Monitoring of performance indicators by the water services regulator using wireless sensor networks
Anzagi, Brenda Lufuso
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The Kenyan water services regulator is required to report annually to the public on issues of water supply and sewerage services and hence maintain a national database of the same. This means that the quality of data provided by the utilities should be reliable data, to ensure efficient and effective management. The lack of appropriate tools to precisely measure data among water utilities impacts on the accuracy of data submitted to the regulator for monitoring. The data validation process is also time consuming as it is rigorous and involves corroboration with other data sources. Leveraging on technology is vital to ensure improved water utility management. This thesis seeks to enhance performance monitoring of water utilities using wireless sensor networks. Challenges experienced by the water services regulator are identified and the smart water infrastructure is the proposed solution. A smart water infrastructure integrates sensors, controls, and analytical components to ensure that the data captured within the system is consistent and precise. The smart water data generated provides visual monitoring of utilities data and provides real-time data for analysis, hence providing an accurate and timely basis for performance. The proposed smart water prototype leverages on the Internet of Things concept, which is a system of interrelated computing devices, which have unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Since a typical smart water infrastructure requires large scale deployment, the use of wireless sensor network architecture is used. It includes data management and processing, actuation and analytics. Primary and secondary data was used to identify the requirements, which then formed an input to the prototype’s design, implementation and testing. The prototype captures selected water quality and water volumes data. It is then evaluated and found to be effective in monitoring the performance of water utilities and reduces the time spent on validating utilities’ operational data. The prototype also demonstrates timely decision making. It is hoped that this study will be applied practically to aid the Kenyan water regulator’s monitoring efforts.
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