Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

In the present case study, the use of an advanced, efficient and low-cost technique for monitoring an urban stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon stream (Seoul, South Korea) were assessed using an Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO), and pH parameters were monitored for the three summer months and the first fall month at a fixed location. Analysis was performed using clustering techniques (CTs), such as K-means clustering, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN). An IoT-based Arduino sensor module (ASM) network with a 99.99% efficient communication platform was developed to allow collection of stream data with user-friendly software and hardware and facilitated data analysis by interested individuals using their smartphones. Clustering was used to formulate relationships among physicochemical parameters. K-means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into two clusters as well as temperature, DO and pH into four, eight, and four clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. Noise points (NOISE) of temperature in 2016 were border points ( Water 2017, 9, 220; doi:10.3390/w9030220 www.mdpi.com/journal/water Article Internet of Things‐Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream Byungwan Jo 1 and Zafar Baloch 1,2,* 1 Jae Sung Civil Engineering Building, Department of Civil and Environmental Engineering, Hanyang University, 222 Wasgsimini‐ro, Seongdong‐gu, Seoul 04763, Korea; [email protected] 2 Civil Engineering Department, Faculty of Engineering, BUITEMS, Quetta 87650, Balochistan, Pakistan * Correspondence: [email protected]; Tel.: +82‐2‐2220‐3576 or +82‐10‐5927‐0333 Academic Editor: Maria Filomena Camões Received: 29 January 2017; Accepted: 13 March 2017; Published: 16 March 2017 Abstract: In the present case study, the use of an advanced, efficient and low‐cost technique for monitoring an urban stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon stream (Seoul, South Korea) were assessed using an Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO), and pH parameters were monitored for the three summer months and the first fall month at a fixed location. Analysis was performed using clustering techniques (CTs), such as K‐means clustering, agglomerative hierarchical clustering (AHC), and density‐based spatial clustering of applications with noise (DBSCAN). An IoT‐based Arduino sensor module (ASM) network with a 99.99% efficient communication platfor was developed to allow collection of stream data with user‐friendly software and hardware and facilitated data analysis by interested individuals using their smartphones. Clustering was used to formulate relationships among physicochemical parameters. K‐means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into two clusters as well as temperature, DO and pH into four, eight, and four clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. Noise points (NOISE) of temperature in 2016 ere border points (ƥ), whereas in 2014 and 2015 they remained core points (ɋ), indicating a trend toward increasing stream temperature. We found the stream parameters were within the permissible limits set by the Water Quality Standards for River Water, South Korea. In the present case study, the use of an advanced, efficient and low‐cost technique for monitoring an urban stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon stream (Seoul, South Korea) were assessed using an Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO), and pH parameters were monitored for the three summer months and the first fall month at a fixed location. Analysis was performed using clustering techniques (CTs), such as K‐means clustering, agglomerative hierarchical clustering (AHC), and density‐based spatial clustering of applications with noise (DBSCAN). An IoT‐based Arduino sensor module (ASM) network with a 99.99% efficient communication platfor was developed to allow collection of stream data with user‐friendly software and hardware and facilitated data analysis by interested individuals using their smartphones. Clustering was used to formulate relationships among physicochemical parameters. K‐means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into two clusters as well as temperature, DO and pH into four, eight, and four clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. Noise points (NOISE) of temperature in 2016 ere border points (ƥ), whereas in 2014 and 2015 they remained core points (ɋ), indicating a trend toward increasing stream temperature. We found the stream parameters were within the permissible limits set by the Water Quality Standards for River Water, South Korea.