1–21.17 17 Tzoulis, I. and Andreopoulou, Z. (2013). Emerging traceability technologies as a tool for quality wood trade. Procedia Technology 8 (1): 606–611.
18 18 Agrawal, T.K., Koehl, L., and Campagne, C. (2018). A secured tag for implementation of traceability in textile and clothing supply chain. The International Journal of Advanced Manufacturing Technology 99 (1): 2563–2577.
19 19 Chen, R.‐S., Chen, C.‐C., Yeh, K.C. et al. (2008). Using RFID technology in food produce traceability. WSEAS Transactions on Information Science and Applications 5 (11): 1551–1560.
20 20 Kelepouris, T., Pramatari, K., and Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. 107 (2): 183–200.
21 21 Sethi, P., Sarangi, S., and R. (2017). Internet of Things: architectures, protocols, and applications. Journal of Electrical and Computer Engineering 2017 (9324035): 1–25.
22 22 Ke, C.K., Wu, M.Y., Chan, Y.W., and Lu, K.C. (2018). Developing a BLE Beacon‐based location system using location fingerprint positioning for smart home power management. Energies 11 (3464): 1–18.
23 23 Lin, Y.‐W. and Lin, C.‐Y. (2018). An interactive real‐time locating system based on Bluetooth low‐energy beacon network. Sensors 18 (1637): 1–17.
24 24 Triantafyllou, A., Sarigiannidis, P., and Lagkas, T.D. (2018). Network protocols, schemes, and mechanisms for Internet of Things (IoT): features, open challenges, and trends. Wireless Communications and Mobile Computing https://doi.org/10.1155/2018/5349894.
25 25 Cilfone, A., Davoli, L., Belli, L., and Ferrari, G. (2019). Wireless mesh networking: an IoT‐oriented perspective survey on relevant technologies. Future Internet 11 (99): 1–35.
26 26 Froiz‐Míguez, I., Fernández‐Caramés, T.M., Fraga‐Lamas, P., and Castedo, L. (2018). Design, implementation and practical evaluation of an IoT home automation system for fog computing applications based on MQTT and ZigBee‐WiFi sensor nodes. Sensors 18 (2660): 1–42.
27 27 Amelia, A., Julham, Sundawa, B.V. et al. (2017). Implementation of the RS232 communication trainer using computers and the ATMEGA microcontroller for interface engineering courses. Journal of Physics: Conference Series 890 (012095): 1–6.
28 28 Dey, M. (2012). Comparision of data transfer protocols over USB. International Journal of Engineering Research & Technology 1 (9): 1–6.
29 29 Riberio, F.M., Costa, T.S., Baratella, A. et al. (2014). Comparative analysis of industrial network profinet, ethernet/IP, and HSE. International Journal of Innovative Computing, Information and Control 10 (5): 1931–1945.
30 30 Jaloudi, S. (2019). Communication protocols of an industrial Internet of Things environment: a comparative study. Future Internet 11 (66): 1–18.
31 31 Savaglio, C., Ganzha, M., Paprzycki, M. et al. (2019). Agent‐based Internet of Things: state‐of‐the‐art and research challenges. Future Generation Computer Systems 102 (1): 1038–1053.
32 32 Massaro, A., Calicchio, A., Maritati, V. et al. (2018). A case study of innovation of an information communication system and upgrade of the knowledge base in industry by ESB, artificial intelligence, and big data system integration. International Journal of Artificial Intelligence and Applications (IJAIA) 9 (5): 27–43.
33 33 Burhan, M., Asif Rehman, R., Khan, B., and Kim, B.‐S. (2018). IoT elements, layered architectures and security issues: a comprehensive survey. Sensors 18 (2796): 1–37.
34 34 Arsan, T., Günay, F., and Kaya, E. (2014). Implementation of application for huge data file transfer. International Journal of Wireless & Mobile Networks (IJWMN) 6 (4): 27–46.
