TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks

[EN] Wireless sensor networks (WSNs) play a key role in the ecosystem of the Industrial Internet of Things (IIoT) and the definition of today's Industry 4.0. These WSNs have the ability to sensor large amounts of data, thanks to their easy scalability. WSNs allow the deployment of a large number of self-configuring nodes and the ability to automatically reorganize in case of any change in the topology. This huge sensorization capacity, together with its interoperability with IP-based networks, allows the systems of Industry 4.0 to be equipped with a powerful tool with which to digitalize a huge amount of variables in the different industrial processes. The IEEE 802.15.4e standard, together with the access mechanism to the Time Slotted Channel Hopping medium (TSCH) and the dynamic Routing Protocol for Low-Power and Lossy Networks (RPL), allow deployment of networks with the high levels of robustness and reliability necessary in industrial scenarios. However, these configurations have some disadvantages in the deployment and synchronization phases of the networks, since the time it takes to synchronize the nodes is penalized compared to other solutions in which access to the medium is done randomly and without channel hopping. This article proposes an analytical model to characterize the behavior of this type of network, based on TSCH and RPL during the phases of deployment along with synchronization and connection to the RPL network. Through this model, validated by simulation and real tests, it is possible to parameterize different configurations of a WSN network based on TSCH and RPL.This work has been supported 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.; Silvestre-Blanes, J.; Sempere Paya, VM. (2021). TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks. Sensors. 21(11):1-17. https://doi.org/10.3390/s21113904117211

Smart Sensor Architectures for Multimedia Sensing in IoMT

[EN] Today, a wide range of developments and paradigms require the use of embedded systems characterized by restrictions on their computing capacity, consumption, cost, and network connection. The evolution of the Internet of Things (IoT) towards Industrial IoT (IIoT) or the Internet of Multimedia Things (IoMT), its impact within the 4.0 industry, the evolution of cloud computing towards edge or fog computing, also called near-sensor computing, or the increase in the use of embedded vision, are current examples of this trend. One of the most common methods of reducing energy consumption is the use of processor frequency scaling, based on a particular policy. The algorithms to define this policy are intended to obtain good responses to the workloads that occur in smarthphones. There has been no study that allows a correct definition of these algorithms for workloads such as those expected in the above scenarios. This paper presents a method to determine the operating parameters of the dynamic governor algorithm called Interactive, which offers significant improvements in power consumption, without reducing the performance of the application. These improvements depend on the load that the system has to support, so the results are evaluated against three different loads, from higher to lower, showing improvements ranging from 62% to 26%.This work has been supported 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.Silvestre-Blanes, J.; Sempere Paya, VM.; Albero Albero, T. (2020). Smart Sensor Architectures for Multimedia Sensing in IoMT. Sensors. 20(5):1-16. https://doi.org/10.3390/s20051400S116205Bangemann, T., Riedl, M., Thron, M., & Diedrich, C. (2016). Integration of Classical Components Into Industrial Cyber–Physical Systems. Proceedings of the IEEE, 104(5), 947-959. doi:10.1109/jproc.2015.2510981Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017). The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0. IEEE Industrial Electronics Magazine, 11(1), 17-27. doi:10.1109/mie.2017.2649104Salehi, M., & Ejlali, A. (2015). A Hardware Platform for Evaluating Low-Energy Multiprocessor Embedded Systems Based on COTS Devices. IEEE Transactions on Industrial Electronics, 62(2), 1262-1269. doi:10.1109/tie.2014.2352215Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., & Mahmood, W. (2015). Internet of multimedia things: Vision and challenges. Ad Hoc Networks, 33, 87-111. doi:10.1016/j.adhoc.2015.04.006Jridi, M., Chapel, T., Dorez, V., Le Bougeant, G., & Le Botlan, A. (2018). SoC-Based Edge Computing Gateway in the Context of the Internet of Multimedia Things: Experimental Platform. Journal of Low Power Electronics and Applications, 8(1), 1. doi:10.3390/jlpea8010001Memos, V. A., Psannis, K. E., Ishibashi, Y., Kim, B.-G., & Gupta, B. B. (2018). An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework. Future Generation Computer Systems, 83, 619-628. doi:10.1016/j.future.2017.04.039Chianese, A., Piccialli, F., & Riccio, G. (2015). Designing a Smart Multisensor Framework Based on Beaglebone Black Board. Lecture Notes in Electrical Engineering, 391-397. doi:10.1007/978-3-662-45402-2_60Wang, W., Wang, Q., & Sohraby, K. (2016). Multimedia Sensing as a Service (MSaaS): Exploring Resource Saving Potentials of at Cloud-Edge IoTs and Fogs. IEEE Internet of Things Journal, 1-1. doi:10.1109/jiot.2016.2578722Munir, A., Gordon-Ross, A., & Ranka, S. (2014). Multi-Core Embedded Wireless Sensor Networks: Architecture and Applications. IEEE Transactions on Parallel and Distributed Systems, 25(6), 1553-1562. doi:10.1109/tpds.2013.219Baali, H., Djelouat, H., Amira, A., & Bensaali, F. (2018). Empowering Technology Enabled Care Using IoT and Smart Devices: A Review. IEEE Sensors Journal, 18(5), 1790-1809. doi:10.1109/jsen.2017.2786301Kim, Y. G., Kong, J., & Chung, S. W. (2018). A Survey on Recent OS-Level Energy Management Techniques for Mobile Processing Units. IEEE Transactions on Parallel and Distributed Systems, 29(10), 2388-2401. doi:10.1109/tpds.2018.2822683Chaib Draa, I., Niar, S., Tayeb, J., Grislin, E., & Desertot, M. (2016). Sensing user context and habits for run-time energy optimization. EURASIP Journal on Embedded Systems, 2017(1). doi:10.1186/s13639-016-0036-8Chen, Y.-L., Chang, M.-F., Yu, C.-W., Chen, X.-Z., & Liang, W.-Y. (2018). Learning-Directed Dynamic Voltage and Frequency Scaling Scheme with Adjustable Performance for Single-Core and Multi-Core Embedded and Mobile Systems. Sensors, 18(9), 3068. doi:10.3390/s18093068Tamilselvan, K., & Thangaraj, P. (2020). Pods – A novel intelligent energy efficient and dynamic frequency scalings for multi-core embedded architectures in an IoT environment. Microprocessors and Microsystems, 72, 102907. doi:10.1016/j.micpro.2019.10290

