A proof of contribution in blockchain using game theoretical deep learning model

TL;DR

This paper introduces a game-theoretic deep learning model combined with blockchain technology to improve resource allocation and reduce latency in edge computing for smart city services.

Contribution

It presents a novel decentralized resource management framework using a deep learning model and blockchain to incentivize service providers and optimize task scheduling.

Findings

Reduces latency by 584% compared to existing methods.

Enables decentralized, credible resource trading among service providers.

Improves efficiency of edge resource utilization.

Abstract

Building elastic and scalable edge resources is an inevitable prerequisite for providing platform-based smart city services. Smart city services are delivered through edge computing to provide low-latency applications. However, edge computing has always faced the challenge of limited resources. A single edge device cannot undertake the various intelligent computations in a smart city, and the large-scale deployment of edge devices from different service providers to build an edge resource platform has become a necessity. Selecting computing power from different service providers is a game-theoretic problem. To incentivize service providers to actively contribute their valuable resources and provide low-latency collaborative computing power, we introduce a game-theoretic deep learning model to reach a consensus among service providers on task scheduling and resource provisioning. Traditional centralized resource management approaches are inefficient and lack credibility, while the introduction of blockchain technology can enable decentralized resource trading and scheduling. We propose a contribution-based proof mechanism to provide the low-latency service of edge computing. The deep learning model consists of dual encoders and a single decoder, where the GNN (Graph Neural Network) encoder processes structured decision action data, and the RNN (Recurrent Neural Network) encoder handles time-series task scheduling data. Extensive experiments have demonstrated that our model reduces latency by 584% compared to the state-of-the-art.