Work

Chaos and elite reverse learning – Enhanced sparrow search algorithm for IIoT sensing communication optimization

Year: 2025

Type: article

Abstract: Sensing, communication, and collaborative optimization are currently hot topics in the Industrial Internet of Things (IIoT) research. This paper addresses minimizing energy consumption in IIoT user terminal devices by modeling energy consumption as an optimization challenge. Initially, a data - aware sharing architecture for IIoT user terminal devices is constructed to reduce device energy consumption. In scenarios involving multiple intelligent terminal devices, collaborative devices, and edge IIoT proxy devices, factors such as user device location stability, local network status, task arrival rate, and queue stability are comprehensively considered. Subsequently, this paper introduces a Chaos and Elite Reverse Learning Sparrow Search Algorithm (CERL-SSA) to solve the established model. The testing experiments use common benchmark functions to verify the superiority of the improved algorithm, and the experimental results show the good performance and effectiveness of the proposed algorithm in IIoT sensing communication and collaborative optimization.

Source: Alexandria Engineering Journal

DOI: 10.1016/j.aej.2025.04.054

Authors: Yongmei Wang, Junyong Li, Xiaoyun Tan

Institutions: Hefei Normal University, Jiangxi Institute of Fashion Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,900