# Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles

arXiv: https://arxiv.org/abs/2408.03435 · HTML: https://mlsanity.com/paper/2408.03435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 24 references that are in the map.
- First posted on arXiv 6 Aug 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)
- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 9 of 9)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Dynamic Split Computing for Efficient Deep Edge Intelligence](https://mlsanity.com/paper/2205.11269/) (2022)
- [Vehicular Edge Computing and Networking: A Survey](https://mlsanity.com/paper/1908.06849/) (2019)
- [Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks](https://mlsanity.com/paper/2010.01722/) (2020)
- [ACHORD: Communication-Aware Multi-Robot Coordination with Intermittent Connectivity](https://mlsanity.com/paper/2206.02245/) (2022)
- [Follow-Me Cloud: When Cloud Services Follow Mobile Users](https://mlsanity.com/paper/2201.05068/) (2022)
- [Empirical Analysis of Bi-directional Wi-Fi Network Performance on Mobile Robots in Indoor Environments](https://mlsanity.com/paper/2110.03011/) (2021)
- [Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications](https://mlsanity.com/paper/2310.08364/) (2023)
- [Analog Twin Framework for Human and AI Supervisory Control and Teleoperation of Robots](https://mlsanity.com/paper/2210.16703/) (2022)

## Abstract (arXiv)

Offloading time-sensitive, computationally intensive tasks-such as advanced learning algorithms for autonomous driving-from vehicles to nearby edge servers, vehicle-to-infrastructure (V2I) systems, or other collaborating vehicles via vehicle-to-vehicle (V2V) communication enhances service efficiency. However, whence traversing the path to the destination, the vehicle's mobility necessitates frequent handovers among the access points (APs) to maintain continuous and uninterrupted wireless connections to maintain the network's Quality of Service (QoS). These frequent handovers subsequently lead to task migrations among the edge servers associated with the respective APs. This paper addresses the joint problem of task migration and access-point handover by proposing a deep reinforcement learning framework based on the Deep Deterministic Policy Gradient (DDPG) algorithm. A joint allocation method of communication and computation of APs is proposed to minimize computational load, service latency, and interruptions with the overarching goal of maximizing QoS. We implement and evaluate our proposed framework on simulated experiments to achieve smooth and seamless task switching among edge servers, ultimately reducing latency.