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A Conceptual Model of Sensor System Ontology with an Event-Based Information Processing Method

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The aim of the present work was to analyze existing methods of event-based processing of information both at the level of the sensors of sensor systems and at the level of the system as a whole. To achieve this goal, sensors with an event-based principle of operation are considered, the most widely used of these being cameras and dynamic sound sensors. For other types of sensors with continuous data transmission, event processing methods using ontologies that work with homogeneous and heterogeneous sensor systems are considered. Methods for separating events from the general flow of data coming from sensors and methods for creating complex events are identified. The most popular way to isolate an event from a stream of data coming from sensors is to match the data received from sensors with a sample. To create complex events, most of the studies addressed here use templates and specialized systems for processing complex events. Drawbacks of these methods are highlighted and an approach to eliminating them is proposed, based on developing an editable ontology for the sensor system able to take account of the consequences of adding or removing sensor nodes.

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References

  • Angsuchotmetee, C., Chbeir, R., and Cardinale, Y., “MSSN-Onto: An ontology-based approach for flexible event processing in Multimedia Sensor Networks,” Fut. Gener. Comp. Syst., 108, 1140–1158 (2020), https://doi.org/10.1016/j.future.2018.01.044.

    Article  Google Scholar 

  • Anumula, J., Ceolini, E., He, Z., et al., “An event-driven probabilistic model of sound source localization using cochlea spikes,” IEEE Int. Symp. on Circuits and Systems (ISCAS) (2018), pp. 1–5, https://doi.org/10.1109/iscas.2018.8351856.

  • Belbachir, A. N., Litzenberger, M., Posch, C., and Schon, P., “Real-time vision using a smart sensor system,” IEEE Int. Symp. on Industrial Electronics (2007), pp. 1968–1973, https://doi.org/10.1109/ISIE.2007.4374909.

  • Belkaroui, R., Bertaux, A., Labbani, O., et al., “Towards events ontology based on data sensors network for viticulture domain,” in: Proc. 8th Int. Conf. on the Internet of Things (2018), pp. 1–7, https://doi.org/10.1145/3277593.3277619.

  • Bhargavi, R., Vaidehi, V., Bhuvaneswari, P. T. V., et al., “Complex event processing for object tracking and intrusion detection in wireless sensor networks,” in: 11th Int. Conf. on Control Automation Robotics & Vision (2010), pp. 848–853, https://doi.org/10.1109/ICARCV.2010.5707288.

  • Brickley, D. and Miller, L., FOAF Vocabulary Specifi cation 0.99 (2014), http://xmlns.com/foaf/spec/URL, acc. Oct. 20, 2021.

  • Brickley, D., Geo Ontology (2003), https://www.w3.org/2003/01/geo/#development, acc. Oct. 20, 2021.

  • Cao, Q., Abdelzaher, T., He, T., and Stankovic, J., “Towards optimal sleep scheduling in sensor networks for rare-event detection,” in: IPSN 2005. 4th Int. Symp. on Information Processing in Sensor Networks (2005), pp. 20–27, https://doi.org/10.1109/IPSN.2005.1440887.

  • Ceolini, E., Frenkel, C., Shrestha, S. B., et al., Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing,” Front. Neurosci., 14, 637 (2020), https://doi.org/10.3389/fnins.2020.00637.

    Article  PubMed  PubMed Central  Google Scholar 

  • Dunkel, J., “On complex event processing for sensor networks,” Int. Symp. on Autonomous Decentralized Systems (2009), pp. 1–6, https://doi.org/10.1109/ISADS.2009.5207376.

  • Ghosh, R., Mishra, A., Orchard, G., and Thako, N. V., “Real-time object recognition and orientation estimation using an event-based camera and CNN,” IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings (2014), pp. 544–547, https://doi.org/10.1109/BioCAS.2014.6981783.

  • Glover, A. and Bartolozzi, C., “Robust visual tracking with a freely-moving event camera,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS) (2017), pp. 3769–3776, https://doi.org/10.1109/IROS.2017.8206226.

  • Herzog, A., Jacobi, D., and Buchmann, A., “A3ME – an Agent-Based middleware approach for mixed mode environments,” 2nd Int. Conf. on Mobile Ubiquitous Computing, Systems, Services and Technologies (2008), pp. 191–196, https://doi.org/10.1109/UBICOMM.2008.78.

  • Hobbs, J. R. and Feng Pan, Time Ontology (2017), https://www.w3.org/TR/owl-time/, acc. Oct 20, 2021.

  • Kasi, M. K., Hinze, A., Legg, C., and Jones, S., “SEPSen: Semantic event processing at the sensor nodes for energy effi cient wireless sensor networks,” in: Proc. 6th ACM Int. Conf. on Distributed Event-Based Systems, 119–122 (2021), https://doi.org/10.1145/2335484.2335497.

  • Khaidarova, R. R., Konev, A. S., Lapaev, M. V., and Bondarenko, I. B., “An ontological approach to automating the processes of microclimate control and safety of museum objects,” Nauchn. Tekhn. Vestn. Inform. Tekhnol. Mekhan. Opt., 19, No. 1, 118–125 (2019).

    Article  Google Scholar 

  • Kim, H., Leutenegger, S., and Davison, A. J., “Real-time 3D reconstruction and 6-DoF tracking with an event camera,” in: Europ. Conf. on Computer Vision (2016), pp. 349–364, https://doi.org/10.1007/978-3-319-46466-4_21.

  • Kuznetsov, B. F. and Buzunova, M. Yu., “Ontology of a street lighting control system,” in: Climate, Ecology, and Agriculture of Eurasia, Conference Proceedings (2018), pp. 217–222.

