Development of wireless passive water quality catchment monitoring system

To maintain the quality of aquatic ecosystems, good water quality is needed. The quality of water needs to be tracked in real-time for environmental protection and tracking pollution sources. This paper aims to describe the development and data acquired for water catchment quality monitoring by using a passive system which includes location tagging. Wireless Passive Water Quality Catchment Monitoring (WPWQCM) System is used to check and monitor water quality continuously. The condition of water in terms of acidity, temperature and light intensity needs to be monitored. WPWQCM System featured four sensors which are a temperature sensor, light intensity sensor, pH sensor and GPS tracker that will float in water to collect the data. GPS tracker on passive water catchment monitoring system is a new feature in the system where the location of water can be identified. With the extra feature, water quality can be mapped and in the future, the source of disturbance can be determined. UMP Lake was chosen to check and monitor the water quality. The system used wireless communication by using XBee Pro as a medium of communication between CT-Uno board and PC

Development of Wireless Passive Water Quality Catchment Monitoring System

To maintain the quality of aquatic ecosystems, good water quality is needed. The quality of water needs to be tracked in real-time for environmental protection and tracking pollution sources. This paper aims to describe the development and data acquired for water catchment quality monitoring by using a passive system which includes location tagging. Wireless Passive Water Quality Catchment Monitoring (WPWQCM) System is used to check and monitor water quality continuously. The condition of water in terms of acidity, temperature and light intensity needs to be monitored. WPWQCM System featured four sensors which are a temperature sensor, light intensity sensor, pH sensor and GPS tracker that will float in water to collect the data. GPS tracker on passive water catchment monitoring system is a new feature in the system where the location of water can be identified. With the extra feature, water quality can be mapped and in the future, the source of disturbance can be determined. UMP Lake was chosen to check and monitor the water quality. The system used wireless communication by using XBee Pro as a medium of communication between CT-Uno board and PC

The integration, analysis and visualization of sensor data from dispersed wireless sensor network systems using the SWE framework

Wireless Sensor Networks (WSNs) have been used in numerous applications to remotely gather real-time data on important environmental parameters. There are several projects where WSNs are deployed in different locations and operate independently. Each deployment has its own models, encodings, and services for sensor data, and are integrated with different types of visualization/analysis tools based on in-dividual project requirements. This makes it difucult to reuse these services for other WSN applications. A user/system is impeded by having to learn the models, encodings, and ser-vices of each system, and also must integrate/interoperate data from different data sources. Sensor Web Enablement (SWE) provides a set of standards (web service interfaces and data encoding/model specifications) to make sensor data publicly available on the web. This paper describes how the SWE framework can be extended to integrate disparate WSN sys-tems and to support standardized access to sensor data. The proposed system also introduces a web-based data visualiza-tion and statistical analysis service for data stored in the Sen-sor Observation Service (SOS) by integrating open source technologies. A performance analysis is presented to show that the additional features have minimal impact on the sys-tem. Also some lessons learned through implementing SWE are discussed

The Integration, Analysis and Visualization of Sensor Data from Dispersed Wireless Sensor Network Systems Using the SWE Framework, Journal of Telecommunications and Information Technology, 2001, nr 4

Wireless Sensor Networks (WSNs) have been used in numerous applications to remotely gather real-time data on important environmental parameters. There are several projects where WSNs are deployed in different locations and operate independently. Each deployment has its own models, encodings, and services for sensor data, and are integrated with different types of visualization/analysis tools based on individual project requirements. This makes it difficult to reuse these services for other WSN applications. A user/system is impeded by having to learn the models, encodings, and services of each system, and also must integrate/interoperate data from different data sources. Sensor Web Enablement (SWE) provides a set of standards (web service interfaces and data encoding/model specifications) to make sensor data publicly available on the web. This paper describes how the SWE framework can be extended to integrate disparate WSN systems and to support standardized access to sensor data. The proposed system also introduces a web-based data visualization and statistical analysis service for data stored in the Sensor Observation Service (SOS) by integrating open source technologies. A performance analysis is presented to show that the additional features have minimal impact on the system. Also some lessons learned through implementing SWE are discussed

New Generation Sensor Web Enablement

Many sensor networks have been deployed to monitor Earth’s environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement

Geographic Information Systems for Real-Time Environmental Sensing at Multiple Scales

The purpose of this investigation was to design, implement, and apply a real-time geographic information system for data intensive water resource research and management. The research presented is part of an ongoing, interdisciplinary research program supporting the development of the Intelligent River® observation instrument. The objectives of this research were to 1) design and describe software architecture for a streaming environmental sensing information system, 2) implement and evaluate the proposed information system, and 3) apply the information system for monitoring, analysis, and visualization of an urban stormwater improvement project located in the City of Aiken, South Carolina, USA. This research contributes to the fields of software architecture and urban ecohydrology. The first contribution is a formal architectural description of a streaming environmental sensing information system. This research demonstrates the operation of the information system and provides a reference point for future software implementations. Contributions to urban ecohydrology are in three areas. First, a characterization of soil properties for the study region of the City of Aiken, SC is provided. The analysis includes an evaluation of spatial structure for soil hydrologic properties. Findings indicate no detectable structure at the scales explored during the study. The second contribution to ecohydrology comes from a long-term, continuous monitoring program for bioinfiltration basin structures located in the study area. Results include an analysis of soil moisture dynamics based on data collected at multiple depths with high spatial and temporal resolution. A novel metric is introduced to evaluate the long-term performance of bioinfiltration basin structures based on soil moisture observation data. Findings indicate a decrease in basin performance over time for the monitored sites. The third contribution to the field of ecohydrology is the development and application of a spatially and temporally explicit rainfall infiltration and excess model. The model enables the simulation and visualization of bioinfiltration basin hydrologic response at within-catchment scales. The model is validated against observed soil moisture data. Results include visualizations and stormwater volume calculations based on measured versus predicted bioinfiltration basin performance over time

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