Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing
Abstract
In this article we propose a novel Device-to-Device (D2D) Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage the network-assisted D2D collaboration for computation and communication resource sharing among each other. A key objective of this framework is to achieve energy-efficient collaborative task executions at network-edge for mobile users. Specifically, we first introduce the D2D Crowd system model in details, and then formulate the energy-efficient D2D Crowd task assignment problem by taking into account the necessary constraints. We next propose a graph matching based optimal task assignment policy, and further evaluate its performance through extensive numerical study, which shows a superior performance of more than 50% energy consumption reduction over the case of local task executions. Finally, we also discuss the directions of extending the D2D Crowd framework by taking into variety of application factors.
Cite
@article{arxiv.1703.10340,
title = {Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing},
author = {Xu Chen and Lingjun Pu and Lin Gao and Weigang Wu and Di Wu},
journal= {arXiv preprint arXiv:1703.10340},
year = {2017}
}
Comments
Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, and Di Wu, "Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing," accepted by IEEE Wireless Communications, 2017