Dynamic autonomous set-up of relays in Bluetooth mesh

BLE-based mesh networks are based on a simple flooding algorithm with some mechanisms to reduce network saturation, called managed flooding. The operating parameters of the network establish its performance, but in an industrial environment the operating conditions are not permanent, so a system that can adjust to these changes is necessary. A global decision system is not valid since each part of the network may have different properties. An autonomous system that does not introduce an overhead of message exchange is necessary for its operation. This paper proposes an algorithm based on the information provided by a single control message exchange that allows each node to autonomously select its operating parameters to improve the quality of links with neighbouring nodes and thus improve the overall performance of the network

Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios

[EN] Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy.The research leading to these results received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP)". It was also partially supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00).Todoli Ferrandis, D.; Silvestre-Blanes, J.; Sempere Paya, VM.; Planes, A. (2020). Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios. Applied Sciences. 10(22):1-17. https://doi.org/10.3390/app10227964S1171022Ayoub, W., Samhat, A. E., Nouvel, F., Mroue, M., & Prevotet, J.-C. (2019). Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs Standards and Supported Mobility. IEEE Communications Surveys & Tutorials, 21(2), 1561-1581. doi:10.1109/comst.2018.2877382Zero Defect Manufacturing Platformhttps://www.zdmp.eu/Finnegan, J., Brown, S., & Farrell, R. (2018). Evaluating the Scalability of LoRaWAN Gateways for Class B Communication in ns-3. 2018 IEEE Conference on Standards for Communications and Networking (CSCN). doi:10.1109/cscn.2018.8581759Luvisotto, M., Tramarin, F., Vangelista, L., & Vitturi, S. (2018). On the Use of LoRaWAN for Indoor Industrial IoT Applications. Wireless Communications and Mobile Computing, 2018, 1-11. doi:10.1155/2018/3982646Kim, S., & Yoo, Y. (2018). Contention-Aware Adaptive Data Rate for Throughput Optimization in LoRaWAN. Sensors, 18(6), 1716. doi:10.3390/s18061716Ta, D.-T., Khawam, K., Lahoud, S., Adjih, C., & Martin, S. (2019). LoRa-MAB: A Flexible Simulator for Decentralized Learning Resource Allocation in IoT Networks. 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC). doi:10.23919/wmnc.2019.8881393Abdelfadeel, K. Q., Cionca, V., & Pesch, D. (2018). Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN. 2018 IEEE 19th International Symposium on «A World of Wireless, Mobile and Multimedia Networks» (WoWMoM). doi:10.1109/wowmom.2018.8449737ns-3 Model Library, Online Resourcehttps://www.nsnam.org/doxygen/classns-3_1_1_hybrid_buildings_propagation_loss_model.html#detailsCode Repositoryhttps://github.com/dtodoli/ns3_lorawan_wor

TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers

[EN] Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them to operate with high determinism. For this reason, the IEEE 802.15.4e standard incorporates the Time Slotted Channel Hopping (TSCH) protocol which reduces interference and increases the reliability of transmissions. This standard does not specify how time resources are allocated in TSCH scheduling, leading to multiple scheduling solutions. Schedulers can be classified as autonomous, distributed and centralised. The first two have prevailed over the centralised ones because they do not require high signalling, along with the advantages of ease of deployment and high performance. However, the increased QoS requirements and the diversity of traffic flows that circulate through the network in today's Industry 4.0 environment require strict, dynamic control to guarantee parameters such as delay, packet loss and deadline, independently for each flow. That cannot always be achieved with distributed or autonomous schedulers. For this reason, it is necessary to use centralised protocols with a disruptive approach, such as Software Defined Networks (SDN). In these, not only is the control of the MAC layer centralised, but all the decisions of the nodes that make up the network are configured by the controller based on a global vision of the topology and resources, which allows optimal decisions to be made. In this work, a comparative analysis is made through simulation and a testbed of the different schedulers to demonstrate the benefits of a fully centralized approach such as SDN. The results obtained show that with SDN it is possible to simplify the management of multiple flows, without the problems of centralised schedulers. SDN maintains the Packet Delivery Ratio (PDR) levels of other distributed solutions, but in addition, it achieves greater determinism with bounded end-to-end delays and Deadline Satisfaction Ratio (DSR) at the cost of increased power consumption.This work has been supported by DAIS (https://dais-project.eu/) which has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 101007273. The JU receives support from the European Union's Horizon 2020 research and innovation programme and Sweden, Spain, Portugal, Belgium, Germany, Slovenia, Czech Republic, Netherlands, Denmark, Norway and Turkey. It has also been funded by Generalitat Valenciana through the "Instituto Valenciano de Competitividad Empresarial-IVACE". Furthermore, has been supported 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.Orozco-Santos, F.; Sempere Paya, VM.; Silvestre-Blanes, J.; Albero Albero, T. (2022). TSCH Multiflow Scheduling with QoS Guarantees: A Comparison of SDN with Common Schedulers. Applied Sciences. 12(1):1-19. https://doi.org/10.3390/app1201011911912

Enhancing SDN WISE with Slicing Over TSCH

[EN] IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime.This work has been supported 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 and has been possible thanks to the collaboration of the Instituto Tecnologico de Informatica (ITI) of Valencia.Orozco-Santos, F.; Sempere Paya, VM.; Albero Albero, T.; Silvestre-Blanes, J. (2021). Enhancing SDN WISE with Slicing Over TSCH. Sensors. 21(4):1-29. https://doi.org/10.3390/s21041075S12921

Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes

[EN] Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission.The research leading to these results is part of the i4Q project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement No. 958205.Vera-Pérez, J.; Silvestre-Blanes, J.; Sempere Paya, VM.; Cuesta Frau, D. (2021). Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes. Applied Sciences. 11(19):1-15. https://doi.org/10.3390/app11198790115111

A Public Fabric Database for Defect Detection Methods and Results

[EN] The use of image processing for the detection and classification of defects has been a reality for some time in science and industry. New methods are continually being presented to improve every aspect of this process. However, these new approaches are applied to a small, private collection of images, which makes a real comparative study of these methods very difficult. The objective of this paper was to compile a public annotated benchmark, that is, an extensive set of images with and without defects, and make these public, to enable the direct comparison of detection and classification methods. Moreover, different methods are reviewed and one of these is applied to the set of images; the results of which are also presented in this paper.The authors thank for the financial support provided by IVACE (Institut Valencia de Competitivitat Empresarial, Spain) and FEDER (Fondo Europeo de Desarrollo Regional, Europe), throughout the projects: AUTOVIMOTION and INTELITEX.Silvestre-Blanes, J.; Albero Albero, T.; Miralles, I.; Pérez-Llorens, R.; Moreno, J. (2019). A Public Fabric Database for Defect Detection Methods and Results. AUTEX Research Journal. 19(4):363-374. https://doi.org/10.2478/aut-2019-0035S36337419

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