Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream
2. Experimental Methods
2.1. Experimental Setup
2.1.1. Sampling Site
2.1.2. Testbed Design
2.1.3. Instrument Calibration
2.2. Establishment of an Arduino Sensor Modules Network
2.3. Internet of Things Site Monitoring Procedure
- The sensor modules of temperature, DO, and pH were fabricated with Arduino shield V2.1 as shown in Figure 3a.
- This ASM frame was installed in stainless steel and fixed at the site, as shown in Figure 1b.
- Solar power energy of 3 V to 5 V was supplied to sensor nodes, while solar power energy of 12 V to 24 V was supplied to the Bluetooth antenna using a leakage, corrosion, and water-resistant sealed battery, as shown in Figure 3b.
- This prototype model was pre-programed with the permissible limits of water quality standards for river water in Korea in Arduino IDE 1.0.X (Arduino AG) .
- ASM data acquisition and subsequent data were transferred to the main server. Communication was developed using the Bluetooth antenna, as shown in Figure 3c.
- Development of the ASM as part of the IoT and information sharing with residents on their smartphones are shown in Figure 3d.
- Stream data was transferred to the MS every second on weekdays for three years. Summary statistics of seasonal variations in stream data obtained via the ASM framework are given in Table 1.
2.4. Clustering Techniques
2.4.1. K-Means Clustering Technique
2.4.2. Agglomerative Hierarchical Clustering
2.4.3. Density-Based Spatial Clustering of Applications with Noise
3. Results and Discussion
3.1. Experimental Approach
3.2. K-Means Clustering
3.3. Agglomerative Hierarchical Clustering
3.4. Density-Based Spatial Clustering of Applications with Noise
- Three parameters, temperature, DO, and pH, were measured at a fixed location with 99.99% efficiency using an IoT Arduino platform. Simplified information was provided to residents (end users) on their smartphones. Hence, the proposed IoT platform is highly efficient and reliable in data transmission.
- AHC analysis segmented all data into two clusters, temperature into four clusters, DO into eight clusters, and pH into four clusters. AHC did not provide significant results; however, the optimal time for monitoring individual samples was identified allowing for a reduction in the number of sampling sites.
- DBSCAN results showed that temperature points in 2016 were ƥ (increased), while temperature points in 2014 and 2015 were ɋ (no significant change). The measures showed a trend toward an increase in global temperature. Therefore, a targeted sampling collection can be designed to monitor stream PcPs.
- Our results indicated streams can be monitored and the collected data interpreted through data mining. This interpreted information can be shared on smart devices, such as smartphones, smart screens, and navigation devices.
- We performed monitoring using the IoT prototype based on the Arduino shield, only installed a handful of sensors, and only monitored conditions over a period of four months. However, the results indicated this application can help identify seasonal behavior and efficiently monitor PcPs in a low-cost manner.
- Replication of this work could establish a procedural framework for the Ministry of Environment, Republic of Korea, to allow monitoring of civil infrastructure through intelligent monitoring networks. Information can be shared with end users on their smartphones, which may also benefit researchers.
Conflicts of Interest
- Shrestha, S.; Kazama, F. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environ. Model. Softw. 2007, 22, 464–475. [Google Scholar] [CrossRef]
- Jackson, F.L.; Malcolm, I.A.; Hannah, D.M. A novel approach for designing large-scale river temperature monitoring networks. Hydrol. Res. 2016, 47, 569–590. [Google Scholar] [CrossRef]
- Gasperi, J.; Sebastian, C.; Ruban, V.; Delamain, M.; Percot, S.; Wiest, L.; Mirande, C.; Caupos, E.; Demare, D.; Kessoo, M.D.; et al. Micropollutants in urban stormwater: Occurrence, concentrations, and atmospheric contributions for a wide range of contaminants in three french catchments. Environ. Sci. Pollut. Res. Int. 2014, 21, 5267–5281. [Google Scholar] [CrossRef] [PubMed]
- Koklu, R.; Sengorur, B.; Topal, B. Water quality assessment using multivariate statistical methods—A case study: Melen river system (Turkey). Water Resour. Manag. 2010, 24, 959–978. [Google Scholar] [CrossRef]
- Granell, C.; Havlik, D.; Schade, S.; Sabeur, Z.; Delaney, C.; Pielorz, J.; Uslander, T.; Mazzetti, P.; Schleidt, K.; Kobernus, M.; et al. Future internet technologies for environmental applications. Environ. Model. Softw. 2016, 78, 1–15. [Google Scholar] [CrossRef]
- Iyigun, C.; Turkes, M.; Batmaz, I.; Yozgatligil, C.; Purutcuoglu, V.; Koc, E.K.; Ozturk, M.Z. Clustering current climate regions of turkey by using a multivariate statistical method. Theor. Appl. Climatol. 2013, 114, 95–106. [Google Scholar] [CrossRef]
- Lee, H.S.; Lee, J.H.W. Continuous monitoring of short term dissolved oxygen and algal dynamics. Water Res. 1995, 29, 2789–2796. [Google Scholar] [CrossRef]
- Huang, X.; Yi, J.; Chen, S.; Zhu, X. A wireless sensor network-based approach with decision support for monitoring lake water quality. Sensors 2015, 15, 29273–29296. [Google Scholar] [CrossRef] [PubMed]
- Sun, S.; Barraud, S.; Castebrunet, H.; Aubin, J.B.; Marmonier, P. Long-term stormwater quantity and quality analysis using continuous measurements in a french urban catchment. Water Res. 2015, 85, 432–442. [Google Scholar] [CrossRef] [PubMed]
- Storey, M.V.; van der Gaag, B.; Burns, B.P. Advances in on-line drinking water quality monitoring and early warning systems. Water Res. 2011, 45, 741–747. [Google Scholar] [CrossRef] [PubMed]
- Leskovec, J.; Krause, A.; Guestrin, C.; Faloutsos, C.; VanBriesen, J.; Glance, N. Cost-effective outbreak detection in networks. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, CA, USA, 12–15 August 2007; pp. 420–429.
