Abstract.

Cyberphysical systems (CFS) are based on the seamless integration of computing power into the physical environment within an organization, enterprise, or production. In this paper we propose a system of proactive localization for tracking and forecasting the location of users and mobile robots. The developed system allows to predict the activity of the monitored object using various methods of machine learning. The paper presents comparative analysis of various machine learning models, as well as the concept of a proactive localization system.

Keywords:

Cyberphysical system, time series forecasting, proactive localization, recurrent neural networks, reinforcement learning.

PP. 72-83.

DOI 10.14357/20718632180408

References

1. Smirnov A.V., Levashova T.V., Foundations and models of context-aware knowledge integration, Information technology and computer systems, vol. 4, 2013, pp. 58-73. (in Russian)
2. Kashevnik A.M., An approach to semantic interoperability support between mobile robots for coalition formation, Information technology and computer systems, vol. 1, 2017, pp. 90-100. (in Russian)
3. Levonevskiy D., Vatamaniuk I., Saveliev A., Integration of Corporate Electronic Services into a Smart Space Using Temporal Logic of Actions, International Conference on Interactive Collaborative Robotics, Springer, Cham, 2017, pp. 134-143.
4. Lee J., Bagheri B., Kao, H.A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems, Manufacturing Letters, 2015, vol. 3, pp. 18-23.
5. Amri M.-H., Becis Y., Aubry D., Ramdani N., Indoor human/robot localization using robust multi-modal data fusion, IEEE International Conference on Robotics and Automation (ICRA), 2015.
6. Liu Z., Yang D.-s., Wen D., Zhang W.-m., Mao W., Cyber-Physical-Social Systems for Command and Control, IEEE Intelligent Systems, July-Aug. 2011, vol. 26, i. 4, pp. 92-96.
7. Frazzon E. M., Hartmann J., Makuschewitz T., Scholz-Reiter B., Towards Socio-Cyber-Physical Systems in Production Networks, Procedia CIRP, 2013, vol. 7, pp. 49-54.
8. Shi J., Wan J., Yan H., Suo H, A survey of Cyber-Physical Systems, International Conference on Wireless Communications and Signal Processing (WCSP), 2011.
9. Balico L. N., Loureiro A. A. F., Nakamura E. F., Barreto R.S., Pazzi R. W., Oliveira H. A. B. F., Localization Prediction in Vehicular Ad Hoc Networks, IEEE Communications Surveys & Tutorials (Early Access), 2018.
10. Nadembega A., Hafid A., Taleb T., A Destination and Mobility Path Prediction Scheme for Mobile Networks, IEEE Transactions on Vehicular Technology vol. 64, i. 6, June 2015, pp. 2577-2590.
11. Lin K., Chen M., Deng J., Hassan M. M., Fortino G., Enhanced Fingerprinting and Trajectory Prediction for IoT Localization in Smart Buildings, IEEE Transactions on Automation Science and Engineering, July 2016, vol. 13, i. 3, pp. 1294-1307.
12. Shit R.C., Sharma S., Puthal D., Zomaya A. Y., Location of Things (LoT): A Review and Taxonomy of Sensors Localization in IoT Infrastructure, IEEE Communications Surveys & Tutorials (Early Access), 2018.
13. Alletto S., Cucchiara R., Fiore G. D., Mainetti L., Mighali V., Patrono L., Serra G, An Indoor Location-Aware System for an IoT-Based Smart Museum, IEEE Internet of Things Journal, April 2016, vol. 3, i. 2, pp. 244-253.
14. Pahlavan K., Krishnamurthy P., Geng Y., Localization Challenges for the Emergence of the Smart World, IEEE Access, 2015, vol. 3, pp. 3058-3067.
15. Drevelle V., Bonnifait P., Robust positioning using relaxed constraint-propagation, IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010.
16. Cook D.J., Schmitter-Edgecombe M., Dawadi P., Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques, IEEE Journal of Biomedical and Health Informatics, 2015, vol.19, i. 6, pp. 1882-1892.
17. Dixit A., Naik A., Use of prediction algorithms in smart homes, International Journal of Machine Learning and Computing, 2014, 4 (2), p. 157.
18. Wang Y., Yuan N.J., Lian D., Xie X., Chen E., Rui Y., Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 1275-1284.
19. Xu G., Gao S., Daneshmand M., Wang C., Liu Y., A Survey for Mobility Big Data Analytics for Geolocation Prediction, IEEE Wireless Communications, 2017, vol. 24, i. 1, pp. 111-119.
20. Kim Y., An J., Lee M., Lee Y., An Activity-Embedding Approach for Next-Activity Prediction in a Multi-User Smart Space, IEEE International Conference on Smart Computing (SMARTCOMP), 2017.
21. Qolomany B., Al-Fuqaha A., Benhaddou D., Gupta A., Role of Deep LSTM Neural Networks And Wi-Fi Networks in Support of Occupancy Prediction in Smart Buildings, The 15th IEEE International Conference on Smart City, 2017, pp. 18-20.
22. Saveliev A., Malov D., Edemskii A. Proactive Localization System Concept for Users of Cyber Physical Space, International Conference on Interactive Collaborative Robotics. – Springer, Cham, 2018. (unpublished)
23. Official documentation of GYM - a toolkit for developing and comparing reinforcement learning algorithms. Available at: https://gym.openai.com/ (accessed September 28, 2018).
24. Official documentation of ML-Agents in Unity3D. Available at: https://unity3d.com/ru/machine-learning (accessed September 28, 2018).