IoT Based Indoor Disinfection Coordinating System Against the New Coronavirus

Yıl 2020, Cilt: 4 Sayı: 2, 81 - 85, 31.12.2020

Fırat Aydemir

https://doi.org/10.47897/bilmes.751995

Cited By: 2

Öz

In this study, a system solution for monitoring and coordinating indoor disinfection processes based on the Internet of Things technology is presented. Studies about COVID-19 shows that novel coronavirus is spreading through the virus-containing droplets exhaled by infected people on the surfaces; moreover, it is shown that the virus can remain stable up to 72 hours depending on the type of surface. Therefore, proper sterilization and disinfection routines in public areas play a major role in reducing the spread of coronavirus. In the proposed system, IoT nodes, consisting of single-board computer and camera, separate the human density in certain regions into various levels through image processing algorithms and write these densities in a cloud database. An Android application reads data from the cloud database periodically and locates the risky areas on the map. When the sterilization staff disinfects the specified spots, his/her location is determined in the android application via Bluetooth beacons located in the area, and the database is updated to show that disinfection is complete in these areas.

Anahtar Kelimeler

Internet of Things, COVID-19, Disinfection, Bluetooth Beacon, Cloud Database

IoT Based Indoor Disinfection Coordinating System Against the New Coronavirus

Öz

In this study, a system solution for monitoring and coordinating indoor disinfection processes based on the Internet of Things technology is presented. Studies about COVID-19 shows that novel coronavirus is spreading through the virus-containing droplets exhaled by infected people on the surfaces; moreover, it is shown that the virus can remain stable up to 72 hours depending on the type of surface. Therefore, proper sterilization and disinfection routines in public areas play a major role in reducing the spread of coronavirus. In the proposed conceptual system, IoT nodes, consisting of single-board computer and camera, separate the human density in certain regions into various levels through image processing algorithms and write these densities in a cloud database. An Android application reads data from the cloud database periodically and locates the risky areas on the map. When the sterilization staff disinfects the specified spots, his/her location is determined in the android application via Bluetooth beacons located in the area, and the database is updated to show that disinfection is complete in these areas.

Anahtar Kelimeler

Internet of things, COVID-19, disinfection, bluetooth beacon, cloud database

Kaynakça

• [1] N. Zhu et al., “A novel coronavirus from patients with pneumonia in China, 2019,” N. Engl. J. Med., vol. 382, no. 8, pp. 727–733, 2020.
• [2] World Health Organization (2020) Novel Coronavirus (2019-nCoV). Situation Report-51, 11 March 2020.
• [3] World Health Organization (2020) Novel Coronavirus (2019-nCoV). Situation Report-137, 5 June 2020.
• [4] H. A. Rothan and S. N. Byrareddy, “The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak,” J. Autoimmun., vol. 109, no. February, pp. 18–21, 2020.
• [5] Y. H. Jin et al., “A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version),” Med. J. Chinese People’s Lib. Army, vol. 45, no. 1, pp. 1–20, 2020.
• [6] T. Singhal, “A Review of Coronavirus Disease-2019 (COVID-19),” Indian J. Pediatr., vol. 87, no. 4, pp. 281–286, 2020.
• [7] World Health Organization (2020) Novel Coronavirus (2019-nCoV). Situation Report-11, 31 January 2020.
• [8] R. P. Singh, M. Javaid, A. Haleem, and R. Suman, “Internet of things (IoT) applications to fight against COVID-19 pandemic,” Diabetes Metab. Syndr., vol. 14, no. 4, pp. 521–524, 2020.
• [9] E. Christaki, “New technologies in predicting, preventing and controlling emerging infectious diseases,” Virulence, vol. 6, no. 6, pp. 558–565, 2015.
• [10] M. Pal, G. Berhanu, C. Desalegn, and V. Kandi, “Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2): An Update,” Cureus, vol. 2, no. 3, 2020.
• [11] G. Kampf, D. Todt, S. Pfaender, and E. Steinmann, “Persistence of coronaviruses on inanimate surfaces and their inactivation with biocidal agents,” J. Hosp. Infect., vol. 104, no. 3, pp. 246–251, 2020.
• [12] L. Patients, D. Taylor, A. C. Lindsay, and J. P. Halcox, “Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1,” N. Engl. J. Med., pp. 0–3,2020.
• [13] Y. Y. Liu et al., “Aerodynamic Characteristics and RNA Concentration of SARS-CoV-2 Aerosol in Wuhan Hospitals during COVID-19 Outbreak,” bioRxiv, vol. 86, no. 21, p. 2020.03.08.982637, 2020.
• [14] A. W. H. Chin et al., “Stability of SARS-CoV-2 in different environmental conditions,” The Lancet Microbe, vol. 1, no. 1, p. e10, 2020.
• [15] S. P. Adhikari et al., “Epidemiology, causes, clinical manifestation and diagnosis, prevention and control of coronavirus disease (COVID-19) during the early outbreak period: A scoping review,” Infect. Dis. Poverty, vol. 9, no. 1, pp. 1–12, 2020.
• [16] C. wei Lu, X. fen Liu, and Z. fang Jia, “2019-nCoV transmission through the ocular surface must not be ignored,” Lancet, vol. 395, no. 10224, p. e39, 2020.
• [17] G. Pascarella et al., “COVID-19 diagnosis and management: a comprehensive review”, vol. 2019. 2020.
• [18] C. Hegde et al., “AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System,” medRxiv, p. 2020.04.09.20059840, 2020.
• [19] G. B. Rehm, X. L. Chen, B. T. Kuhn, I. Cortes-puch, N. R. Anderson, and J. Y. Adams, “Leveraging IoTs and Machine Learning for Patient Diagnosis and Ventilation Management in the Intensive Care Unit,” pp. 1–11, 2020.
• [20] A. K. Tripathy, A. G. Mohapatra, S. P. Mohanty, E. Kougianos, A. M. Joshi, and G. Das, “EasyBand: A Wearable for Safety-Aware Mobility during Pandemic Outbreak,” IEEE Consum. Electron. Mag., pp. 1–5, 2020.
• [21] S. I. Sou, W. H. Lin, K. C. Lan, and C. S. Lin, “Indoor location learning over wireless fingerprinting system with particle markov chain model,” IEEE Access, vol. 7, pp. 8713–8725, 2019.
• [22] Y. Zhuang, J. Yang, Y. Li, L. Qi, and N. El-Sheimy, “Smartphone-based indoor localization with bluetooth low energy beacons,” Sensors (Switzerland), vol. 16, no. 5, pp. 1–20, 2016.
• [23] P. Kriz, F. Maly, and T. Kozel, ““Improving Indoor Localization Using Bluetooth Low Energy Beacons”, Mob. Inf. Syst., vol. 2016, 2016.