Sensor Web Enablement SWE and Sensor Modeling Language

Sensor Web Enablement (SWE) and Sensor Modeling Language (Sensor. ML) Öner Balçıklı Middle East Technical University May 2011 METU - 2011 1

Presentation Outline 1. Motivation 2. Open Geospatial Consortium (OGC) 3. Sensor Web Enablement (SWE) 4. Sensor Model Language (Sensor. ML) 5. Examples METU - 2011 2

Motivation • Sensors are devices for the measurement of physical quantities. • There a great variety of sensor types from simple visual thermometers to complex electron microscopes and earth orbiting satellites. • Sensors play vital roles within the environmental, intelligence, emergency management, and defense. • These sensors are not easily discoverable and accessible. • Processing of observations from these sensors are often confined to its own systems. • Often one is unaware of private or public sensor systems that are available for a particular application. • An application area of process modeling concepts in design METU - 2011 3

Why is this a Challenge? • There is a lack of uniform operations and standard representation for sensor data. • There exists no means for resource reallocation and resource sharing. • Deployment and usage of resources is usually tightly coupled with the specific location, application, and devices employed. • Resulting in a lack of interoperability. • Sensor. ML provides a common framework for describing virtually any sensor system, as well as the processing that might be associated with these sensor systems. METU - 2011 4

Interoperability • The ability of two or more autonomous, heterogeneous, distributed digital entities to communicate and cooperate among themselves despite differences in language, context, format or content. • These entities should be able to interact with one another in meaningful ways without special effort by the user – the data producer or consumer – be it human or machine. METU - 2011 5

Open Geospatial Consortium-OGC • An international industry consortium of 334+ companies, government agencies and universities participating in a consensus process to develop publicly available interface specifications and encodings. • Open Standards development by consensus process • Interoperability Programs provide end-to-end implementation and testing before spec approval • Develop standard encodings and Web service interfaces • Sensor Web Enablement Framework • http: //www. opengeospatial. org METU - 2011 6

Open Geospatial Consortium-OGC • Standard encodings: – – Geography Markup Language (GML) Style Layer Description language (SLD) Sensor. ML Observations and Measurement (O&M) • Standard Web Service interfaces: – Web Map Service (WMS) – Web Feature Service (WFS) – Web Coverage Service (WCS) – Catalog Service – Open Location Services – used by communications and navigation industry – Sensor Web Enablement Services (SOS, SAS, SPS) METU - 2011 7

What is Sensor Web Enablement? METU - 2011 8

What is Sensor Web Enablement? • An interoperability framework for accessing and utilizing sensors and sensor systems via Internet and Web protocols • Enables a “Sensor Web” through which applications and services will be able to access sensors of all types over the Web • A set of web-based services can be used to maintain a registry of available sensors and observation queries • Web technology standard for describing the sensors’ outputs, platforms, locations, and control parameters should be used across applications • This standard encompasses specifications for interfaces, protocols, and encodings that enable the use of sensor data and services METU - 2011 9

Sensor Web Enablement Aims • Quickly discover sensors (secure or public) that can meet my needs – location, observables, quality, ability to task • Obtain sensor information in a standard encoding that is understandable by me and my software • Readily access sensor observations in a common manner, and in a form specific to my needs • Subscribe to and receive alerts when a sensor measures a particular phenomenon METU - 2011 10

OGC Sensor Web Enablement Constellations of heterogeneous sensors Vast set of users and applications Satellite Airborne Sensor Web Enablement Weather Surveillance • • Chemical Detectors Biological Detectors • • Distributed self-describing sensors and related services Network Services Link sensors to network and networkcentric services Common XML encodings, information models, and metadata for sensors and observations Access observation data for value added processing and decision support applications Sea State METU - 2011 11

SWE Components - Languages Information Model for Observations and Sensing Sensor and Processing Description Language Observations & Measurements (O&M) Geography. ML (GML) Common Model for Geographical Information METU - 2011 Sensor. ML (SML) Transducer. ML (TML) Multiplexed, Real Time Streaming Protocol 12