- Lillesand, T.; Kiefer, R.W.; Chipman, J. Remote Sensing and Image Interpretation; John Wiley & Sons: Hoboken, NJ, USA, 2014. [Google Scholar]
- Demars, B.O.; Manson, J.R. Temperature dependence of stream aeration coefficients and the effect of water turbulence: A critical review. Water Res. 2013, 47, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Ng, R.T.; Han, J. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, Chile, 12–15 September 1994; Morgan Kaufmann Publishers Inc.: Santiago; pp. 144–155.
- Chang, H. Spatial analysis of water quality trends in the han river basin, South Korea. Water Res. 2008, 42, 3285–3304. [Google Scholar] [CrossRef] [PubMed]
- Ester, M.; Kriegel, H.-P.; Sander, J.; Xu, X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA, 2–4 August 1996; pp. 226–231.
- Tran, T.N.; Drab, K.; Daszykowski, M. Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chem. Intell. Lab. Syst. 2013, 120, 92–96. [Google Scholar] [CrossRef]
- Tan, P.N.; Steinbach, M.; Kumar, V. Data Mining Cluster Analysis: Basic Concepts and Algorithms; Addison-Wesly: Reading, MA, USA, 2013. [Google Scholar]
- Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. Data Mining: Practical Machine Learning Tools and Techniques; Morgan Kaufmann: Burlington, MA, USA, 2016. [Google Scholar]
- Satsangi, J.; Silverberg, M.S.; Vermeire, S.; Colombel, J.F. The montreal classification of inflammatory bowel disease: Controversies, consensus, and implications. Gut 2006, 55, 749–753. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Pawlak, Z. Rough sets. Int. J. Comput. Inf. Sci. 1982, 11, 341–356. [Google Scholar] [CrossRef]
- Chung, Y. A simulation of the oxygen profile in the han river. Yonsei Med. J. 1975, 16, 29–39. [Google Scholar] [CrossRef] [PubMed]
- Ministry of Environment, R.o.K. Water Policies & Innovative Practices Republic of Korea 2004; Ministry of Environment: Seoul, Korea, 2004; p. 14.
- Ministry of Environment, Republic of Korea. Ministry of Environment; Ministry of Environment: Sejong City, Korea, 2015; p. 40. Available online: http://eng.me.go.kr/eng/file/readDownloadFile.do?fileId=115224&fileSeq=1&openYn=Y (accessed on 15 March 2017).
- An, Y.-J.; Lee, J.-K.; Cho, S. Korean water quality standards for the protection of human health and aquatic life. In Proceedings of the 2nd International Forum on Water Environment Partnership in Asia, Beppu City, Japan, 3–4 December 2008.
- Han, J.; Pei, J.; Kamber, M. Data Mining: Concepts and Techniques; Elsevier: Amsterdam, The Netherlands, 2011. [Google Scholar]
- Rousseeuw, P.J. Silhouettes—A graphical aid to the interpretation and validation of cluster-analysis. J. Comput. Appl. Math. 1987, 20, 53–65. [Google Scholar] [CrossRef]
- Mcquitty, L.L. Elementary linkage analysis for isolating orthogonal and oblique types and typal relevancies. Educ. Psychol. Meas. 1957, 17, 207–229. [Google Scholar] [CrossRef]
- Ng, R.T.; Han, J.W. Clarans: A method for clustering objects for spatial data mining. IEEE Tran. Knowl. Data Eng. 2002, 14, 1003–1016. [Google Scholar] [CrossRef]
- Ankerst, M.; Breunig, M.M.; Kriegel, H.-P.; Sander, J. Optics. In ACM Sigmod Record; ACM: New York, NY, USA, 1999; Volume 28, pp. 49–60. [Google Scholar]
- Daszykowski, M.; Serneels, S.; Kaczmarek, K.; Van Espen, P.; Croux, C.; Walczak, B. TOMCAT: A MATLAB toolbox for multivariate calibration techniques. Chem. Intell. Lab. Syst. 2007, 85, 269–277. [Google Scholar] [CrossRef]
- Eaton, J.G.; Scheller, R.M. Effects of climate warming on fish thermal habitat in streams of the United States. Limnol. Oceanogr. 1996, 41, 1109–1115. [Google Scholar] [CrossRef]
|Seasonal Parameter||Temperature Data (MBps) Mean Values||DO Data (MBps) Mean Value||pH Data (MBps) Mean Value||Temperature Data (MBps2) Variance||DO Data (MBps2) Variance||pH Data (MBps2) Variance|
|R2 Correlation Values|
|Temperature vs. DO||Temperature vs. pH|
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Jo, B.; Baloch, Z. Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream. Water 2017, 9, 220. https://doi.org/10.3390/w9030220
Jo B, Baloch Z. Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream. Water. 2017; 9(3):220. https://doi.org/10.3390/w9030220Chicago/Turabian Style
Jo, Byungwan, and Zafar Baloch. 2017. "Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream" Water 9, no. 3: 220. https://doi.org/10.3390/w9030220