35 35 Riabov, V.V. and SMTP (Simple Mail Transfer Protocol) (2007). The Handbook of Computer Networks, Volume 2, LANs, MANs, WANs, the Internet, and Global, Cellular, and Wireless Networks (ed. H. Bidgoli), 388–406. Hoboken, NJ: Wiley.
36 36 Nguyen, T.S. and Huynh, T.‐H. (eds.) (2015). Design and implementation of Modbus slave based on ARM Platform and FreeRTOS environment. Proceedings of International Conference on Advanced Technologies for Communications (ATC), Ho Chi Minh City, Vietnam (14–16 October 2015). Piscataway, NJ: IEEE.
37 37 Rajinder, S. and Satish, K. (2016). An overview of world wide web protocol (Hypertext Transfer Protocol and Hypertext Transfer Protocol Secure). International Journal of Advanced Research in Computer Science and Software Engineering 6 (5): 396–399.
38 38 Ansari, D.B., Rehman, A.U., and Mughal, R.A. (2018). Internet of Things (IoT) protocols: a brief exploration of MQTT and CoAP. International Journal of Computer Applications 179 (27): 9–14.
39 39 Tukade, T.M. and Banakar, R.M. (2018). Data transfer protocols in IoT – an overview. International Journal of Pure and Applied Mathematics 118 (16): 121–138.
40 40 Kastner, W., Neugschwandtner, G., and Kogler, M. (eds.) (2006). An open approach to Eib/Knx software development. Proceedings of 6th IFAC International Conference, Puebla, Mexico (14–25 November 2005). Elsevier Ltd.
41 41 Massaro, A., Manfredonia, I., Galiano, A., and Contuzzi, N. (eds.) (2019). Inline image vision Technique for tires industry 4.0: quality and defect monitoring in tires assembly. Proceedings of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.
42 42 Sathya, R. and Abraham, A. (2013). Comparison of supervised and unsupervised learning algorithms for pattern classification. International Journal of Advanced Research in Artificial Intelligence 2 (2): 34–38.
43 43 Massaro, A., Manfredonia, I., Galiano, A. et al. (2019). Sensing and quality monitoring facilities designed for pasta industry including traceability, image vision and predictive maintenance. Proceeding of 2019 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.
44 44 Massaro, A., Manfredonia, I., Galiano, A., and Xhaysa, B. (2019). Advanced process defect monitoring model and prediction improvement by artificial neural network in kitchen manufacturing industry: a case of study. Proceeding of IEEE International Workshop on Metrology for Industry 4.0 and IoT, Naples, Italy (4–6 June 2019). Piscataway, NJ: IEEE.
45 45 Massaro, A., Vitti, V., and Galiano, A. (2018). Automatic image processing engine oriented on quality control of electronic boards. Signal & Image Processing: An International Journal (SIPIJ) 9 (2): 1–14.
46 46 Kiran, B.R., Thomas, D.M., and Parakkal, R. (2018). An overview of deep learning based methods for unsupervised and semi‐supervised anomaly detection in videos. Journal of Imaging 4 (36): 1–25.
47 47 Xu, X., Zheng, H., Guo, Z. et al. (2019). SDD‐CNN: small data‐driven convolution neural networks for subtle roller defect inspection. Applied Sciences 9 (1364): 1–16.
48 48 Perez, H., Tah, J.H.M., and Mosavi, A. (2019). Deep learning for detecting building defects using convolutional neural networks. Sensors 19 (3556): 1–22.
49 49 Lin, C.‐S., Huang, Y.‐C., Chen, S.‐H. et al. (2018). The application of deep learning and image processing technology in laser positioning. Applied Sciences 8 (1542): 1–13.
50 50 Iskra, P. and Hernàndez, R.E. (2012). Toward a process monitoring of CNC wood router. Sensor selection and surface roughness prediction. Wood Science and Technology 46 (1): 115–128.
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