A Multi-Strategy Ivy Algorithm for Low-Latency and Reliable Task Scheduling in Industrial Internet of Things

Abstract

The Industrial Internet of Things (IIoT) facilitates the large-scale interconnection of heterogeneous devices and processes. Nevertheless, scheduling hundreds of latency-sensitive, resource-intensive tasks across distributed systems efficiently remains a challenge. State-of-the-art swarm intelligence and evolutionary approaches often suffer from premature convergence, high computational costs, and unreliable output in dynamic network environments. In this study, we propose a Multi-Strategy Ivy Algorithm (MS-IA) for low-latency and reliable task scheduling in IIoT environments. MS-IA adopts the adaptive growth and propagation behavior of ivy plants, which consists of four strategic extensions: adaptive perturbation, adaptable growth velocity, the fish-aggregation device concept, and hybridization with differential evolution. By incorporating these mechanisms into the IIoT task scheduling model, MS-IA maintains a proper exploration-exploitation balance, thereby reducing makespan and power consumption while maximizing system reliability and throughput. Through comprehensive testing of multi-scale IIoT workloads, it has been certified that MS-IA demonstrably outperforms state-of-the-art scheduling algorithms.