Game Theoretic Approaches to Massive Data Processing in Wireless Networks

TL;DR

This paper introduces a hierarchical game framework for incentivizing agents in wireless networks to perform data processing tasks, enabling efficient big data handling with real-time requirements.

Contribution

It proposes a scalable, fast-converging game-theoretic model tailored for hierarchical wireless network data processing.

Findings

Framework achieves fast convergence

Ensures scalability for large networks

Facilitates real-time data processing

Abstract

Wireless communication networks are becoming highly virtualized with two-layer hierarchies, in which controllers at the upper layer with tasks to achieve can ask a large number of agents at the lower layer to help realize computation, storage, and transmission functions. Through offloading data processing to the agents, the controllers can accomplish otherwise prohibitive big data processing. Incentive mechanisms are needed for the agents to perform the controllers' tasks in order to satisfy the corresponding objectives of controllers and agents. In this article, a hierarchical game framework with fast convergence and scalability is proposed to meet the demand for real-time processing for such situations. Possible future research directions in this emerging area are also discussed.