# EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

arXiv: https://arxiv.org/abs/1709.02582 · HTML: https://mlsanity.com/paper/1709.02582/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2017.

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

- [Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraints](https://mlsanity.com/paper/2106.00490/) (2021)
- [MASS: Mobility-Aware Sensor Scheduling of Cooperative Perception for Connected Automated Driving](https://mlsanity.com/paper/2302.13029/) (2023)
- [Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach](https://mlsanity.com/paper/2112.11818/) (2021)
- [An Ensemble Scheme for Proactive Dominant Data Migration of Pervasive Tasks at the Edge](https://mlsanity.com/paper/2410.09621/) (2024)
- [Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission](https://mlsanity.com/paper/2601.08135/) (2026)

## Abstract (arXiv)

Merging mobile edge computing (MEC) functionality with the dense deployment of base stations (BSs) provides enormous benefits such as a real proximity, low latency access to computing resources. However, the envisioned integration creates many new challenges, among which mobility management (MM) is a critical one. Simply applying existing radio access oriented MM schemes leads to poor performance mainly due to the co-provisioning of radio access and computing services of the MEC-enabled BSs. In this paper, we develop a novel user-centric energy-aware mobility management (EMM) scheme, in order to optimize the delay due to both radio access and computation, under the long-term energy consumption constraint of the user. Based on Lyapunov optimization and multi-armed bandit theories, EMM works in an online fashion without future system state information, and effectively handles the imperfect system state information. Theoretical analysis explicitly takes radio handover and computation migration cost into consideration and proves a bounded deviation on both the delay performance and energy consumption compared to the oracle solution with exact and complete future system information. The proposed algorithm also effectively handles the scenario in which candidate BSs randomly switch on/off during the offloading process of a task. Simulations show that the proposed algorithms can achieve close-to-optimal delay performance while satisfying the user energy consumption constraint.