Intelligent Elements for ISHM

There are a number of architecture models for implementing Integrated Systems Health Management (ISHM) capabilities. For example, approaches based on the OSA-CBM and OSA-EAI models, or specific architectures developed in response to local needs. NASA s John C. Stennis Space Center (SSC) has developed one such version of an extensible architecture in support of rocket engine testing that integrates a palette of functions in order to achieve an ISHM capability. Among the functional capabilities that are supported by the framework are: prognostic models, anomaly detection, a data base of supporting health information, root cause analysis, intelligent elements, and integrated awareness. This paper focuses on the role that intelligent elements can play in ISHM architectures. We define an intelligent element as a smart element with sufficient computing capacity to support anomaly detection or other algorithms in support of ISHM functions. A smart element has the capabilities of supporting networked implementations of IEEE 1451.x smart sensor and actuator protocols. The ISHM group at SSC has been actively developing intelligent elements in conjunction with several partners at other Centers, universities, and companies as part of our ISHM approach for better supporting rocket engine testing. We have developed several implementations. Among the key features for these intelligent sensors is support for IEEE 1451.1 and incorporation of a suite of algorithms for determination of sensor health. Regardless of the potential advantages that can be achieved using intelligent sensors, existing large-scale systems are still based on conventional sensors and data acquisition systems. In order to bring the benefits of intelligent sensors to these environments, we have also developed virtual implementations of intelligent sensors

Deliberative architecture for smart sensors in the filtering operation of a water purification plant

The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research work, an architecture for deliberative-type intelligent industrial sensors is proposed, based on the BDI (Belief Desire Intentions) model, adaptable to the measurement of different variables of the filtering process of a water purification plant. An intelligent sensor with functions of signal digitalization, self-calibration, alarm generation, communication with PLC, user interface for parameter adjustment, and analysis with data extrapolation have been arranged. For decision making, the use of fuzzy logic techniques has been considered, which allows imprecise parameters to be appropriately represented, simplifying decision problem solving in the industrial environment, generating stable and fast systems with low processing requirements. The proposed architecture has been modelled, simulated and validated using UML language in conjunction with Petri nets, which facilitate the representation of discrete system events, presenting them clearly and precisely. In the implementation and testing of the prototype, C/C ++ language has been used in an 8-bit microcontroller, experimentally corroborating the operation of the device, which allowed evaluating the behavior of a pseudo-intelligent agent based on the requirements of the water treatment plant, and also through comparisons with similar works developed by other researchers

Interfacing of neuromorphic vision, auditory and olfactory sensors with digital neuromorphic circuits

The conventional Von Neumann architecture imposes strict constraints on the development of intelligent adaptive systems. The requirements of substantial computing power to process and analyse complex data make such an approach impractical to be used in implementing smart systems. Neuromorphic engineering has produced promising results in applications such as electronic sensing, networking architectures and complex data processing. This interdisciplinary field takes inspiration from neurobiological architecture and emulates these characteristics using analogue Very Large Scale Integration (VLSI). The unconventional approach of exploiting the non-linear current characteristics of transistors has aided in the development of low-power adaptive systems that can be implemented in intelligent systems. The neuromorphic approach is widely applied in electronic sensing, particularly in vision, auditory, tactile and olfactory sensors. While conventional sensors generate a huge amount of redundant output data, neuromorphic sensors implement the biological concept of spike-based output to generate sparse output data that corresponds to a certain sensing event. The operation principle applied in these sensors supports reduced power consumption with operating efficiency comparable to conventional sensors. Although neuromorphic sensors such as Dynamic Vision Sensor (DVS), Dynamic and Active pixel Vision Sensor (DAVIS) and AEREAR2 are steadily expanding their scope of application in real-world systems, the lack of spike-based data processing algorithms and complex interfacing methods restricts its applications in low-cost standalone autonomous systems. This research addresses the issue of interfacing between neuromorphic sensors and digital neuromorphic circuits. Current interfacing methods of these sensors are dependent on computers for output data processing. This approach restricts the portability of these sensors, limits their application in a standalone system and increases the overall cost of such systems. The proposed methodology simplifies the interfacing of these sensors with digital neuromorphic processors by utilizing AER communication protocols and neuromorphic hardware developed under the Convolution AER Vision Architecture for Real-time (CAVIAR) project. The proposed interface is simulated using a JAVA model that emulates a typical spikebased output of a neuromorphic sensor, in this case an olfactory sensor, and functions that process this data based on supervised learning. The successful implementation of this simulation suggests that the methodology is a practical solution and can be implemented in hardware. The JAVA simulation is compared to a similar model developed in Nengo, a standard large-scale neural simulation tool. The successful completion of this research contributes towards expanding the scope of application of neuromorphic sensors in standalone intelligent systems. The easy interfacing method proposed in this thesis promotes the portability of these sensors by eliminating the dependency on computers for output data processing. The inclusion of neuromorphic Field Programmable Gate Array (FPGA) board allows reconfiguration and deployment of learning algorithms to implement adaptable systems. These low-power systems can be widely applied in biosecurity and environmental monitoring. With this thesis, we suggest directions for future research in neuromorphic standalone systems based on neuromorphic olfaction

Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities

[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2020). Distributed Architecture to Integrate Sensor Information: Object Recognition for Smart Cities. Sensors. 20(1):1-18. https://doi.org/10.3390/s20010112S118201Munera, E., Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., & Noguera, J. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors, 15(8), 18080-18101. doi:10.3390/s150818080Cao, J., Song, C., Peng, S., Xiao, F., & Song, S. (2019). Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles. Sensors, 19(18), 4021. doi:10.3390/s19184021González García, C., Meana-Llorián, D., Pelayo G-Bustelo, B. C., Cueva Lovelle, J. M., & Garcia-Fernandez, N. (2017). Midgar: Detection of people through computer vision in the Internet of Things scenarios to improve the security in Smart Cities, Smart Towns, and Smart Homes. Future Generation Computer Systems, 76, 301-313. doi:10.1016/j.future.2016.12.033Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1-10. doi:10.1016/j.jii.2017.04.005Li, S., Xu, L. D., & Zhao, S. (2014). The internet of things: a survey. Information Systems Frontiers, 17(2), 243-259. doi:10.1007/s10796-014-9492-7Zdraveski, V., Mishev, K., Trajanov, D., & Kocarev, L. (2017). ISO-Standardized Smart City Platform Architecture and Dashboard. IEEE Pervasive Computing, 16(2), 35-43. doi:10.1109/mprv.2017.31Dastjerdi, A. V., & Buyya, R. (2016). Fog Computing: Helping the Internet of Things Realize Its Potential. Computer, 49(8), 112-116. doi:10.1109/mc.2016.245Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for Smart Cities. IEEE Internet of Things Journal, 1(1), 22-32. doi:10.1109/jiot.2014.2306328Hancke, G., Silva, B., & Hancke, Jr., G. (2012). The Role of Advanced Sensing in Smart Cities. Sensors, 13(1), 393-425. doi:10.3390/s130100393Chen, Y. (2016). Industrial information integration—A literature review 2006–2015. Journal of Industrial Information Integration, 2, 30-64. doi:10.1016/j.jii.2016.04.004Lim, G. H., Suh, I. H., & Suh, H. (2011). Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 41(3), 492-509. doi:10.1109/tsmca.2010.2076404Zhang, J. (2010). Multi-source remote sensing data fusion: status and trends. International Journal of Image and Data Fusion, 1(1), 5-24. doi:10.1080/19479830903561035Deng, X., Jiang, Y., Yang, L. T., Lin, M., Yi, L., & Wang, M. (2019). Data fusion based coverage optimization in heterogeneous sensor networks: A survey. Information Fusion, 52, 90-105. doi:10.1016/j.inffus.2018.11.020Jain, A. K., Duin, P. W., & Jianchang Mao. (2000). Statistical pattern recognition: a review. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(1), 4-37. doi:10.1109/34.824819Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Adam, M. S., Anisi, M. H., & Ali, I. (2020). Object tracking sensor networks in smart cities: Taxonomy, architecture, applications, research challenges and future directions. Future Generation Computer Systems, 107, 909-923. doi:10.1016/j.future.2017.12.011Gaur, A., Scotney, B., Parr, G., & McClean, S. (2015). Smart City Architecture and its Applications Based on IoT. Procedia Computer Science, 52, 1089-1094. doi:10.1016/j.procs.2015.05.122Byers, C. C. (2017). Architectural Imperatives for Fog Computing: Use Cases, Requirements, and Architectural Techniques for Fog-Enabled IoT Networks. IEEE Communications Magazine, 55(8), 14-20. doi:10.1109/mcom.2017.1600885Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., Puliafito, A., & Buyya, R. (2018). Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms. Software: Practice and Experience, 48(8), 1475-1492. doi:10.1002/spe.2586Rincon, J. A., Poza-Lujan, J.-L., Julian, V., Posadas-Yagüe, J.-L., & Carrascosa, C. (2016). Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments. PLOS ONE, 11(2), e0149665. doi:10.1371/journal.pone.0149665Pérez Tijero, H., & Gutiérrez, J. J. (2018). Desarrollo de Sistemas Distribuidos de Tiempo Real y de Criticidad Mixta a través del Estándar DDS. Revista Iberoamericana de Automática e Informática industrial, 15(4), 439. doi:10.4995/riai.2017.9000Amurrio, A., Azketa, E., Gutiérrez, J. J., Aldea, M., & Parra, J. (2019). Una revisión de técnicas para la optimización del despliegue y planificación de sistemas de tiempo real distribuidos. Revista Iberoamericana de Automática e Informática industrial, 16(3), 249. doi:10.4995/riai.2019.10997Turtlebot http://turtlebot.comChen, L., Wei, H., & Ferryman, J. (2013). A survey of human motion analysis using depth imagery. Pattern Recognition Letters, 34(15), 1995-2006. doi:10.1016/j.patrec.2013.02.006Munera Sánchez, E., Muñoz Alcobendas, M., Blanes Noguera, J., Benet Gilabert, G., & Simó Ten, J. (2013). A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League. Sensors, 13(11), 14954-14983. doi:10.3390/s131114954Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641-647. doi:10.1109/34.295913Chow, J., Lichti, D., Hol, J., Bellusci, G., & Luinge, H. (2014). IMU and Multiple RGB-D Camera Fusion for Assisting Indoor Stop-and-Go 3D Terrestrial Laser Scanning. Robotics, 3(3), 247-280. doi:10.3390/robotics303024

