AI-Driven Task Scheduling Strategy with Blockchain Integration for Edge Computing

Abstract

In recent times, edge computing has arisen as a highly promising paradigm aimed at facilitating resource-intensive Internet of Things (IoT) applications by offering low-latency services. However, the constrained computational capabilities of the IoT nodes present considerable obstacles when it comes to efficient task-scheduling applications. In this paper, a nature-inspired coati optimization-based energy-aware task scheduling (CO-ETS) approach is proposed to address the challenge of efficiently assigning tasks to available edge devices. The proposed work incorporates a fitness function that effectively enhances task assignment optimization, leading to improved system efficiency, reduced power consumption, and enhanced system reliability. Moreover, we integrate blockchain with AI-driven task scheduling to fortify security, protect user privacy, and optimize edge computing in IoT-based environments. The blockchain-based approach ensures a secure and trusted decentralized identity management and reputation system for IoT edge networks. To validate the effectiveness of the proposed CO-ETS approach, we conduct a comparative analysis against state-of-the-art methods by considering metrics such as makespan, CPU execution time, energy consumption, and mean wait time. The proposed approach offers promising solutions to optimize task allocation, enhance system performance, and ensure secure and privacy-preserving operations in edge computing environments.