Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing

TL;DR

This paper introduces a D2D Crowd framework for 5G mobile edge computing that enables massive device collaboration to significantly reduce energy consumption during task execution.

Contribution

It presents a novel system model and an optimal task assignment policy based on graph matching for energy-efficient D2D collaboration at the network edge.

Findings

Achieves over 50% energy reduction compared to local execution.

Proposes a graph matching based optimal task assignment policy.

Provides extensive numerical evaluation of the framework.

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.