CBI4.0: A Cross-Layer Approach for Big Data Gathering for Active Monitoring and Maintenance in the Manufacturing Industry 4.0
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Date
2021, 2021
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Open Access Color
Green Open Access
No
OpenAIRE Downloads
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Publicly Funded
No
Abstract
Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.
Description
Phd, Muhammad Faheem,/0000-0003-4628-4486; Raza, Basit/0000-0001-6711-2363; Butt, Rizwan Aslam/0000-0002-4784-0918
Keywords
Internet of Things, Industry 4.0, Big Data, Multi-Channel Communication, Wireless Sensor Network, 670, QA75 Electronic computers. Computer science
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
34
Source
Journal of Industrial Information Integration
Volume
24
Issue
Start Page
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CrossRef : 33
Scopus : 48
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Mendeley Readers : 95
SCOPUS™ Citations
52
checked on Mar 06, 2026
Web of Science™ Citations
43
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Page Views
382
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Downloads
10
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Google Scholar™

OpenAlex FWCI
7.2648
Sustainable Development Goals
9
INDUSTRY, INNOVATION AND INFRASTRUCTURE


