Use cases

From Semantic Sensor Network Incubator Group
Revision as of 07:50, 20 January 2011 by Llefort (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

Use Cases

This page is being re-organised to provide a survey of the literature in relation to the use cases developed for the two deliverables defined in the charter on the sensor ontology and the semantic markup extension to SensorML-based services.

An iterative process has been used to identify and prioritize the use cases.

For the sensor ontology activity:

  • A preliminary list of use cases has been established and used to rank the use cases Favorite Use Cases List
  • Three categories of use cases have been short listed in the Reference Use Case List
  • Each category will be further described in a separate wiki page at a general level and with more detailed information for a selected sub-use case
  • These categories are:
    • Device discovery: Find device(s) that meet given criteria
    • Data discovery: Find data meeting certain criteria (e.g. temp + geo-temporal constraints
    • Process/provenance: Describe and exploit information about how the data has been or can be collected to support other operation like composition of resources or diagnosis

The scope of the second activity is to extend the Sensor Markup Language (SML), one of the four SWE languages, to support semantic annotations of sensor descriptions and services that support sensor data exchange and sensor network management and will a serve similar purpose as that espoused by semantic annotation of Web services.

  • The RDF-ization of sensor descriptions or data to support the discovery of resources and the recording of provenance information thanks to the use of named relationships and Web-based URIs,
  • The creation of service annotations to enable the composition/mash up of individual sensor services and the creation of plans or workflow or macro-programs for sensor networks.

The wikipedia page on SensorML [1] states that:

SensorML provides standard models and an XML encoding for describing any process, including the process of measurement by sensors and instructions for deriving higher-level information from observations. It provides a provider-centric view of information in a sensor web, which is complemented by Observations and Measurements which provides a user-centric view.

Processes described in SensorML are discoverable and executable. All processes define their inputs, outputs, parameters, and method, as well as provide relevant metadata. SensorML models detectors and sensors as processes that convert real phenomena to data.

One of the challenges for the specification of the process/provenance use case is to cover not only the simple types of instrument management services allowing to access already sourced data or to acquire it according to a predefined workflow but also the more complex types of dynamically adaptive demand-driven data acquisition services.

Classification of Use cases

This classification is done in several steps:

  • Identification of sub-uses cases for the ontology viewpoint and the markup viepoint
  • Bundling of sub-use cases into a limited number project categories

This approach help to explain how these different use cases relate to each other:

The first table focuses on the discovery use cases. The different columns are used to specify the ontology modules which must be assembled together to cover the set of requirements for each type of discovery use case.

Reference Use cases vs. Ontology scope Sensor Data Sensor and Data Sensor, Data and Process
Sensor/Device discovery Sensor/Device Discovery (general, sensor type, functionality, host platform) Sensor/Device Method and Classification
Data discovery Data/Dataset Discovery (geospatiotemporal) Sensor/Device Scope (output/measurable properties/measurable phenomenon) Sensor/Device Scope and output API/format
Derived and Virtual Sensor Discovery Derived and Virtual Sensor/Device (and Sensors Networks) Discovery (Method, Scope, API/format)

Assumption: It is worth using a classifier to support discovery if and only the ontology enable reasoning over the three descriptive aspects (sensor, data and process)

The second table focuses on the provenance/process use cases. The different columns are used to specify specify the ontology modules which must be assembled together to cover the set of requirements for each type of provenance/process use case.

Reference Use cases vs. management level Process Sensor, Data and Process
Provenance and Diagnosis Discovery: control Context: provenance, operation: diagnosis
Lightweight (static) composition and mashups Operation: piping (predefined workflow) Derived/Programmable Sensors and Sensors Networks
Dynamic composition and tasking Operation: tasking (adaptive workflow) Virtual Sensors and Sensors Networks

Assumption (open to discussion): the process model is rich enough to be used on its own (or with a subset of the Sensor and Data ontologies) to control the sensor(s) operation(s).

The third table describes the bundles

Short name Long name Discovery category Process category Examples
Semantic Sensor/Obs Portals Semantically-enabled sensor and observation portal Discovery of Sensor, Data and Process/APIs Lightweight mashups, no possibility to command sensor, limited provenance support Pachube, OOI, Semantic SOS-es
Semantic Observatories Semantically-enabled virtual observatory Discovery of Sensor, Data and Process/API - Discovery of Derived Sensors Lightweight mashups and programmable sensors and sensor networks, rigorous provenance management, data quality evaluation and failure detection SensorPedia, Semantic SAS-es, Scientific workflows
Semantic Sensor Networks and Grids Semantically-enabled and programmable/adaptive sensors and sensor networks and grids Discovery of Sensor, Data and Process/API - Discovery of Virtual Sensors Dynamic composition and tasking of sensors and sensor networks (and models), rigorous provenance management, data quality evaluation and failure detection, adaptivity to faults and to real world events NCSA Virtual sensor (Liu et al.), GSN/Swiss experiment


The fourth table focuses on service annotations with the discovery use cases. The different columns are used to specify the annotations which must be present in a service description to cover the set of requirements for each type of discovery use case. The discovery of sensors and observations will likely occur through services. Annotation of these services will improve the process of discovery. There are four concepts defined within the OGC-SWE Sensor Observation Service GetCapabilities document that should be annotated to aid discovery of sensors and observations:

  • ObservationOffering - SOS organizes collections of related sensor systems into Observation Offerings. The concept of an Observation Offering is often equivalent to that of a sensor constellation.
  • Procedure - Method, algorithm or instrument (e.g., sensor).
  • ObservationProperty - An observable or phenomenon.
  • FeatureOfInterest - Represents the identifiable object(s) and event(s) on which the sensor systems are making observations (i.e, real-world entities, named locations, etc.).