SWE Components - Languages • Sensor Model Language (Sensor. ML) – Standard models and XML Schema for describing sensors systems and processes; provides information needed for discovery of sensors, location of sensor observations, processing of lowlevel sensor observations, and listing of properties • Transducer Model Language (Transducer. ML) – The conceptual model and XML Schema for describing transducers and supporting real-time streaming of data to and from sensor systems • Observations and Measurements (O&M) – Standard models and XML Schema for encoding observations and measurements from a sensor, both archived and real-time METU - 2011 13

SWE Components – Web Services • Sensor Observation Service (SOS) – Standard Web service interface for requesting, filtering, and retrieving observations and sensor system information. This is the intermediary between a client and an observation repository or near real-time sensor channel • Sensor Alert Service (SAS) – Standard Web service interface for publishing and subscribing to alerts from sensors • Sensor Planning Service (SPS) – Standard Web service interface for requesting user-driven acquisitions and observations. This is the intermediary between a client and a sensor collection management environment • Web Notification Service (WNS) – Standard Web service interface for asynchronous delivery of messages or alerts from SAS and SPS web services and other elements of service workflows METU - 2011 14

SWE Components – Web Services Command Task Sensor Systems Access Sensor Description and Data Discover Services, Sensors, Providers, Data SOS SPS SAS Catalog Service Clients METU - 2011 Dispatch Sensor Alerts to registered Users Accessible from various types of clients from PDAs and Cell Phones to high end Workstations 15

Sensor Model Language (Sensor. ML) METU - 2011 16

Sensor. ML Overview • Sensor. ML is an XML schema for defining the geometric, dynamic, and observational characteristics of a sensor • Sensor. ML provides the information needed for discovery of sensors, including the sensor’s capabilities, location, and task ability. • The purpose of the sensor description: 1. 2. 3. 4. 5. • provide general sensor information in support of data discovery support the processing and analysis of the sensor measurements support the geo-location of the measured data. provide performance characteristics (e. g. accuracy, threshold, etc. ) archive fundamental properties and assumptions regarding sensor Sensor. ML provides functional model for sensor, not detail description of hardware METU - 2011 17

Information provided by Sensor. ML • Observation characteristics – Physical properties measured (radiometry, temperature, concentration, etc. ) – Quality characteristics (accuracy, precision, etc. ) – Response characteristics (spectral curve, temporal response, etc. ) • Geometry Characteristics – Size, shape, spatial weight function of individual samples – Geometric and temporal characteristics of sample collections • Description and Documentation – Overall information about the sensor – History and reference information supporting the Sensor. ML document METU - 2011 18

Sensor. ML Properties • Within Sensor. ML components (detectors, transmitters, actuators, and filters) are modeled as processes that can be connected and participated within a process chain or system. • Sensor. ML provides a common framework for any process and process chain, but is particularly well-suited for the description of sensor and systems and the processing of sensor observations. • Processes are entities that take one or more inputs and through the application of well-defined methods using specific parameters, results in one or more outputs. • Sensor. ML supports linking between processes and thus supports the concept of process chains, which are themselves defined as processes. METU - 2011 19

Sensor. ML Properties • The models and schema within the core Sensor. ML definition provide a “skeletal” framework for describing processes, process chains, and sensor systems • Within Sensor. ML, all processes and components are encoded as application schema of the Feature model in the Geographic Markup Language (GML) METU - 2011 20

Sensor. ML Properties • A sensor measurement can be modeled as a process by which an input phenomenon is observed by the sensor at some discrete moment in time. • Some measure of some property of that phenomenon is then output from the sensor. • through subsequent processing in software, raw observations are processed to higher-level knowledge. • The Sensor. ML model does not try to define where observation measurement and observation processing begin or end. These are considered as part of the process. METU - 2011 21

