Hastaneler için Ultra-genişbant Konumlandırma Kullanan Bulut Tabanlı Varlık Takip Sistemi

Öz

Hastane enfeksiyonları, hastane ortamında mikroorganizmaların neden olduğu ölümcül hastalıklardır. Bu enfeksiyonlar, yoğun bakım ünitelerinde %70'e varan ölüm oranlarına sahiptir. Bu enfeksiyonla savaşmanın en önemli yolu hastanedeki cihazların ve eşyaların hijyenine dikkat etmektir. Bu bildiri kapsamında, IoT tabanlı gerçek zamanlı gelişmiş bir varlık takip sistemi hayata geçirilmiştir. Önerilen bu sistem üç ana bileşenden oluşmaktadır: El Hijyeni Gözetim Sistemi, Cihaz Takip Sistemi ve Enfeksiyon Kontrol Sistemi. Hastanelerde el hijyeninin sağlanması enfeksiyonların önlenmesi açısından kritik öneme sahiptir. Önerilen Hijyen Gözetim Sistemi sayesinde enfeksiyon riskinin en yüksek olduğu alanlar olan yoğun bakım üniteleri ve yenidoğan servislerinde el hijyeni kontrolü sağlanmaktadır. Ultra geniş bant (UWB) modüllerinden alınan sinyaller, bir konumlandırma algoritması tarafından işlenir ve konum bilgileri üretilir. 1-2 m hassasiyetli konumlandırma nedeniyle birçok senaryoda benzer çalışmalar yetersiz kalırken, UWB teknolojisi sayesinde önerilen sistem bu senaryolarda 20-25 cm hassasiyetle çözüm sunmaktadır. Antibiyotiğe dirençli hastane enfeksiyonunun tedavisi oldukça zordur, bulaşıcıdır ve ölümcüldür. Bu hastalığa yakalananlar mecburi olarak karantinada tutulmaktadır. Bu hastaların nakli sırasında uçtan uca hijyen kontrolü yapılması gerekmektedir. Geliştirilen Enfeksiyon Kontrol Sistemi kullanılarak system tarafından bu hastalar gerçek zamanlı olarak uçtan uca takip edilecektir. Sistemimiz 3 ana katmandan oluşmaktadır. İlk katmanda, ortamdaki dağıtılmış IoT cihazları, mobil düğümlerden yayınlanan UWB ve Bluetooth Düşük Enerji (BLE) sinyallerini toplar. Alınan sinyallerin gücü, çeşitli kök düğümlerinden alınan sinyallere dayalı olarak mobil düğümlerin konumunun hesaplanmasından sorumlu olan ikinci katmana gönderilir. Tespit edilen lokasyonlar, web ve mobil uygulamalar için gerçek zamanlı takip sistemi sağlayan üçüncü katmana gönderilir. Bu katmanda gerçek zamanlı konum verilerini kullanıcılara ulaştırmak için JSON, Ajax, WebSockets, MongoDB ve RestFul gibi farklı teknolojiler kullanılmaktadır.

Anahtar Kelimeler

• Nesnelerin İnterneti
• Bulut Bilişim
• Varlık Takibi
• Ultra-genişbant
• Konumlandırma
• Medikal Sistemler
• Hijyen Takibi

A Cloud-based Asset Tracking System for Hospitals Using Ultra-wideband Localization

Abstract

Nosocomial infections are fatal diseases caused by microorganisms in the hospital environment. It has mortality rates of up to 70% in intensive care units. The most important way to fight this infection is to pay attention to the hygiene of the assets in the hospital. Within the scope of this paper, an IoT-based real-time advanced asset tracking system has been implemented. This system consists of three main components: Hand Hygiene Surveillance System, Device Tracking System, and Infection Control System. Providing hand hygiene in hospitals is critical for the prevention of infections. The proposed Hygiene Surveillance System provides hand hygiene control in intensive care units and neonatal services, which are the areas with the highest risk of infections. Received signals from ultrawide-band (UWB) modules are processed by a designed positioning algorithm. While competitors are insufficient in many scenarios because of the 1-2 m precision positioning, the proposed system provides a solution in these scenarios with 20-25 cm precision. Antibiotic-resistant nosocomial infection is difficult to treat, contagious, and fatal. These patients are kept in quarantine. During the transportation of these patients, end-to-end hygiene control is required. By using the developed Infection Control System, these patients will be followed end-to-end in real-time. Our system consists 3 main layers. In the first layer, the distributed IoT devices in the environment collect the broadcasted UWB and Bluetooth Low Energy (BLE) signals from the mobile nodes. The strength of the received signals is sent to the second layer which is responsible for calculating the location of mobile nodes based on the received signals from different anchor nodes. The detected locations are sent to the thir layer which provide the real time tracking system for the web and mobile applications. In this layer different technologies such as JSON, Ajax, Server Sent Events, WebSockets, and RestFul technologies are used to deliver the real time location data to the users.

