An Incentive-Based Mechanism for Volunteer Computing using Blockchain

TL;DR

This paper proposes a blockchain-based incentive mechanism for volunteer computing in IoT environments, enhancing secure resource sharing, service provisioning, and balanced participation through reinforcement learning.

Contribution

It introduces a novel blockchain-enabled resource sharing framework with incentive mechanisms and reinforcement learning for efficient volunteer computing in IoT and tactile internet contexts.

Findings

High reward distribution achieved

Increased blockchain formations observed

Reduced delays and balanced resource usage

Abstract

The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.