tasks to minimize application execution time is an NP-hard problem [9]. For this decision, we consider contextual information such as speed, location, direction, CPU capacity and availability, data rates and communication ranges WAVE and 5G, link lifetimes, tasks characteristics, distances between devices, transmission and processing times, connectivity, known routes of vehicles, and estimated arrival time to the destination.…”

“…In this way, there is no exact polynomial-time solution for this problem. Furthermore, this decision needs to consider different contextual parameters [9].…”

“…However, in this computation offloading process, the distinctive aspects of vehicular networks are also not considered, and the objectives do not include reducing the execution time of vehicular applications. In addition, clients have energy constraints that vehicles do not have, and communicating with traditional cloud servers can add high access latency, making applications with ultra-low latency requirements unfeasible [9].…”

“…These servers are deployed close to streets and roads by service providers. Even though they have a little more communication latency and can be quite requested by network devices, these servers generally have greater computational capacity than vehicular clouds [9].…”