Keywords

• Internet of things
• Cloud computing
• Asset tracking
• Ultra-wideband
• Localization
• Medical systems
• Hygiene Monitoring

Kaynakça

1. Alhmiedat,T. A. and Yang, S. H. (2007) A survey: Localization and tracking mobile targets through wireless sensors network. PGNet.
2. Atzori, L., Iera, A. & Morabito, G. (2010). The Internet of Things: A survey, Elsevier, Computer Networks, Volume 54, Issue 15.
3. Belka, R. (2019, November). An indoor tracking system and pattern recognition algorithms as key components of IoT-based entertainment industry. In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019 (Vol. 11176, p. 111765P). International Society for Optics and Photonics.
4. Doherty, L., Pister, K. S. J., and Ghaoui, L. E. (2001) Convex position estimation in wireless sensor networks. Proc. of the 12th Annual Joint Conf. of the IEEE Computer and Communications Societies, Anchorage, Alaska, 22-26 April, pp. 1655-1663. IEEE Computer Society, Washington.
5. He, Y., Bahirat, P., Knijnenburg, B. P., & Menon, A. (2019). A data-driven approach to designing for privacy in household IoT. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(1), 1-47.
6. Karimpour, N., Karaduman, B., Ural, A., Challenger, M., & Dagdeviren, O. (2019, June). Iot based hand hygiene compliance monitoring. In 2019 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-6). IEEE.
7. Khudhair, A. A., Jabbar, S. Q., Sulttan, M. Q., & Wang, D. (2016). Wireless indoor localization systems and techniques: survey and comparative study. Indonesian Journal of Electrical Engineering and Computer Science, 3(2), 392-409.
8. Islam, S. M. R., Kwak, D.,Kabir, M. H., Hossain M. and Kwak, K. (2015). The Internet of Things for Health Care: A Comprehensive Survey, in IEEE Access, vol. 3, pp. 678-708.

9. McClelland, K., Flinner, H., Abler, R., Garver, P., & George, J. (2017, September). Time Difference of Arrival Localization Testbed: Development, Calibration, and Automation. In Proceedings of the GNU Radio Conference (Vol. 2, No. 1, pp. 8-8).
10. Melis, A., Prandini, M., Sartori, L., & Callegati, F. (2016, September). Public transportation, IoT, trust and urban habits. In International conference on internet science (pp. 318-325). Springer, Cham.
11. Mourtzis, D., Vlachou, E., & Milas, N. J. P. C. (2016). Industrial big data as a result of IoT adoption in manufacturing. Procedia cirp, 55, 290-295.
12. Nagpal, R., Shrobe, H., and Bachrach, J. (2003) Organizing a global coordinate system from local information on an ad hoc sensor network. Proc. of the 2nd Int. Workshop on Information Processing in Sensor Networks, Palo Alto, CA, USA, 22-23 April, pp. 333-348. Springer-Verlag, Berlin.
13. Patwari, N., Ash, J. N., Kyperountas, S., Hero, A. O., Moses, R. L., and Correal, N. S. (2005) Locating the nodes: Cooperative localization in wireless sensor networks. IEEE Signal Processing Magazine, 22(4), 54-69.
14. Pei, L., Liu, J., Chen, Y., Chen, R., & Chen, L. (2017). Evaluation of fingerprinting-based WiFi indoor localization coexisted with Bluetooth. The Journal of Global Positioning Systems, 15(1), 1-12.
15. Peng, R., & Sichitiu, M. L. (2006, September). Angle of arrival localization for wireless sensor networks. In 2006 3rd annual IEEE communications society on sensor and ad hoc communications and networks (Vol. 1, pp. 374-382). IEEE.
16. Ruiz, A. R. J., & Granja, F. S. (2017). Comparing ubisense, bespoon, and decawave uwb location systems: Indoor performance analysis. IEEE Transactions on instrumentation and Measurement, 66(8), 2106-2117.
17. Shang, Y., Fromherz, M. P. J., Ruml, W., and Zhang, Y. (2003) Localization from mere connectivity. Proc. of the 4th ACM Int. Symp. on Mobile Ad Hoc Networking and Computing, Annapolis, Maryland, USA, 1-3 June, pp. 201-212. ACM Press, New York.
18. Tan J. & Koo, S. G. M. (2014). A Survey of Technologies in Internet of Things, 2014 IEEE International Conference on Distributed Computing in Sensor Systems, 2014, pp. 269-274.
19. Tripathi, A. K., Sharma, K., Bala, M., Kumar, A., Menon, V. G. and Bashir, A. K. (2021) A Parallel Military-Dog-Based Algorithm for Clustering Big Data in Cognitive Industrial Internet of Things, IEEE Transactions on Industrial Informatics, vol. 17, no. 3, pp. 2134-2142.
20. Wang, Y., Chen, M., Wang, X., Chan, R. H., & Li, W. J. (2018). IoT for next-generation racket sports training. IEEE Internet of Things Journal, 5(6), 4558-4566.
21. Whitmore, A., Agarwal, A. & Da Xu, L. (2015). The Internet of Things—A survey of topics and trends. Inf Syst Front 17, 261–274.
22. Xiao, L. and Ouksel, A. M. (2006) Scalable self-configuring integration of localization and indexing in wireless ad-hoc sensor networks. Proc. of the 7th Int. Conf. on Mobile Data Management, Nara, Japan, 9-13 May. IEEE, Washington.
23. Xu, L. D., He, W. and Li, S. (2014). Internet of Things in Industries: A Survey, in IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233-2243.