Some Definitions in Sensor. ML • Process: A process that takes one or more inputs, and based on parameters and methodologies, generates one or more outputs. • Process Method: Definition of the behavior and interface of a Process. It can be stored in a library so that it can be reused by different Process instances. It essentially describes the process interface and algorithm, and can point the user to existing implementations. • Process Chain: Processing block consisting of interconnected subprocesses, which can in turn be Process Models or Process Chains. A process chain also includes possible data sources as well as connections that explicitly link input and output signals of subprocesses together. It also precisely defines its own inputs, outputs and parameters. METU - 2011 22

Sensor. ML Conceptual Models • In Sensor. ML, all components are modeled as processes. • Components are transducers, actuators, processors (viewed as process components) and sensors and platforms (which are modeled as systems) • Sensor. ML can be viewed as a specialized process description language with emphasis on application to sensor data • Sensor. ML does not try to replace other existing technologies (such as BPEL or MATLAB Simulink) • Sensor. ML-defined processes could be imported and executed within other execution environments, such as BPEL or MATLAB Simulink, as well as within Sensor. ML-enabled process execution software. METU - 2011 23

Sensor. ML Conceptual Models • Models for Sensor. ML are represented using UML structure diagrams. • These UML diagrams represent conceptual models only and are not intended for automatic encoding within XML Schema. • Sensor. ML models sensor systems as a collection of physical and non-physical processes. • A process in Sensor. ML describes inputs and outputs expected, as well as the parameters and methodology required to create output values from input values. • All processes in Sensor. ML are derived from Abstract Process which is itself derived from Abstract Feature. All features have as a minimum, name and description properties. In addition, all processes include inputs, outputs, and parameters. METU - 2011 24

Process Type METU - 2011 25

Sensor. ML Processes METU - 2011 26

SML Concepts – Sensor METU - 2011 27

SML Concepts – Sensor Description METUMike Botts, "Sensor. ML and Sensor Web Enablement, " Earth System Science Center, UAB Huntsville - 2011 28

SML Concepts –Accuracy and Range METU - 2011 29

SML Concepts –Platform METU - 2011 30

SML Concepts – Process Model • In Sensor. ML, everything is modeled as a Process • Process Model – defines atomic process modules (detector being one) – has five sections • metadata • inputs, outputs, parameters • method – Inputs, outputs, and parameters defined using SWE Common data definitions METU - 2011 31

SML Concepts – Process • • Process – defines a process chain – includes: • metadata • inputs, outputs, and parameters • processes (Process Model, Process) • data sources • connections between processes and data System – defines a collection of related processes along with positional information METU - 2011 32

SML Concepts –Metadata Group • Metadata is primarily for discovery and assistance, and not typically used within process execution • Includes – Identification, classification, description – Security, legal, and time constraints – Capabilities and characteristics – Contacts and documentation – History METU - 2011 33

SML Concepts – Event METU - 2011 34

Sensor. ML provides metadata suitable for discovery of sensors and processes METU - 2011 35

Tool Development and Support for Sensor. ML • Open Source Sensor. ML Process Execution Engine – Along with open-source process model library, provides execution environment for Sensor. ML described algorithms • Open Source Sensor. ML editor and process chain development client – on-going development of tools to allow human-friendly editors for Sensor. ML descriptions • Sensor. ML-enabled decision support client – Open source Space Time Toolkit is Sensor. ML-enabled and will be available to discover, access, task, and process sensor observations; use as is or as template for COTS development METU - 2011 36

Sensor. ML Process Editor: Ongoing Work by OGC METU - 2011 37

Example Applications Northrop Grumman Pulse. Net Project METU - 2011 38

Application: NASA Sensor Web METU - 2011 39

Tsunami Early Warning & Mitigation Center Systems Seismic Monitoring GPS Tide Gauges Ocean Bottom Units Buoys EO Data Observations Simulation BMG 5 in 1 / 6 in 1 System Risk- & Vulnerability Modelling METU - 2011 Geospatial Data Repository 40

NASA/NWS Forecast Model METU - 2011 41

THANK YOU METU - 2011 42