  • Lee, W. and Bailer, W., Ontology for Media Resources 1.0 (2012), https://www.w3.org/TR/mediaont-10/, acc. Sept. 9, 2021.

  • Lefort, L., Henson, C., and Taylor, K., Semantic Sensor Network XG Final Report (2011), http://www.w3.org/2005/Incubator/ssn/XGRssn-20110628/, acc. Oct 20, 2021.

  • Li, C. H., Delbruck, T., and Liu, S. C., “Real-time speaker identification using the AEREAR2 event-based silicon cochlea,” IEEE Int. Symp. on Circuits and Systems(ISCAS) (2012), pp. 1159–1162, https://doi.org/10.1109/iscas.2012.6271438.

  • Liu, H., Moeys, D. P., Das, G., et al., “Combined frame- and event-based detection and tracking,” IEEE Int. Symp. on Circuits and Systems (ISCAS) (2016), https://doi.org/10.1109/iscas.2016.7539103.

    Article  Google Scholar 

  • Mazo, M., and Tabuada, P., “Decentralized event-triggered control over wireless sensor/actuator networks,” IEEE T. Autom. Contr., 56, No. 10, 2456–2461 (2011), https://doi.org/10.1109/TAC.2011.2164036.

    Article  Google Scholar 

  • Mueggler, E., Huber, B., and Scaramuzza, D., “Event-based, 6-DOF pose tracking for high-speed maneuvers,” IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (2014), pp. 2761–2768, https://doi.org/10.1109/iros.2014.6942940.

  • Mueggler, E., Rebecq, H., Gallego, G., et al., “The event-camera dataset and simulator: Event-based data for pose estimation, visual odometry, and SLAM,” Int. J. Robot. Res., 36, No. 2, 142–149 (2017), https://doi.org/10.1177/0278364917691115.

    Article  Google Scholar 

  • Nawaz, F., Janjua, N. K., and Hussain, O. K., “PERCEPTUS: Predictive complex event processing and reasoning for IoT-enabled supply chain,” Knowl.-Based Syst., 180, 133–146 (2019), https://doi.org/10.1016/j.knosys.2019.05.024.

  • Raimond, Y. and Abdallah, S., The Event Ontology. http://motools.sourceforge.net/event/event.html, acc. Oct 20, 2021.

  • Ramesh, B., Zhang, S., Lee, Z. W., et al., “Long-term object tracking with a moving event camera,” in: Proc. 29th British Machine Vision Conference (2018), p. 241.

  • Rebecq, H., Horstschaefer, T., Gallego, G., and Scaramuzza, D., “EVO: A Geometric approach to event-based 6-DOF parallel tracking and mapping in real time,” IEEE Robot. Automat. Lett., 2, No. 2, 593–600 (2017), https://doi.org/10.1109/lra.2016.2645143.

    Article  Google Scholar 

  • Rinne, M., Blomqvist, E., Keskisärkkä, R., and Nuutila, E., “Event processing in RDF,” in: Proc. 4th Int. Conf. on Ontology and Semantic Web Patterns (2013), Vol. 118, pp. 52–64.

  • Savel’ev, A. I., Khar’kov, I. Yu., Pavlyuk, N. A., and Karpov, A. A., Mobile Autonomous Robotic Platform with Modular Structure, RF Patent No. RU 2704048 C1 (2019).

  • Shaukat, A., Shah, K., Ullah, I., et al., “SmartOntoSensor: Ontology for Semantic Interpretation of Smartphone Sensors Data for Context-Aware Applications,” J. Sensors, 1–26 (2017), https://doi.org/10.1155/2017/8790198.

  • Sokolova, A. and Konushin, A., “Human identifi cation by gait from eventbased camera,” 16th Int. Conf. on Machine Vision Applications (MVA) (2019), pp. 1–6, https://doi.org/10.23919/MVA.2019.8758019.

  • Somov, A., Baranov, A., Savkin, A., et al., “Development of wireless sensor network for combustible gas monitoring,” Sens. Actuat. A: Physical, 171, No. 2, 398–405 (2011), https://doi.org/10.1016/j.sna.2011.07.016, EDN: PEDPJX.

    Article  CAS  Google Scholar 

  • Taylor, K. and Leidinger, L., “Ontology-driven complex event processing in heterogeneous sensor networks,” in: Extended Semantic Web Conference (2011), pp. 285–299, https://doi.org/10.1007/978-3-642-21064-8_20.

  • Thierry, M., Ontology for Media Resources (2012), https://www.w3.org/ns/ma-ont#, acc. Oct 20, 2021.

  • Vatamanyuk, I. V. and Savel’ev, A. I., “A mobile robotic platform as a component of the cyberphysical intellectual space,” Ekstrem. Robotekhn., 1, No. 1, 37–42 (2017).

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Authors and Affiliations

  1. St. Petersburg Federal Research Center, Russian Academy of Sciences, St. Petersburg, Russia

    E. O. Cherskikh

  2. St. Petersburg Institute for Informatics and Automation, Russian Academy of Sciences, St. Petersburg, Russia

    E. O. Cherskikh

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  1. E. O. Cherskikh
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Correspondence to E. O. Cherskikh.

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Translated from Sensornye Sistemy, Vol. 36, No. 2, pp. 124–135, April–June, 2022.

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Cherskikh, E.O. A Conceptual Model of Sensor System Ontology with an Event-Based Information Processing Method. Neurosci Behav Physi 52, 1310–1317 (2022). https://doi.org/10.1007/s11055-023-01360-5

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