# DRLD-SP: A Deep Reinforcement Learning-based Dynamic Service Placement in Edge-Enabled Internet of Vehicles

arXiv: https://arxiv.org/abs/2106.06291 · HTML: https://mlsanity.com/paper/2106.06291/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (2020)
- [Autonomous Navigation via Deep Reinforcement Learning for Resource Constraint Edge Nodes using Transfer Learning](https://mlsanity.com/paper/1910.05547/) (2019)
- [Survey on Multi-Access Edge Computing for Internet of Things Realization](https://mlsanity.com/paper/1805.06695/) (2018)
- [Edge-enabled V2X Service Placement for Intelligent Transportation Systems](https://mlsanity.com/paper/2001.06288/) (2020)
- [Cost-optimal V2X Service Placement in Distributed Cloud/Edge Environment](https://mlsanity.com/paper/2010.07223/) (2020)
- [Reinforcement Learning-based Dynamic Service Placement in Vehicular Networks](https://mlsanity.com/paper/2105.15022/) (2021)

## Built on by (most cited first, 1 of 1)

- [Adversarial Attacks Against Deep Reinforcement Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.00833/) (2021)

## Abstract (arXiv)

The growth of 5G and edge computing has enabled the emergence of Internet of Vehicles. It supports different types of services with different resource and service requirements. However, limited resources at the edge, high mobility of vehicles, increasing demand, and dynamicity in service request-types have made service placement a challenging task. A typical static placement solution is not effective as it does not consider the traffic mobility and service dynamics. Handling dynamics in IoV for service placement is an important and challenging problem which is the primary focus of our work in this paper. We propose a Deep Reinforcement Learning-based Dynamic Service Placement (DRLD-SP) framework with the objective of minimizing the maximum edge resource usage and service delay while considering the vehicle's mobility, varying demand, and dynamics in the requests for different types of services. We use SUMO and MATLAB to carry out simulation experiments. The experimental results show that the proposed DRLD-SP approach is effective and outperforms other static and dynamic placement approaches.