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IoT-based River Water Quality Monitoring Design for Smart Environments in Cimahi City
Hanifah H.P.a, Supangkat S.H.a
a School of Electrical Engineering and Informatics, Institute of Technology, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Smart Environment is one of the Smart City domain that has a primary focus on using technology to help the government to create a comfortable environment for the community. River water pollution is a significant concern in Cimahi City, which has an industrial area where most of its waste is discharged through rivers. Although there are regulations regarding the placement of wastewater treatment plants before disposal of wastewater, rogue industry players still commit violations that cause river water pollution by industrial waste. Therefore, appropriate technology is needed that can be used to monitor and classify river water quality based on the level of water pollution in various river locations. The proposed technology is based on the Internet of Things, which consists of sensor devices and microcontrollers with data classification methods using k-mean clustering. The sensors used are ph sensors, temperature sensors, conductivity and salinity sensors, flow sensors, rain sensors, and turbidity sensors. The combination of various parameters is expected to show the quality of river water comprehensively and sustainably so that predictive analysis can be carried out for the future. This technology is expected to be stakeholder support in making the right decisions related to the improvement and prevention of environmental pollution.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Appropriate technologies,Data classification,Environmental pollutions,K-mean clustering,River water pollutions,River water quality,Stakeholder supports,Wastewater treatment plants[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]internet of things,k-mean clustering,smart environment,water quality[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICEEI47359.2019.8988883[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]