Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments
Abstract
:1. Introduction
1.1. Industry 4.0 (I4.0)
1.2. Cyber-Physical Systems
1.3. Smart Cyber-Physical Systems
2. Materials and Methods
- Implementation technologies
- Implementation methodology
- Stage one. Implementation technologies
- Lower size, weight, cost, and power consumption;
- Lower data consumption;
- Reduce internet bottlenecks, which translates into less network congestion;
- Consequently, lower costs and lower latency;
- Less bandwidth consumption;
- Increases the security of encrypted data since the information is closer to the end-user, reducing exposure and vulnerability;
- Improved scalability potential is key in a sector with so much growth potential.
- Decreases the reaction time of the system;
- The system works even when there is no internet connection, a key factor when choosing a connected system;
- The risk of blockages or massive failures is considerably reduced since the intelligence is not tied to a single point but is dispersed in several action nuclei;
- Balance and equilibrium increased;
- The possibility to connect sensors and actuators on Input-Output ports.
- Stage two. Operating system and software
- Stage three. Selecting the data set
- Stage four. Training and testing the MLPANN
- -
- Achieving an accuracy above 97% to ensure satisfactory recognition by the network and to be among favorable accuracy results compared to other studies on digit recognition;
- -
- Avoiding overfit and minimizing training time costs;
- -
- Achieving similar performance across the three technologies used in this research.
3. Results
- Data analysis, continuous learning, and adaptation to improve real-time decision-making accuracy and capacity;
- Real-time identification and classification of objects, patterns, and events that enable interaction with the environment and real-time adjustment to face unforeseen scenarios.
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Platform | Hardware | Price (USD) | Energy Consump | Size/ Weight |
---|---|---|---|---|
Laptop | Intel Core i7-3520M processor, 2 cores, 8 GB RAM | 300 | 65 Wh | 13.31″– 9.11″– 1.34″ 4.47 lb |
Cloud computing | Virtual machine with 2 cores, 4 GB of RAM | 9.99 to 50 p/m | - | - |
SCPS | AML-S905XCC, Minicomputer, ARM Cortex-A53 processor, 4 cores, 2 GB DDR3 of RAM | 50 | 1.5 Wh | 4.8″– 3.03″– 1.06″ 0.1124 lb |
Technologies | Accuracy Test | Training and Test Time |
---|---|---|
Laptop | 97.33% | 70 s |
Cloud | 97.31% | 97 s |
SCPS | 97.17% | 378 s |
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Torres-Hernández, M.A.; Escobedo-Barajas, M.H.; Guerrero-Osuna, H.A.; Ibarra-Pérez, T.; Solís-Sánchez, L.O.; Martínez-Blanco, M.d.R. Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments. Sensors 2023, 23, 6935. https://doi.org/10.3390/s23156935
Torres-Hernández MA, Escobedo-Barajas MH, Guerrero-Osuna HA, Ibarra-Pérez T, Solís-Sánchez LO, Martínez-Blanco MdR. Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments. Sensors. 2023; 23(15):6935. https://doi.org/10.3390/s23156935
Chicago/Turabian StyleTorres-Hernández, Mayra A., Miguel H. Escobedo-Barajas, Héctor A. Guerrero-Osuna, Teodoro Ibarra-Pérez, Luis O. Solís-Sánchez, and Ma del R. Martínez-Blanco. 2023. "Performance Analysis of Embedded Multilayer Perceptron Artificial Neural Networks on Smart Cyber-Physical Systems for IoT Environments" Sensors 23, no. 15: 6935. https://doi.org/10.3390/s23156935