From Sensor Web to Sensor Grid Yaohang Li Department of Computer Science North Carolina A&T State University

Agenda Enabling Sensor Web Sensor Markup Language From Sensor Web to Sensor Grid Research at NCAT

Sensors on the Web Sensor Web – Comprise diverse, location-aware environmental sensing devices – Sensors All connected to the web All reporting position All reporting measure All readable remotely Most are accessible from the web in real time Some controllable remotely

Sensor Web at NCAT

SeaMonster develop a wireless sensor web which will harvest high volumes of geospatial environmental data from on and around the Juneau Icefield in Southeast Alaska

Functions of Sensor Web Sensor Discovery – Location – Observable – Quality – Programmable – etc. Obtain Sensor Information – Sensor Data Format – Programming Interface Programming Sensors – According to Needs Alerts and Notification – Notify a user when a particular phenomenon happened

Open Geospatial Consortium – An international, non-profit consensus standards organization Tasks: – Standardize Sensor Web Services – Sensor Web Enablement OGC Sensor Web Service Standards (APIs) – Sensor Registration Service Discovery of sensors and sensor data – Sensor Observation Service Access sensor information (SensorML) and sensor observations (O&M) – Sensor Planning Service Task sensors or sensor systems – Web Alert Service Asynchronous notification of sensor events (tasks, observation of phenomena)

SensorML SensorML – An XML schema for defining the geometric, dynamic, and observational characteristics of a sensor Characteristics of SensorML – Functional Model of Sensors Not Detailed Hardware Description – Hardware Independent Independent of OS (TinyOS, JVM, etc.) Independent of – Handles Stationary and Dynamic Sensors – Handles in-situ and Remote Sensors

Information Provided by SensorML General sensor information in support of data discovery Geolocation of the measure data Sensor observations – Physical properties measured radiometry temperature concentration etc. – Response characteristics temporal response Sensor performance/quality characteristics – Accuracy and Precision – Threshold Assumptions regarding the sensors

SensorML Semantic

From Sensor Web to Sensor Grid Beyond Sensor Web – Sensor Web focuses on sensor data acquisition – Many Sensor Applications Want More

Information Fusion Problem How can we retrieve the valuable information from the large amount of distributed sensor data? – Sensor Fusion – Distributed Sensor Data Mining

Sensor Trustworthiness Problem How can we trust the sensor data from the sensor web? – Sensor Correlation – Sensor data verification

Sensor Service Orchestration Problem How can we coordinate multiple, distributed sensor services? – Temporal-Spatial

Sensor Assimilation Problem How can we feed distributed, heterogeneous sensor data to computational models to achieve real time processing/forecasting?

Sensor Grid: Integrating Sensor Networks and Grid Computing Integrating Sensor Networks and Grid Computing – Give “eyes” and “ears” to a computational grid – Process, model, correlate, and mine real-time, distributed information about phenomena in the physical world – Permit on-the-fly modeling, decision and action Applications – Environment monitoring – Prediction and early warning of natural disasters

Sensor Grid Figure from Chen-Khong Tham and Rajkumar Buyya

Sensor Grid: Physical World  IT World Edge Server Applications AntennaChip RFID TagSensor Hardware & Physics Information Technology Sensor Data Archive

Sensor Grid vs. Sensor Network Service-Oriented Architecture Distributed Information Fusion – Nodes in a sensor network are independently sensoring the environment Redundant Information Some reading may be inaccurate – Information Fusion Compute the most probable sensor reading Distributed Autonomous Decision Making – Can be achieved through an adaptive learing process

Challenge of the Sensor Grid EVENT SOURCES Many types of event sources and sensors – RFID (handheld, portal, truck mount, etc.), barcode, location, moisture, temperature, … Many sensor vendors – Alien, Intermec, Matrics, ThingMagic, SAMsys, Zebra Many different frequencies, protocols, interfaces – LF, HF, VHF, UHF, Microwave – Periodic, Aperiodic – Ethernet, PCMCIA, RS-232 Many devices in different locations to manage – Device health, device upgrade – Distributed management Information Capture – Data format not consistent – Data is granular and redundant – Information based on observations from multiple sensor sources

Ongoing NOAA NCAT – Earth Information System Framework New technologies emerging: – Service-oriented architecture, OpenDAP, HDF5, Google Earth, GIS, GEOSS Framework Approach – Applications can “plug-in” and extend functionality – Can support a broad set of applications with relatively concise software

Earth Information System Data Access Visualization Interactive Visualization Data Access Visualization Interactive Visualization Analysis Forecast/ Prediction Interactive Analysis Uses Framework for Access Viewing Manipulation Observations, Models, Policies Predictions, Improved Models, Improved Policies

Earth System Science “By taking the ‘whole systems’ approach, we are more likely to find sustainable solutions to environmental problems.”

Earth System Model Atmosphere Model Non-transient eddy resolving planetary wave Terrestrial Carbon Cycle Model Simplified TRIFFID Ocean Carbon and Nutrient Cycle Model Including sediments Cryosphere Model Dynamic ice sheet including fast flow. Simple seaice Terrestrial Hydrology Model Simplified MOSES scheme Ocean Model 3-D, non-eddy- resolving, frictional geostrophic

NCAT Workflow for a Sensor Grid – Sensor Orchestration – GridNexus Ensemble-based Kalman Filter for Sensor Data Fusion From Weather Nowcast to Weather Forecast Acknowledgement – NOAA-ISET Center