Deadline-Aware Task Offloading for Vehicular Edge Computing Networks Using Traffic Light Data

Pratham Oza, Nathaniel Hudson, Thidapat Chantem, Hana Khamfroush

Research output: Contribution to journalArticlepeer-review


As vehicles have become increasingly automated, novel vehicular applications have emerged to enhance the safety and security of the vehicles and improve user experience. This brings ever-increasing data and resource requirements for timely computation by the vehicle’s on-board computing systems. To meet these demands, prior work proposes deploying vehicular edge computing (VEC) resources in road-side units (RSUs) in the traffic infrastructure with which the vehicles can communicate and offload compute-intensive tasks. Due to the limited communication range of these RSUs, the communication link between the vehicles and the RSUs — and, therefore, the response times of the offloaded applications — are significantly impacted by vehicle mobility through road traffic. Existing task offloading strategies do not consider the influence of traffic lights on vehicular mobility while offloading workloads onto the RSUs. This causes deadline misses and quality-of-service (QoS) reduction for the offloaded tasks. In this article, we present a novel task model that captures time and location-specific requirements for vehicular applications. We then present a deadline-based strategy that incorporates traffic light data to opportunistically offload tasks. Our approach allows up to 33% more tasks to be offloaded onto RSUs compared with existing work without causing deadline misses, maximizing the resource utilization of RSUs.

Original languageEnglish
Article number8
JournalTransactions on Embedded Computing Systems
Issue number1
StatePublished - Jan 10 2024

Bibliographical note

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  • Edge computing
  • connected traffic infrastructure
  • task offloading

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture


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