Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

Xu Chen, Lingjun Pu, Lin Gao, Weigang Wu, Di Wu

Abstract: In this article we propose a novel D2D Crowd framework for 5G mobile edge computing, where a massive crowd of devices at the network edge leverage network-assisted D2D collaboration for computation and communication resource sharing. A key objective of this framework is to achieve energy-efficient collaborative task executions at the network edge for mobile users. Specifically, we first introduce the D2D Crowd system model in detail, 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 superior performance of more than 50 percent 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 account a variety of application factors.