Open Access

ARTICLE

Efficient Data Aggregation and Message Transmission for Information Processing Model in the CPS-WSN

Chao-Hsien Hsieh¹, Qingqing Yang^2,*, Dehong Kong², Fengya Xu², Hongmei Wang²

1 College of Engineering, Xi’an International University, Xi’an, 710077, China
2 School of Cyber Science and Engineering, Qufu Normal University, Qufu, 273165, China

* Corresponding Author: Qingqing Yang. Email:

Computers, Materials & Continua 2025, 82(2), 2869-2891. https://doi.org/10.32604/cmc.2024.058122

Received 04 September 2024; Accepted 24 October 2024; Issue published 17 February 2025

Abstract

The Cyber-Physical Systems (CPS) supported by Wireless Sensor Networks (WSN) helps factories collect data and achieve seamless communication between physical and virtual components. Sensor nodes are energy-constrained devices. Their energy consumption is typically correlated with the amount of data collection. The purpose of data aggregation is to reduce data transmission, lower energy consumption, and reduce network congestion. For large-scale WSN, data aggregation can greatly improve network efficiency. However, as many heterogeneous data is poured into a specific area at the same time, it sometimes causes data loss and then results in incompleteness and irregularity of production data. This paper proposes an information processing model that encompasses the Energy-Conserving Data Aggregation Algorithm (ECDA) and the Efficient Message Reception Algorithm (EMRA). ECDA is divided into two stages, Energy conservation based on the global cost and Data aggregation based on ant colony optimization. The EMRA comprises the Polling Message Reception Algorithm (PMRA), the Shortest Time Message Reception Algorithm (STMRA), and the Specific Condition Message Reception Algorithm (SCMRA). These algorithms are not only available for the regularity and directionality of sensor information transmission, but also satisfy the different requirements in small factory environments. To compare with the recent HPSO-ILEACH and E-PEGASIS, DCDA can effectively reduce energy consumption. Experimental results show that STMRA consumes 1.3 times the time of SCMRA. Both optimization algorithms exhibit higher time efficiency than PMRA. Furthermore, this paper also evaluates these three algorithms using AHP.

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Keywords

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