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A new paradigm for integrated environmental monitoring

Published: 21 June 2010 Publication History

Abstract

The vision of an integrated Earth observation system to help protect and sustain the planet and its inhabitants is significant and timely, and thus has been identified recently by many organizations. Clearly, the societal benefits of global integrated monitoring are many: to understand how environment and humans are linked, to protect and monitor resources (water supply, weather, oceans) and predict and adapt to their change, to provide for sustainable development, and to reduce costs/impacts of disasters and provide for an effective and intelligent response.
The requirements of such a system are that it be able to collect observations (remote sensing/satellite data and in-situ sensors), manage data (archive, model), interface with users (user- and context-specific display), and that it enhance human capacity by providing for research/training, collaboration and ultimately decision support. Further, in order to be effective, it must also be easily usable by a wide cross section of users, provide for advanced analysis and visualization with interaction and collaboration tools over the Internet; be open source, protocol, and information; and future-proof, modular and extendable as new needs and technologies arise.
Intelesense Technologies was spun off from Stanford University to provide worldwide integrated monitoring of the environment and its' inhabitants, to understand their interrelationships and improve our ability to protect the planet and its people. A global network of wireless sensor devices transmit their data to grid-based computing servers where they are integrated with hundreds of thousands of other data sources to help to better understand their interrelationships. This data, along with thousands of sources from NASA, USGS, Google, and others are provided within a federated, open system of systems, with a collaborative, worldwide GIS portal to provide interactive exploration of the world and its data. The goal is to collaborate across government, academia, and with industrial partners to empower the researcher, scientist, and policymaker with data, analysis, and information leading to a better understanding.

References

[1]
Chervenak, A., I. Foster, C. Kesselman, C. Salisbury, and S. Tuecke. 2000. The data grid: Towards an architecture for the distributed management and analysis of large scientific datasets.
[2]
Estrin, D., R. Govindan, J. Heidemann, and S. Kumar. 1999. Next century challenges: Scalable coordination in sensor networks. Proceedings of the 5th Annual Association for Computing Machinery Conference on Mobile Computing and Networking. ACM Press, New York, p. 263--270.
[3]
Gray, J., D. T. Liu, M. Nieto-Santisteban, A. Szalay, D. J. DeWitt, and G. Heber. 2005. Scientific data management in the coming decade. SIGMOD Record 34(4):27--33.
[4]
Heidemann, J., F. Silva, C. Intanagonwiwat, R. Govindan, D. Estrin, and D. Ganesan. 2003. Building efficient wireless sensor networks with low-level naming. Proceedings of the 18th Association for Computing Machinery Symposium on Operating Principles SOSP'01 35: 146--159.
[5]
Hempstead, M., N. Tripathi, P. Mauro, G-Y. Wei, and D. Brooks. 2005. An ultra low power system architecture for sensor network applications. Association for Computing Machinery Architecture News 33:208--219.
[6]
Hill, J., M. Horton, R. King, and L. Krishnamurthy. 2004. The platforms enabling wireless sensor networks. Communications of the ACM 47: 41--46.
[7]
Karasti, H., K. S. Baker, and E. Halkola. 2006. Enriching the notion of data curation in E-Science: Data managing and information infrastructuring in the Long Term Ecological Research (LTER) Network. Computer Supported Cooperative Work 15:321--358.
[8]
Kido, M, Mundt, C, Montgomery, K, Asquith, A, Goodale, D, Kaneshiro, K. 2008. Integration of Wireless Sensor Networks into Cyberinfrastructure for Montitoring Hawaiian "Mountain to Sea" Environments. Environmental Management, DOI 10.1007/s00267-008-9164-9
[9]
Santi, P. 2005. Topology control in wireless ad hoc and sensor networks. ACM Computing Surveys 37:164--194.
[10]
Wilcox, B. A. and R. R. Colwell. 2005. Emerging and reemerging infectious diseases: Biocomplexity as an interdisciplinary paradigm. Ecohealth 2: 244--257.

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  • (2018)High-Performance Small-Scale Raster Map Projection Empowered by CyberinfrastructureCyberGIS for Geospatial Discovery and Innovation10.1007/978-94-024-1531-5_9(171-188)Online publication date: 27-Jun-2018
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cover image ACM Other conferences
COM.Geo '10: Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
June 2010
274 pages
ISBN:9781450300315
DOI:10.1145/1823854
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 June 2010

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Author Tags

  1. cloud computing
  2. data integration
  3. environmental monitoring
  4. modeling and simulation
  5. sensors
  6. visualization

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View all
  • (2023)Integrating Open Source Geospatial Technologies for Environmental Monitoring and Conservation: A Case Study of SaveGreen Project and CCIBIS Geoportal2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)10.1109/IDAACS58523.2023.10348724(621-626)Online publication date: 7-Sep-2023
  • (2019)Portable Autonomous Rain Prediction Model Using Machine Learning Algorithm2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN)10.1109/ViTECoN.2019.8899704(1-4)Online publication date: Mar-2019
  • (2018)High-Performance Small-Scale Raster Map Projection Empowered by CyberinfrastructureCyberGIS for Geospatial Discovery and Innovation10.1007/978-94-024-1531-5_9(171-188)Online publication date: 27-Jun-2018
  • (2016)Wavelet Study of Meteorological Data Collected by Arduino-Weather Station: Impact on Solar Energy Collection TechnologyMATEC Web of Conferences10.1051/matecconf/2016550200455(02004)Online publication date: 25-Apr-2016
  • (2015)Mobile Phones as Ubiquitous Social and Environmental Geo-SensorsEncyclopedia of Mobile Phone Behavior10.4018/978-1-4666-8239-9.ch098(1194-1213)Online publication date: 2015
  • (2014)Visualization of oceanographic applications using a common data modelProceedings of the 29th Annual ACM Symposium on Applied Computing10.1145/2554850.2554859(933-938)Online publication date: 24-Mar-2014

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