Street CORNERS: Real-time Contextual Representation of Sensor Network Data for Environmental Trend Identification

Abstract-Sensor networks have been deployed for a range of rural and environmental applications. Wellregarded for the volume and range of data which can be obtained, wireless sensor network applications capable of using the data gathered have not been fully realized, particularly in urban settings. Street CORNERS is a wireless sensor network application which supports the contextual presentation of data gathered from an urban setting. The Street CORNERS application offers realtime data display, and provides support for predictive algorithms suitable for anticipating, detecting, and defending urban communities, among others, from environmental threats such as declining air quality and urban flash floods. Street CORNERS is presented in two parts. The network design and deployment is outlined, followed by a discussion of the design of the network application, which is involved in data preprocessing and the contextual presentation of the data gathered for trend identification

System design and analysis of a web-based application for sensor network data integration and real-time presentation

The design of wireless sensor network applications is an emerging area of research. The volume of data collected by a sensor network of any type can be overwhelming and the associated challenges of application processing and presentation are significant. The design of a wireless network sensor information and identification system (WiNS Id) database which archives the data reported by distributed sensors is outlined here, as well as the implemented support for queries and data presentation. The innovations in this research include near real-time support for data presentation and visual presentation of sensor nodes reporting in a geographical and temporal context. WiNS Id provides support for pattern identification and data mining in sensor systems. © 2009 IEEE

Real-time environmental monitoring and notification for public safety

The design of a wireless sensor network database that records the data reported by distributed sensors and implement support for queries and data presentation is discussed. The chart software permits a sensor query to report the specific details of that sensor at one point in time and present the sensor status for prior dates and times. The bar chart display of real-time sensor data, with appropriate date and timestamps, provides an opportunity to analyze new patterns or correlations, which can then be followed appropriately. The WiNS project demonstrates the viability of a wireless sensor network for real-time monitoring and reporting and the utility of the system for health care and sustainability enable to construct some of the correlation patterns presented from the data. The ability to present the geographical location of a node, along with near real-time interactive sensor data can provide significant contextual information for the data gathered by the sensor network

Street CORNERS: Real-time contextual representation of sensor network data for environmental trend identification

Sensor networks have been deployed for a range of rural and environmental applications. Wellregarded for the volume and range of data which can be obtained, wireless sensor network applications capable of using the data gathered have not been fully realized, particularly in urban settings. Street CORNERS is a wireless sensor network application which supports the contextual presentation of data gathered from an urban setting. The Street CORNERS application offers realtime data display, and provides support for predictive algorithms suitable for anticipating, detecting, and defending urban communities, among others, from environmental threats such as declining air quality and urban flash floods. Street CORNERS is presented in two parts. The network design and deployment is outlined, followed by a discussion of the design of the network application, which is involved in data preprocessing and the contextual presentation of the data gathered for trend identification. © 2009 IEEE