Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring

Yohanes Yohanie Fridelin Panduman, Mochamad Rifki Ulil Albaab, Adnan Rachmat Anom Besari, Sritrusta Sukaridhoto, Anang Tjahjono, Rizqi Putri Nourma Budiarti

Abstract


Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality.

Keywords


cloud computing; air quality; Kafka; data abstraction; internet of things.

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References


Liu, Jen-Hao, et al. "Developed urban air quality monitoring system based on wireless sensor networks." Sensing technology (ICT), 2011 fifth international conference on. IEEE, 2011.

Satya Widya Yudha. Air Pollution in Indonesia Challenges and Imperatives for Change. Singapore: The National Bureau of Asian Research.2016.

Head of Environmental Impact Control Agency, Perhitungan Dan Pelaporan Serta Informasi Indeks Standard Pencemar Udara [Calculation And Reporting As well as Information on Air Pollution Standard Index]. Indonesia: of Environmental Impact Control Agency.1997.

Sukaridhoto, S., Pramadihanto, D., Alif, M., Yuwono, A., & Funabiki, N. (2015, May). “A design of radio-controlled submarine modification for river water quality monitoring.†In Intelligent Technology and Its Applications (ISITIA), 2015 International Seminar on (pp. 75-80). IEEE.

Berlian, M. H., Sahputra, T. E. R., Ardi, B. J. W., Dzatmika, L. W., Besari, A. R. A., Sudibyo, R. W., & Sukaridhoto, S. (2016, September). “Design and implementation of smart environment monitoring and analytics in real-time system framework based on internet of underwater things and big data.†In Electronics Symposium (IES), 2016 International (pp. 403-408). IEEE.

Sukaridhoto, Sritrusta, et al. "Design and development of a portable, low-cost COTS-based water quality monitoring system." 2016 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2016.

Arridha, R., Sukaridhoto, S., Pramadihanto, D., & Funabiki, N. (2017). “Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in the real-time system.†International Journal of Space-Based and Situated Computing, 7(2), 82-93.

Abdillah, Abid Famasya, et al. "Design and development of low-cost coral monitoring system for shallow water based on internet of underwater things." Journal of Telecommunication, Electronic and Computer Engineering (JTEC) 9.2-5 (2017): 97-101.

Panduman, Y. Y. F., Besari, A.R.A., Sukaridhoto, S., Budiarti, R. P. N., Sudibyo, R. W., & Funabiki, N., (2018). “Implementation of Integration VaaMSN and SEMAR for Wide-Coverage Air Quality Monitoring.†TELKOMNIKA, 16(6), 2630- 2642.

Sukaridhoto, Sritrusta, Achmad Subhan KHalilullah, and Dadet Pramadihanto. "Further Investigation on Building and Benchmarking A Low Power Embedded Cluster for Education." IPTEK Journal of Proceedings Series 1.1 (2015).

Sukaridhoto, S., Sasaki, Y., Ito, K., & Aoki, T. "Development of a compact cluster with embedded CPUs." Proceeding of the Sixth Industrial Electronic Seminar. 2004.

Sill, Alan. "The design and architecture of microservices." IEEE Cloud Computing 3.5 (2016): 76-80.

Meehan, John, et al. "Data Ingestion for the Connected World." CIDR. 2017.

Sun, Long, Yan Li, and Raheel Ahmed Memon. "An open IoT framework based on a microservices architecture." China Communications 14.2 (2017): 154-162.

Villamizar, Mario, et al. "Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures." Cluster, Cloud, and Grid Computing (CCGrid), 2016 16th IEEE/ACM International Symposium on. IEEE, 2016.

Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). “Edge computing: Vision and challenges.†IEEE Internet of Things Journal, 3(5), 637-646.

Wiska, Rindra et al. "Big sensor-generated data streaming using Kafka and impala for data storage in wireless sensor network for co 2 monitoring." 2016 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2016.

Phala, Kgoputjo Simon Elvis, Anuj Kumar, and Gerhard P. Hancke. "Air quality monitoring system based on ISO/IEC/IEEE 21451 standards." IEEE Sensors Journal 16.12 (2016): 5037-5045.

Kang, L., Poslad, S., Wang, W., Li, X., Zhang, Y., & Wang, C. (2016, September). “A public transport bus as a flexible mobile smart environment sensing platform for IoT.†In Intelligent Environments (IE), 2016 12th International Conference on (pp. 1-8). IEEE.

Hunkeler, U., Truong, H. L., & Stanford-Clark, A. (2008, January). “MQTT-S—A publish/subscribe protocol for Wireless Sensor Networks.†In Communication systems software and middleware and workshops, 2008. comsware 2008. 3rd international conference on (pp. 791-798). IEEE.

Leighton, Benjamin, et al. "A best of both worlds approach to complex, efficient, time series data delivery." International Symposium on Environmental Software Systems. Springer, Cham, 2015.

Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." Journal of machine learning research 12.Oct (2011): 2825-2830.

Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297.

Nieto, PJ García, et al. "A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study." Applied Mathematics and Computation219.17 (2013): 8923-8937.

Breiman, Leo. Classification and regression trees. Routledge, 2017.

Altukhov, V., and E. Chemeritskiy. "On real-time delay monitoring in software-defined networks." Science and Technology Conference (Modern Networking Technologies) MoNeTeC, 2014 First International. IEEE, 2014.

Zappatore, Marco, et al. "Mobile Crowd Sensing-based noise monitoring as a way to improve learning quality on acoustics." Interactive Mobile Communication Technologies and Learning (IMCL), 2015 International Conference on. IEEE, 2015.




DOI: http://dx.doi.org/10.18517/ijaseit.9.5.8547

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