# MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks

arXiv: https://arxiv.org/abs/2210.15111 · HTML: https://mlsanity.com/paper/2210.15111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 11 of 11)

- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networks](https://mlsanity.com/paper/2108.09103/) (2021)
- [Federated mmWave Beam Selection Utilizing LIDAR Data](https://mlsanity.com/paper/2102.02802/) (2021)
- [Edge Learning with Timeliness Constraints: Challenges and Solutions](https://mlsanity.com/paper/2009.11065/) (2020)
- [Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm](https://mlsanity.com/paper/2002.08833/) (2020)
- [Exploiting Moving Intelligence: Delay-Optimized Computation Offloading in Vehicular Fog Networks](https://mlsanity.com/paper/1902.09401/) (2019)
- [V2X in 3GPP Standardization: NR Sidelink in Rel-16 and Beyond](https://mlsanity.com/paper/2104.11135/) (2021)
- [Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking](https://mlsanity.com/paper/2108.11879/) (2021)

## Built on by (most cited first, 5 of 5)

- [Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks](https://mlsanity.com/paper/2401.09656/) (2024)
- [MOB-FL: Mobility-Aware Federated Learning for Intelligent Connected Vehicles](https://mlsanity.com/paper/2212.03519/) (2022)
- [Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning](https://mlsanity.com/paper/2406.17470/) (2024)
- [Data-Heterogeneous Hierarchical Federated Learning with Mobility](https://mlsanity.com/paper/2306.10692/) (2023)
- [Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification](https://mlsanity.com/paper/2506.07328/) (2025)

## Abstract (arXiv)

Edge intelligence is an emerging paradigm for real-time training and inference at the wireless edge, thus enabling mission-critical applications. Accordingly, base stations (BSs) and edge servers (ESs) need to be densely deployed, leading to huge deployment and operation costs, in particular the energy costs. In this article, we propose a new framework called Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing, communication, computing, and self-powering capabilities of intelligent connected vehicles for the smart and green 6G networks. Specifically, the operators can incorporate infrastructural vehicles as movable BSs or ESs, and schedule them in a more flexible way to align with the communication and computation traffic fluctuations. Meanwhile, the remaining compute resources of opportunistic vehicles are exploited for edge training and inference, where mobility can further enhance edge intelligence by bringing more compute resources, communication opportunities, and diverse data. In this way, the deployment and operation costs are spread over the vastly available vehicles, so that the edge intelligence is realized cost-effectively and sustainably. Furthermore, these vehicles can be either powered by renewable energy to reduce carbon emissions, or charged more flexibly during off-peak hours to cut electricity bills.