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Semantic Sensor Web

Semantic Sensor Web is a computer science topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Semantic Sensor Web rather than just read about it. In short: The Semantic Sensor Web (SSW) is a marriage of sensor web and semantic Web technologies. The encoding of sensor descriptions and sensor observation data with Semantic Web languages enables more expressive representation, advanced access, and formal analysis of sensor resources.

Key takeaways

  • Semantic Sensor Web belongs to computer science; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Semantic Sensor Web to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Semantic Sensor Web from memory before moving on to harder problems.

Reference excerpt

The Semantic Sensor Web (SSW) is a marriage of sensor web and semantic Web technologies. The encoding of sensor descriptions and sensor observation data with Semantic Web languages enables more expressive representation, advanced access, and formal analysis of sensor resources. The SSW annotates sensor data with spatial, temporal, and thematic semantic metadata. This technique builds on current standardization efforts within the Open Geospatial Consortium's Sensor Web Enablement (SWE) and extends them with Semantic Web technologies to provide enhanced descriptions and access to sensor data.

Semantic modeling and annotation of sensor data Ontologies and other semantic technologies can be key enabling technologies for sensor networks because they will improve semantic interoperability and integration, as well as facilitate reasoning, classification and other types of assurance and automation not included in the Open Geospatial Consortium (OGC) standards. A semantic sensor network will allow the network, its sensors and the resulting data to be organised, installed and managed, queried, understood and controlled through high-level specifications. Ontologies for sensors provide a framework for describing sensors. These ontologies allow classification and reasoning on the capabilities and measurements of sensors, provenance of measurements and may allow reasoning about individual sensors as well as reasoning about the connection of a number of sensors as a macroinstrument. The sensor ontologies, to some degree, reflect the OGC standards and, given ontologies that can encode sensor descriptions, understanding how to map between the ontologies and OGC models is an important consideration. Semantic annotation of sensor descriptions and services that support sensor data exchange and sensor network management will serve a similar purpose as that espoused by semantic annotation of Web services. This research is conducted through the W3C Semantic Sensor Network Incubator Group (SSN-XG) activity.

W3C Semantic Sensor Networks The World Wide Web Consortium (W3C) initiated the Semantic Sensor Networks Incubator Group (SSN-XG) to develop the Semantic Sensor Network (SSN) ontology, intended to model sensor devices, systems, processes, and observations. The Incubator Group later transitioned into the Semantic Sensor Networks Community Group. It was then picked up in the joint OGC and W3C Spatial Data on the Web Working Group and published as a W3C Recommendation. The Semantic Sensor Network (SSN) ontology enables expressive representation of sensor observations, sampling, and actuation. The SSN ontology is encoded in the Web Ontology Language (OWL2). A number of projects have used it for improved management of sensor data on the Web, involving annotation, integration, publishing, and search.

Context Sensors around the globe currently collect avalanches of data about the world. The rapid development and deployment of sensor technology is intensifying the existing problem of too much data and not enough knowledge [1] Archived 2013-01-03 at the Wayback Machine. With a view to alleviating this glut, sensor data can be annotated with semantic metadata to increase interoperability between heterogeneous sensor networks, as well as to provide contextual information essential for situation awareness. Semantic web techniques can greatly help with the problem of data integration and discovery as it helps map between different metadata schema in a structured way.

Uses Semantic Sensor Web (SSW) technologies are utilized in fields such as agriculture, disaster management, building management and laboratory management.

Agriculture Monitoring various environmental attributes is critical to the growth of plants. Environmental attributes that are critical for growers are mainly temperature, moisture, pH, electric conductivity (EC), and more. Real-time monitoring in addition to setting alerts for the mentioned sensors was never possible. With the creation of SSW, growers can now track their plant growing conditions in real-time. An example of such advancement in agriculture through utilization of SSW is the research conducted in 2008 on Australian farms where temperature, humidity, barometric pressure, wind speed, wind direction and rainfall were monitored using SSW methodology. The architecture of this research project consists of personal integration needs, Semantic web, and more in addition to semantic data integration, i.e. where data is centralized to make sensor semantic web technologies meaningful and useful.

Building management (smart buildings) Managing buildings can be quite sophisticated, as the cost of fixing damages is significantly higher than having proper monitoring tools in place to prevent damages from happening. SSW allows for getting notified of water leaks, controlling apartment temperature via smartphone, and more.

Laboratory management Managing laboratory tests can be quite challenging, especially if tests take place over a long time, across multiple locations, or in infrastructures where many tests occur. Such tests include creep tests for a material, reaction tests of a certain chemical or wireless transmission tests of a circuit. Advancements in SSW allow for real-time monitoring of laboratory variables via sensors. Such sensors can take more than one factor into consideration before alerting. For example, an alert can go off when pressure and temperature both exceed a certain limit, or an alert can go off when pressure in one building drops, yet pressure in another building remains the same.

Notable contributions Standardization is a lengthy and difficult process, as players in a field that have existing solutions would see any standardization as an additional cost to their activities. Open Geospatial Consortium (OGC), an international voluntary consensus standards organization that was founded in 1994, is making efforts to enhance and accelerate the growth of the SSW community and standardize sensor information across web. Most OGC standards depend on generalized architecture that is collectively captured in set of documents. The goal of OGC is to provide enhancements in description and meaning of sensor data. Also, OGC had enabled Sensor Web communication. OGC is in charge of creating open geospatial standards. Moreover, OCG is supported by industry, government, and academic partners to allow for easy creation of geo-processing technologies known as “plug and play”.

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Semantic Sensor Web

Start with the simplest possible case. Write down what Semantic Sensor Web claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer science, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Semantic Sensor Web before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Semantic Sensor Web ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Semantic Sensor Web

In research
Semantic Sensor Web appears in computer science research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Semantic Sensor Web in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Semantic Sensor Web is common in secondary-school and first-year university syllabi. It links to neighbouring topics Information science, Semantic Web, Sensor networks, so understanding it makes those chapters shorter.
In everyday life
Look for Semantic Sensor Web outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Semantic Sensor Web in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Semantic Sensor Web means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Semantic Sensor Web out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Semantic Sensor Web in simple terms?

The Semantic Sensor Web (SSW) is a marriage of sensor web and semantic Web technologies. The encoding of sensor descriptions and sensor observation data with Semantic Web languages enables more expressive representation, advanced access, and formal analysis of sensor resources.

Why does Semantic Sensor Web matter?

Because it connects several computer science ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Semantic Sensor Web?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Semantic Sensor Web.

Tags

  • Information science
  • Semantic Web
  • Sensor networks
  • Sensors

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