An open source software approach to geospatial sensor network standardization for urban runoff

Rettig, Andrew J.; Khanna, Sumit; Heintzelman, Dan; Beck, Richard

doi:10.1016/j.compenvurbsys.2014.05.003

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The use of mobile sensors is more common for ecological systems, particularly tracking animal movement (e.g., Davis et al, 2012) and for monitoring air quality (Mead et al, 2013); however, Schneider et al (2016) outlined a study in which sensors fitted to rafts or kayaks were used to continuously gather water quality data while moving downstream. Wired sensor networks or systems that required direct data download from local storage were associated with either: (1) scientific studies in which networks were maintained to answer a specific research objective or test a new senor type (Barnard et al, 2014;Pohl et al, 2014); or (2) monitoring networks for human infrastructure in urban environments where Ethernet connections were available (Dauwe et al, 2014;Rettig et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

“…These were in many cases associated with human health (Rogulski, 2018) or climate impacts (Shusterman et al, 2018;Šećerov et al, 2019) or had direct economic implications, for example through flooding (Horita et al, 2015) or fishing livelihoods (Wada et al, 2007). It should be noted that very few studies embraced the principles of open science and open data more generally (however see Rettig et al, 2014;Jones et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

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Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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The use of low-cost sensor networks (LCSNs) is becoming increasingly popular in the environmental sciences and the unprecedented monitoring data generated enable research across a wide spectrum of disciplines and applications. However, in particular, non-technical challenges still hinder the broader development and application of LCSNs. This paper reviews the development of LCSNs over the last 15 years, highlighting trends and future opportunities for a diverse range of environmental applications. We found air quality, meteorological and water-related networks were particularly well represented with few studies focusing on sensor networks for ecological systems. Furthermore, we identified bias toward studies that have direct links to human health, safety and livelihoods. These studies were more likely to involve downstream data analytics, visualizations, and multi-stakeholder participation through citizen science initiatives. However, there was a paucity of studies that considered sustainability factors for the development and implementation of LCSNs. Existing LCSNs are largely focused on detecting and mitigating events which have a direct impact on humans such as flooding, air pollution or geo-hazards, while these applications are important there is a need for future development of LCSNs for monitoring ecosystem structure and function. Our findings highlight three distinct opportunities for future research to unleash the full potential of LCSNs: (1) improvement of links between data collection and downstream activities; (2) the potential to broaden the scope of application systems and fields; and (3) to better integrate stakeholder engagement and sustainable operation to enable longer and greater societal impacts.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

et al. 2019

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“…This system identification allows the results of the design data to be linked with elements of the network. These IDs can later be used as a reference when linking historical data available for future maintenance or planned inspections (Rettig et al, 2014).…”

Section: Excel2gis Tool: Extracting Input Data From Gis Layersmentioning

confidence: 99%

Modelling data of an urban drainage design using a Geographic Information System (GIS) database

Abbas

Salloom

Ruddock

et al. 2019

Journal of Hydrology

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“…Sakurai et al [5] used SOMs (Self-Organizing Maps) of sensor network in indoor environments for human movement tracking and data analysis. Rettig et al [6] implemented a geospatial sensor network for monitoring a green technology stormwater runoff site. Chen et al [7] designed a geospatial sensor web information public service platform based on sensor web information model and service interface specifications.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

Meng

Wang

2014

2014 IEEE 17th International Conference on Computational Science and Engineering

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Nowadays, sensors and sensor networks are being widely used both in the theoretical research and in the engineering application. In order to meet the standardization requirement, the OGC (Open Geospatial Consortium) SWE (Sensor Web Enablement) framework is extended to enable all types of sensors, instruments, and imaging devices to be accessible. However, the raw sensor data cannot represent a wealth of information especially semantic information, and cannot be easily recognized by computers either. With semantic web proposed, formal definitions are captured in ontologies, making it possible for computers to interpret and relate data content more effectively. In the paper, we review the methods of knowledge acquisition and representation, propose to adopt semantic web to acquire and represent sensor knowledge. We are designing and developing a web portal named GSNC (Geospatial Sensor Network eLearning Collaboratory) under the SWE framework. The GSNC application combines sensor data with semantics identified by human and machines, and makes the sensor knowledge acquisition and representation available. In addition, the GSNC project provides the web-based SensorML (Sensor Model Language) editor toolkit which can be migrated to other applications.

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An open source software approach to geospatial sensor network standardization for urban runoff

Cited by 8 publications

References 8 publications

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Modelling data of an urban drainage design using a Geographic Information System (GIS) database

Geospatial Sensor Network eLearning Collaboratory - A Portal for Sensor Knowledge Acquisition and Representation

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