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

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 - awar...

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Bibliographic Details
Published in:Alexandria engineering journal Vol. 125; pp. 663 - 676
Main Authors: Wang, Yongmei, Li, Junyong, Tan, Xiaoyun
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2025
Elsevier
Subjects:
Chaos and elite inverse learning
Cooperative optimization
Industrial Internet of Things
Sensory communication
Sparrow search algorithm
Sensory communication
Cooperative optimization
Sparrow search algorithm
Chaos and elite inverse learning
Industrial Internet of Things
ISSN:1110-0168
Online Access:Get full text
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Summary: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.
ISSN:1110-0168
DOI:10.1016/j.aej.2025.04.054