The following table shows mapping of annotations to concepts in ontology.

Service Annotation Ontology Concept
ObservationOffering System
Procedure Device or Sensor (System?)
ObservationProperty Property
FeatureOfInterest Feature

The following table details which annotations are important for each use case.

Reference Use cases vs. Service Annotations Sensor Data Sensor and Data Sensor, Data and Process
Sensor/Device discovery ObservationOffering, Procedure, ObservationProperty, FeatureOfInterest ObservationOffering, Procedure, ObservationProperty, FeatureOfInterest ObservationOffering, Procedure, ObservationProperty, FeatureOfInterest
Data discovery ObservationProperty, FeatureOfInterest ObservationOffering, ObservationProperty, FeatureOfInterest ObservationOffering, ObservationProperty, FeatureOfInterest
Derived and Virtual Sensor Discovery ObservationOffering, Procedure, ObservationProperty, FeatureOfInterest

Classification of the published literature according to the sub-categories identified above

TO BE COMPLETED

From discovery to lightweight mashup and provenance

Reference Current status Goal
OntoSensor ( Goodwin and Russomanno) Semantic Sensor/Obs Portals
OOSTethys/Oceans IE (Bermudez et al.) Semantic Sensor/Obs Portals Semantic Observatories
MMI (Graybeal et al. ) Semantic Sensor/Obs Portals Semantic Observatories
SemSos (Henson et al.) Semantic Sensor/Obs Portals Semantic Observatories
CSIRO/IOOS and Hydro Sensor Web Semantic Sensor/Obs Portals Semantic Observatories
SemSorGrid4Env/Flooding use case Semantic Sensor/Obs Portals Semantic Observatories
University of Muenster (SIGI) Semantic Sensor/Obs Portals Semantic Observatories
S@ny Semantic Sensor/Obs Portals Semantic Observatories
Janowicz et al. (multiple papers) also Kuhn, Probst, Semantic Sensor/Obs Portals Semantic Observatories
Patrick Maue, Philippe Duchesne, and Sven Schade Semantic Sensor/Obs Portals Semantic Observatories

From lightweight mashup and provenance to composition and macro-programming

Reference Current status Goal
Ontology-driven Adaptive Sensor Networks (Avancha et al. 2004) Semantic Sensor Networks and Grids
SensorMasher (Le Phuoc, Hauswirth) Semantic Observatories Semantic Sensor Networks and Grids
SemSorGrid4Env/Fire detection use case (Semantic) Sensor Networks and Grids Semantic Sensor Networks and Grids
SENSEI (Barnaghi et al.) Semantic Sensor Networks and Grids Semantic Sensor Networks and Grids
CSIRO Weather Station and Plant/Ecological monitoring WSNs Semantic Sensor Networks and Grids
GSN (Swiss experiment) [1] Semantic Sensor Networks and Grids
NEESGrid Semantic Sensor Networks and Grids
VSTO and CEDAR Semantic Observatories Semantic Sensor Networks and Grids
SPASE Semantic Observatories Semantic Sensor Networks and Grids
Barseghian Semantic Observatories  ?
Liu et al 2009 TA-RDF NCSA [2] Semantic Observatories Semantic Sensor Networks and Grids
Calder et al. 2009 CESN Machine reasoning about anomalous sensor data Semantic Observatories Semantic Sensor Networks and Grids

Other projects

Reference Current status Goal
Machine reasoning about anomalous sensor data (Calder et al.) [3] Semantic Observatories (with provenance / diagnosis)
Mac Carthy et al. Reasoning-ready Sensor data Semantic Sensor/Obs Portals -
Optima/SensorMap (Kolli and Doshi) Semantic Sensor/Obs Portals  ?
ES3N (Lewis et al.) [4] (Semantic) Sensor Networks and Grids  ?
MeT (Kawashima et al.) [5] (Semantic) Sensor Networks and Grids  ?
Szekely and Torres 2004 [6] (Semantic) Sensor Networks and Grids  ?
Pedigree Ontology for Level-One Sensor Fusion (Matheus et al. 2005, Cerutti 2004) Semantic Observatories  ?
Semantic Agent Technologies for Tactical Sensor Networks (Jiang, Cybenko 2004) [7] (Semantic) Sensor Networks and Grids  ?
Wun et al Semantic Data Fusion in Sensor Networks (Semantic) Sensor Networks and Grids  ?

+ Other SSN06 and SSN09 papers

Identified Use Cases

Use Case List (survey/priority page)

  • List of use cases (1 line per use case) considered most important

Favorite Use Cases List