Wireless Sensor Network for Monitoring Applied Physical Variables

This article reports an array of wireless sensors connected to a network to monitor physical variables; environment temperature, soil humidity and environment humidity applied to the internet of things. The use of the Wireless Sensor Network (WSN) has a promising future due to current technical advances and its almost unlimited applications. In this paper WSN topologies, measurement methodology, sensor distribution and visualization of recorded data are proposed based on the monitoring area, the communication protocol established by Wi-Fi and the readings of the environmental temperature (ET), environmental humidity (EH) and ground humidity (GH) are recorded and displayed on the web. In this experimental monitoring of environmental physical conditions, a record is made every hour during a period of 24 hours. Some of the potential applications for this remote measurement technique are the green technologies, industrial processes, internet of things, among others

Smart Sensors for Application in IIoT / Industry 4.0 / Digitalization

Napredni „smart“ senzori imaju široku primjernu u Internetu stvari (engl. Internet of Things - IoT), industrijskom internetu stvari i digitalizaciji. Smart senzori imaju glavnu ulogu u ovim konceptima. Za razliku od tradicionalnih senzora, smart senzori imaju više funkcija i mogućnost bežičnog povezivanja. Zahvaljujući razvoju proizvodnje mikro elektroničkih komponenti i smanjenju cijene napredni senzori imaju široku primjenu. Internet Stvari/Industrijski IoT predstavljaju mrežu naprednih uređaja koji su povezani i imaju mogućnost komunikacije. Koncept IoT, IIoT, digitalizacija, napredni senzori i mrežne tehnologije predstavljaju tehnološku budućnost svijeta, iako postoji puno neobjašnjenih pitanja u vezi ovih koncepata kao što su sigurnost, privatnost, upravljanje i analiza podataka.Advanced „smart“ sensors have a wide range of application in the Internet of Things (IoT), Industrial Internet of Things and Digitalization. Smart sensors have a major role in these concepts. Unlike traditional sensors, smart sensors have more functionality and wireless connectivity capability. Thanks to the development of the production of micro eletromechanical systems (MEMS) and reduction of prices smart sensors have wide application. Internet of Things/Industrial IoT are a network of advances devices that are connceted and have the ability to communicate. The concept of IoT, IIoT, Digitalization, smart sensors and network technologies are the technological future of the world, although there are many unanswered questions about these concepts such as security, privacy, dana management and analysis

Cyber-physical machine tool - The era of machine tool 4.0

In the past five years, several industrial initiatives such as “Industry 4.0”, “Industrial Internet of Things”, “Factories of the Future” and “Made in China 2025”, have been announced by different governments and industrial leaders. These initiatives lead to an urgent need to advance current manufacturing systems into a high level of intelligence and autonomy. As the main component of any manufacturing system, machine tools have evolved from manually operated machines into the current computer numerically controlled (CNC) machine tools. It is predicted that current CNC machine tools are not intelligent and autonomous enough to support the smart manufacturing systems envisioned by the aforementioned initiatives. Inspired by recent advances in ICT such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), this paper proposes a new generation of machine tools, i.e. Machine Tool 4.0, as a future development trend of machine tools. Machine Tool 4.0, otherwise known as Cyber-Physical Machine Tool (CPMT), is the integration of machine tool, machining processes, computation and networking, where embedded computers and networks can monitor and control the machining processes, with feedback loops in which machining processes can affect computations and vice versa. The main components and functions of a CPMT are presented. The key research issues related to the development of CPMT are identified and discussed. A three-layer CPMT-centered Cyber Physical Production System (CPPS) is proposed to illustrate both the vertical integration of various smart systems at different hierarchical levels and the horizontal integration of field-level manufacturing facilities and resources

Recent advances in IoT, AI, and national technology resilience

Internet of Things (IoT) and Artificial Intelligence (AI) are the critical enablers of the Industrial Revolution 4.0. IoT can be used in many applications that require precision, such as agriculture, industrial automation, education, automotive, and smart cities, to name a few. In other words, IoT is a powerful technology that can solve various business problems. Nevertheless, its integration with AI can help to take automation to the next level. This talk aims to discuss the recent advances in IoT, edge computing, and its applications. First, the IoT and edge commercial adoption survey 2021 will be highlighted. Then, the IoT framework will be introduced to solve a complex problem, including Things, Connect, Collect, Learn, and Do. Especially, the Learn part is very much related to AI. Then, some applications using IoT and edge computing will be presented. Finally, national technology resilience is now a necessity rather than necessary due to the current world situation. Therefore, future directions to enhance national technology resilience will be elaborated

Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks

[EN] The Industrial Internet of Things (IIoT) is having an ever greater impact on industrial processes and the manufacturing sector, due the capabilities of massive data collection and interoperability with plant processes, key elements that are focused on the implementation of Industry 4.0. Wireless Sensor Networks (WSN) are one of the enabling technologies of the IIoT, due its self-configuration and self-repair capabilities to deploy ad-hoc networks. High levels of robustness and reliability, which are necessary in industrial environments, can be achieved by using the Time-Slotted Channel Hopping (TSCH) medium access the mechanism of the IEEE 802.15.4e protocol, penalizing other features, such as network connection and formation times, given that a new node does not know, a priori, the scheduling used by the network. This article proposes a new beacon advertising approach for a fast synchronization for networks under the TSCH-Medium Access Control (MAC) layer and Routing Protocol for Low-Power and Lossy Networks (RPL). This new method makes it possible to speed up the connection times of new nodes in an opportunistic way, while reducing the consumption and advertising traffic generated by the network.This work has been supported by the SCOTT project (Secure COnnected Trustable Things) (www.scottproject.eu), which has received funding from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No. 737422. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme, and from Austria, Spain, Finland, Ireland, Sweden, Germany, Poland, Portugal, Netherlands, Belgium and Norway. It has also been funded by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial - IVACE", and by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00), which is partially funded by AEI, FEDER and EU.Vera-Pérez, J.; Todoli Ferrandis, D.; Silvestre-Blanes, J.; Sempere Paya, VM. (2019). Bell-X, An Opportunistic Time Synchronization Mechanism for Scheduled Wireless Sensor Networks. Sensors. 19(19):1-22. https://doi.org/10.3390/s19194128S1221919Vitturi, S., Zunino, C., & Sauter, T. (2019). Industrial Communication Systems and Their Future Challenges: Next-Generation Ethernet, IIoT, and 5G. Proceedings of the IEEE, 107(6), 944-961. doi:10.1109/jproc.2019.2913443Candell, R., Kashef, M., Liu, Y., Lee, K. B., & Foufou, S. (2018). Industrial Wireless Systems Guidelines: Practical Considerations and Deployment Life Cycle. IEEE Industrial Electronics Magazine, 12(4), 6-17. doi:10.1109/mie.2018.2873820Brandt, A., Hui, J., Kelsey, R., Levis, P., Pister, K., … Struik, R. (2012). RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks. doi:10.17487/rfc6550Vera-Pérez, J., Todolí-Ferrandis, D., Santonja-Climent, S., Silvestre-Blanes, J., & Sempere-Payá, V. (2018). A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks. Sensors, 18(10), 3556. doi:10.3390/s18103556Pister, K., & Watteyne, T. (2017). Minimal IPv6 over the TSCH Mode of IEEE 802.15.4e (6TiSCH) Configuration. doi:10.17487/rfc8180Levis, P., Clausen, T., Hui, J., Gnawali, O., & Ko, J. (2011). The Trickle Algorithm. doi:10.17487/rfc6206Contiki: The Open Source OS for the Internet of Things: Official Website www.contiki-os.orgStanislowski, D., Vilajosana, X., Wang, Q., Watteyne, T., & Pister, K. S. J. (2014). Adaptive Synchronization in IEEE802.15.4e Networks. IEEE Transactions on Industrial Informatics, 10(1), 795-802. doi:10.1109/tii.2013.2255062Chang, T., Watteyne, T., Pister, K., & Wang, Q. (2015). Adaptive synchronization in multi-hop TSCH networks. Computer Networks, 76, 165-176. doi:10.1016/j.comnet.2014.11.003Palattella, M., & Grieco, L. (2015). Using IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the Internet of Things (IoT): Problem Statement. doi:10.17487/rfc7554Vogli, E., Ribezzo, G., Grieco, L. A., & Boggia, G. (2018). Fast network joining algorithms in industrial IEEE 802.15.4 deployments. Ad Hoc Networks, 69, 65-75. doi:10.1016/j.adhoc.2017.10.013Duy, T. P., Dinh, T., & Kim, Y. (2016). A rapid joining scheme based on fuzzy logic for highly dynamic IEEE 802.15.4e time-slotted channel hopping networks. International Journal of Distributed Sensor Networks, 12(8), 155014771665942. doi:10.1177/1550147716659424Khoufi, I., Minet, P., & Rmili, B. (2019). Beacon advertising in an IEEE 802.15.4e TSCH network for space launch vehicles. Acta Astronautica, 158, 76-88. doi:10.1016/j.actaastro.2018.07.021Karalis, A., Zorbas, D., & Douligeris, C. (2019). Collision-Free Advertisement Scheduling for IEEE 802.15.4-TSCH Networks. Sensors, 19(8), 1789. doi:10.3390/s19081789Vallati, C., Brienza, S., Anastasi, G., & Das, S. K. (2019). Improving Network Formation in 6TiSCH Networks. IEEE Transactions on Mobile Computing, 18(1), 98-110. doi:10.1109/tmc.2018.2828835De Guglielmo, D., Anastasi, G., & Seghetti, A. (2014). From IEEE 802.15.4 to IEEE 802.15.4e: A Step Towards the Internet of Things. Advances onto the Internet of Things, 135-152. doi:10.1007/978-3-319-03992-3_1

Embedded Edge Intelligent Processing for End-To-End Predictive Maintenance in Industrial Applications

This article advances innovative approaches to the design and implementation of an embedded intelligent system for predictive maintenance (PdM) in industrial applications. It is based on the integration of advanced artificial intelligence (AI) techniques into micro-edge Industrial Internet of Things (IIoT) devices running on Armr Cortexr microcontrollers (MCUs) and addresses the impact of a) adapting to the constraints of MCUs, b) analysing sensor patterns in the time and frequency domain and c) optimising the AI model architecture and hyperparameter tuning, stressing that hardware–software co-exploration is the key ingredient to converting micro-edge IIoT devices into intelligent PdM systems. Moreover, this article highlights the importance of end-to-end AI development solutions by employing existing frameworks and inference engines that permit the integration of complex AI mechanisms within MCUs, such as NanoEdgeTM AI Studio, Edge Impulse and STM32 Cube.AI. Both quantitative and qualitative insights are presented in complementary workflows with different design and learning components, as well as in the backend flow for deployment onto IIoT devices with a common inference platform based on Armr Cortexr-M-based MCUs. The use case is an n-class classification based on the vibration of generic motor rotating equipment. The results have been used to lay down the foundation of the PdM strategy, which will be included in future work insights derived from anomaly detection, regression and forecasting applications.publishedVersio

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

[EN] Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.Kumar, K.; Kumar, S.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N. (2017). Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities. Energies. 10(12):1-40. https://doi.org/10.3390/en10122073S1401012Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. 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