Design and Implementation of Low-Cost Data Acquisition System for Small and Medium Enterprises (SMEs) of Pakistan
Low-Cost Data Acquisition System for Small and Medium Enterprises (SMEs)
Keywords:Industry 4.0, Arduino, IoT, UDP/TCP, PC Software, Database, MQTT
This paper presents the development of low-cost and robust industrial IoT based data acquisition system primarily focused on domestic manufacturing industries striving to achieve goals and benefits of “Industrial 4.0”. This proposes aims to promote DAQ System integration in traditional manufacturing process of the small and mid-sized industries of Pakistan with limited capacity of investment. Proposed method comprises of Arduino and it’s IoT features for Data Collection, along with a self-developed PC based Centralized Software for Collection of Data, Graphical User Display and Storing collected Data in Local SQL Database. PC based Software replaces requirement of multiple software in case of traditional low-cost DAQ systems, like OPC Software for collecting data from industrial hardware, Java or PHP based any GUI and SQL Data storage. The analysis of work is done with the help of the Message Queue Telemetry Transport (MQTT) protocol. This project will be in further stages evaluated to add features of Supervisory Control, along with Data Acquisition hardware with minimum increase in cost and further upgrading PC Software to add more features of Industry 4.0, as compared to costly commercial solutions available in the market. A machine learning algorithm, k-nearest neighbors algorithm has been used to classify sensitive and non-sensitive data for improvising cloud security. K-Nearest Neighbors is also called KNN algorithm which is supervised machine learning classifier.
G.L. Tortorella, and D. Fettermann. Implementation of industry 4.0 and lean production in brazilian manufacturing companies. International Journal of Production Research 56: 2975-2987 (2018).
L.S. Dalenogare, G.B. Benitez, N.F. Ayala, and A. G. Frank. The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics 204: 383–394 (2018).
S. Wang, J. Wan, D. Li, and C. Zhang. Implementing Smart Factory of Industrie 4.0: An Outlook. International Journal of Distributed Sensor Networks 2016: (2016).
G. Schuh, R. Anderl, J. Gausemeier, M.T. Hompel, and W. Wahlster. Industrie 4.0 Maturity Index Managing the Digital Transformation of Companies. acatech STUDY (2017).
S. Jeschke, C. Brecher, T. Meisen, D. Özdemir, and T. Eschert. Industrial Internet of Things and Cyber Manufacturing Systems. Industrial Internet of Things 3-19 (2017).
G.J. Cheng, L.T. Liu, X.J. Qiang, and Y. Liu. Industry 4.0 Development and Application of Intelligent Manufacturing. International Conference on Information System and Artificial Intelligence (ISAI) 407-410 (2016).
H. Kagermann. Change Through Digitization-Value Creation in the Age of Industry 4.0. Management of Permanent Change, H. and P.A. and R. R. Albach Horst and Meffert, Ed. Wiesbaden: Springer Fachmedien Wiesbaden (23–45) 2015.
M. Rüßmann, M. Lorenz, P. Gerbert, M. Waldner, J. Justus, P. Engel, and M.J. Harnisch. Industry 4.0 The Future of Productivity and Growth in Manufacturing Industries. Economics, History (2016).
T. Stock, and G. Seliger. Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP 40: 536-541 (2016).
A. Lutfi, A.L. Al-Khasawneh, M.A. Almaiah, A. Alsyouf, and M. Alrawad. Business Sustainability of Small and Medium Enterprises during the COVID-19 Pandemic: The Role of AIS Implementation. Sustainability MDPI 14: 5362 (2022).
R.A. Atmoko, R. Riantini, and M.K. Hasin. IoT real time data acquisition using MQTT protocol. Journal of Physics: Conference Series 853: 012003 (2017).
A. Sharma, S. Airan, and D. Shah. Designing C Library for MODBUS-RTU to CANBUS and MODBUS-TCP IOT Converters. Proceedings of the 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India (2021).
K. Yang. B. Zhang, J. Zhang, and J. Zhu. Design of Remote-Control Inverter Based on MQTT Communication Protocol. Proceedings of the 2021 IEEE International Conference on Mechatronics and Automation (ICMA), Takamatsu, Japan (1374-1378) 2021.
H. Gong, J. Li, R. Ni, P. Xiao, and H. Ouyang. The data acquisition and control system based on IoTcan bus. Intelligent Automation & Soft Computing 30: 1049–1062 (2021).
T. Yokotani, and Y. Sasaki. Comparison with HTTP and MQTT on required network resources for IoT. Proceedings of the International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC) (1–6) 2016.
E. Riedel. MQTT protocol for SME foundries: potential as an entry point into industry 4.0, process transparency and sustainability. Procedia CIRP 105: 601-606 (2022).
M. Verhelst, and B. Murmann. Machine Learning at the Edge. NANO-CHIPS 2030, The Frontiers Collection book series (FRONTCOLL) 293-322 (2020).
M. Polese, R. Jana, V. Kounev, K. Zhang and S. Deb. Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks. IEEE Transactions on Mobile Computing
: 3367-3382 (2021).
M. Merenda, C. Porcaro, and D. Iero. Edge Machine Learning for AI-Enabled IoT Devices: A Review. sensors MDPI 20: 2533 (2020).
E. Bertino, and S. Banerjee. Artificial Intelligence at the Edge. A Computing Community Consortium (CCC) (2020).
S. Hymel. What is Edge AI? Machine Learning + IoT. https://www.digikey.com/en/maker/projects/what-is-edge-ai-machine-learning -iot/4f655838138941138aaad62c170827af. [Accessed 26 07 2022].
G. Anadiotis. Machine learning at the edge: TinyML is getting big (2021). https://www.zdnet.com/article/machine-learning-at-the-edge-tinyml-is-getting-big/. [Accessed 26 07 2022].
Z.A. Mohammad, R. Belgaum, S. Musa, M.M. Alam, and M.S. Mazliham. Role of artificial intelligence in cloud computing, IoT and SDN: Reliability and scalability issues. International Journal of Electrical and Computer Engineering (IJECE)11: (2021).
S. Mohammed, W.C. Fang, and C. Ramos. Special issue on “artificial intelligence in cloud computing”. Computing 105: 507-511 (2023).
A. Qayyum, A. Ijaz, M. Usama, W. Iqbal, J. Qadir, Y. Elkhatib, and A. Al-Fuqaha. Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security. Big Data 3: (2020).
G.B. Dasgupta. AI and its Applications in the Cloud strategy. ISEC 2021: 14th Innovations in Software Engineering Conference (formerly known as India Software Engineering Conference) (2021).
U.A. Butt, M. Mehmood, S.B.H. Shah, R. Amin, M.W. Shaukat, D.Y. Suh, and M.J. Piran. A Review of Machine Learning Algorithms for Cloud Computing Security. Electronics, MDPI 9: 1379 (2020).
M.A. Zardari, L.T. Jung, and N. Zakaria. K-NN Classifier for Data Confidentiality in Cloud Computing. 2014 International Conference on Computer and Information Sciences (ICCOINS) (2014).