Environmental Sensor Networks: A revolution in the earth system science?
By Jane K. Hart, Kirk Martinez. 2006
772 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Geology, Information Systems and Management
View Paper: https://www.semanticscholar.org/paper/b81d5c3f73a754076241e63209540364172bbfe5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Sarvapali D. Ramchurn
- Philip Valencia
- Lino Marques
- Michael A. Osborne
- Nicholas R. Jennings
- Stephen J. Roberts
- Alex Rogers
- Tarik Taleb
In Geology
- Michael A. Osborne
- Yoshua Bengio
- Mark Gahegan
- Robert G. Raskin
- Michael F. Goodchild
- Partha Pratim Ray
- Chaowei Yang
In Information Systems and Management
- Nicholas R. Jennings
- Mark Gahegan
- Robert G. Raskin
- Michael F. Goodchild
- Partha Pratim Ray
- Chaowei Yang
Citations in the corpus
- Low-Altitude Unmanned Aerial Vehicles-Based Internet of Things Services: Comprehensive Survey and Future Perspectives (2016, background): “Some good examples are surveillance and reconnaissance [9], public safety [10], homeland security [11], [12], forest fire monitoring [13], environmental monitoring [14], security and border surveillance [7], farming [8], or even Internet delivery [15], [16], architecture surveillance [17], goods tra”
- Geospatial Cyberinfrastructure: Past, present and future (2010, methodology): “Sensor networks also utilize GCI to support the evolution from passive logging systems to an intelligent sensor networks that actively send data to servers, a capability that will become a critical asset for Earth and environmental science studies (Hart & Martinez, 2006).”
- Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges (2015, background): “Consequently, the need for automation and intelligent decision making is becoming more important to accomplish this mission (ur Rehman et al., 2014; Suprem et al., 2013; Wang et al., 2006; Hart and Martinez, 2006).”
- Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing (2009, background): “Wireless sensor networks are increasingly being used in terrestrial monitoring applications by ecologists and environmental scientists to collect and transmit data from remote field sites back to base [11,12].”
- Robots for Environmental Monitoring: Significant Advancements and Applications (2012, background): “The following sections describe some key scientific and engineering achievements that are allowing robotics today address a wide range of environmental monitoring problems.”
- Application of continuous monitoring of honeybee colonies (2014, background): “agricultural monitoring, and data collection in those fields is often managed using wireless networks (Aqeel-ur-Rehman et al. 2014; Hart and Martinez 2006; Ruiz-Garcia et al. 2009; Zerger et al. 2010).”
- Internet of things for smart agriculture: Technologies, practices and future direction (2017, background): “Automation and intelligent decision making are also becoming more important to accomplish this mission [22,68].”
- Tackling Climate Change with Machine Learning (2019, background): “A more direct source of data is offered by environmental sensor networks, made from densely packed but low-cost devices [12, 491, 492].”
- Towards Real-Time Information Processing of Sensor Network Data Using Computationally Efficient Multi-output Gaussian Processes (2008, background): “…readings from sensor networks (deciding when and which sensor to acquire readings from at any time), and which can, with minimal domain knowledge, perform a range of information processing tasks including modelling the accuracy of the sensor readings, predicting the value of missing sensor…”
- Tree‐based iterative input variable selection for hydrological modeling (2013, background): “In addition, advances in monitoring systems, from remote sensing techniques to pervasive real and virtual sensor networks [e.g., Hart and Martinez, 2006; Hill et al., 2011], has made available an increasingly larger amount of data at the local and global scale at progressively finer temporal and…”