A Framework Based on Distributed Ledger Technologies for Data Management and Services in Intelligent Transportation Systems

Data are becoming the cornerstone of many businesses and entire systems infrastructure. Intelligent Transportation Systems (ITS) are no different. The ability of intelligent vehicles and devices to acquire and share environmental measurements in the form of data is leading to the creation of smart services for the benefit of individuals. In this paper, we present a system architecture to promote the development of ITS using distributed ledgers and related technologies. Thanks to these, it becomes possible to create, store and share data generated by users through the sensors on their devices or vehicles, while on the move. We propose an architecture based on Distributed Ledger Technologies (DLTs) to offer features such as immutability, traceability and verifiability of data. IOTA, a promising DLT for IoT, is used together with Decentralized File Storages (DFSes) to store and certify data (and their related metadata) coming from vehicles or by the users' devices themselves (smartphones). Ethereum is then exploited as the smart contract platform that coordinates the data sharing through access control mechanisms. Privacy guarantees are provided by the usage of distributed key management systems and Zero Knowledge Proof. We provide experimental results of a testbed based on real traces, in order to understand if DLT and DFS technologies are ready to support complex services, such as those that pertain to ITS. Results clearly show that, while the viability of the proposal cannot be rejected, further work is needed on the responsiveness of DLT infrastructures

Teaching old sensors New tricks: archetypes of intelligence

In this paper a generic intelligent sensor software architecture is described which builds upon the basic requirements of related industry standards (IEEE 1451 and SEVA BS- 7986). It incorporates specific functionalities such as real-time fault detection, drift compensation, adaptation to environmental changes and autonomous reconfiguration. The modular based structure of the intelligent sensor architecture provides enhanced flexibility in regard to the choice of specific algorithmic realizations. In this context, the particular aspects of fault detection and drift estimation are discussed. A mixed indicative/corrective fault detection approach is proposed while it is demonstrated that reversible/irreversible state dependent drift can be estimated using generic algorithms such as the EKF or on-line density estimators. Finally, a parsimonious density estimator is presented and validated through simulated and real data for use in an operating regime dependent fault detection framework

The role of smart sensor networks for voltage monitoring in smart grids

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state

Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In an other context, many studies are carried out aiming at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We suggest in this paper to study the natural convergence between these two approaches and we propose an integration architecture dealing with machine tool and machining control that enables the exploitation of distributed smart sensors and actuators in the decisional system

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities