The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform

Gaetan Bellot - One of the best experts on this subject based on the ideXlab platform.

Paul Turner - One of the best experts on this subject based on the ideXlab platform.

  • optimisation in the design of Environmental Sensor networks with robustness consideration
    Sensors, 2015
    Co-Authors: Setia Budi, Paulo De Souza, Greg Timms, Vishv Malhotra, Paul Turner
    Abstract:

    This work proposes the design of Environmental Sensor Networks (ESN) through balancing robustness and redundancy. An Evolutionary Algorithm (EA) is employed to find the optimal placement of Sensor nodes in the Region of Interest (RoI). Data quality issues are introduced to simulate their impact on the performance of the ESN. Spatial Regression Test (SRT) is also utilised to promote robustness in data quality of the designed ESN. The proposed method provides high network representativeness (fit for purpose) with minimum Sensor redundancy (cost), and ensures robustness by enabling the network to continue to achieve its objectives when some Sensors fail.

Michael F Allen - One of the best experts on this subject based on the ideXlab platform.

  • advanced technologies and data management practices in Environmental science lessons from academia
    BioScience, 2012
    Co-Authors: Rebecca R. Hernandez, Michelle L Murphymariscal, Matthew S. Mayernik, Michael F Allen
    Abstract:

    Environmental scientists are increasing their capitalization on advancements in technology, computation, and data management. However, the extent of that capitalization is unknown. We analyzed the survey responses of 434 graduate students to evaluate the understanding and use of such advances in the Environmental sciences. Two-thirds of the students had not taken courses related to information science and the analysis of complex data. Seventy-four percent of the students reported no skill in programming languages or computational applications. Of the students who had completed research projects, 26% had created metadata for research data sets, and 29% had archived their data so that it was available online. One-third of these students used an Environmental Sensor. The results differed according to the students’ research status, degree type, and university type. Changes may be necessary in the curricula of university programs that seek to prepare Environmental scientists for this technologically advanced and data-intensive age.

  • Environmental Sensor networks in ecological research
    New Phytologist, 2009
    Co-Authors: Philip W Rundel, Michael F Allen, Eric Graham, Jason Fisher, Thomas C Harmon
    Abstract:

    Environmental Sensor networks offer a powerful combination of distributed sensing capacity, real-time data visualization and analysis, and integration with adjacent networks and remote sensing data streams. These advances have become a reality as a combined result of the continuing miniaturization of electronics, the availability of large data storage and computational capacity, and the pervasive connectivity of the Internet. Environmental Sensor networks have been established and large new networks are planned for monitoring multiple habitats at many different scales. Projects range in spatial scale from continental systems designed to measure global change and Environmental stability to those involved with the monitoring of only a few meters of forest edge in fragmented landscapes. Temporal measurements have ranged from the evaluation of sunfleck dynamics at scales of seconds, to daily CO2 fluxes, to decadal shifts in temperatures. Above-ground Sensor systems are partnered with subsurface soil measurement networks for physical and biological activity, together with aquatic and riparian Sensor networks to measure groundwater fluxes and nutrient dynamics. More recently, complex Sensors, such as networked digital cameras and microphones, as well as newly emerging Sensors, are being integrated into Sensor networks for hierarchical methods of sensing that promise a further understanding of our ecological systems by revealing previously unobservable phenomena.

Barbara S Minsker - One of the best experts on this subject based on the ideXlab platform.

  • anomaly detection in streaming Environmental Sensor data a data driven modeling approach
    Environmental Modelling and Software, 2010
    Co-Authors: David J Hill, Barbara S Minsker
    Abstract:

    The deployment of Environmental Sensors has generated an interest in real-time applications of the data they collect. This research develops a real-time anomaly detection method for Environmental data streams that can be used to identify data that deviate from historical patterns. The method is based on an autoregressive data-driven model of the data stream and its corresponding prediction interval. It performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no pre-classification of anomalies. Furthermore, this method can be easily deployed on a large heterogeneous Sensor network. Sixteen instantiations of this method are compared based on their ability to identify measurement errors in a windspeed data stream from Corpus Christi, Texas. The results indicate that a multilayer perceptron model of the data stream, coupled with replacement of anomalous data points, performs well at identifying erroneous data in this data stream.

Kirk Martinez - One of the best experts on this subject based on the ideXlab platform.

  • Environmental Sensor networks a revolution in the earth system science
    Earth-Science Reviews, 2006
    Co-Authors: Kirk Martinez
    Abstract:

    Environmental Sensor Networks (ESNs) facilitate the study of fundamental processes and the development of hazard response systems. They have evolved from passive logging systems that require manual downloading, into ‘intelligent’ Sensor networks that comprise a network of automatic Sensor nodes and communications systems which actively communicate their data to a Sensor Network Server (SNS) where these data can be integrated with other Environmental datasets. The Sensor nodes can be fixed or mobile and range in scale appropriate to the environment being sensed. ESNs range in scale and function and we have reviewed over 50 representative examples. Large Scale Single Function Networks tend to use large single purpose nodes to cover a wide geographical area. Localised Multifunction Sensor Networks typically monitor a small area in more detail, often with wireless adhoc systems. BioSensor Networks use emerging biotechnologies to monitor Environmental processes as well as developing proxies for immediate use. In the future, Sensor networks will integrate these three elements (Heterogeneous Sensor Networks). The communications system and data storage and integration (cyberinfrastructure) aspects of ESNs are discussed, along with current challenges which need to be addressed. We argue that Environmental Sensor Networks will become a standard research tool for future Earth System and Environmental Science. Not only do they provide a ‘virtual’ connection with the environment, they allow new field and conceptual approaches to the study of Environmental processes to be developed. We suggest that although technological advances have facilitated these changes, it is vital that Earth Systems and Environmental Scientists utilise them.

  • Environmental Sensor networks
    IEEE Computer, 2004
    Co-Authors: Kirk Martinez, R Ong
    Abstract:

    The developments in wireless network technology and miniaturization makes it possible to realistically monitor the natural environment. Within the field of Environmental Sensor networks, domain knowledge is an essential fourth component. Before designing and installing any system, it is necessary to understand its physical environment and deployment in detail. Sensor networks are designed to transmit data from an array of Sensors to a server data repository. They do not necessarily use a simple one way data stream over a communication network rather elements of the system decide what data to pass on, using local area summaries and filtering to minimize power use while maximizing the information content. The Envisense Glacs Web project is developing a monitoring system for a glacial environment. Monitoring the ice caps and glaciers provides valuable information about the global warming and climate change.