Semantic Sensor Web Advanced

Semantic Sensor Web technologies enable meaningful information representation of acquired data by means of standardizing the description of sensor data and enhancement of data format for the goal of creating a web centric information infrastructure where sensor data can be collected, visualized, manipulated, retrieved, and shared in a web environment. This ability enables the SSW community to have access to structured data where they can build applications to better analyze data. Overall, standardization is key to excellence in new technology, and SSW community is enabled for SSW standardization movement. Moreover, meaningful annotation is critical to interpretation of data, since it provides the structural information required to understand platforms. Meaningful semantic annotation is possible by standardizing SSW formats. There are various languages utilized for annotating sensor data; RDFa, XLink, and SAWSDL (Semantic Annotations for WSDL and XML Schema) are common languages utilized for annotating sensor data.

Major advancements in Semantic Sensor Web field

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”.

Current challenges

Current challenges in the SSW field include a lack of standardization, which slows down the growth rate of sensors created to measure things. For the semantic sensor web to be meaningful, the languages, tags, and labels across various applications, developed by various developers, must be the same. Unfortunately, due to scattered development of various architectures, such standardization is not possible. This problem is called vastness.

There is also the problem of inconsistency, such that when changing the architecture of an existing solution, the system logic will no longer hold. In order to resolve this problem, there is a need for an extensive amount of resources (depending on the size and complexity of system). For example, many existing systems use twelve bits to transfer temperature data to a local computer. However, in a SSW 16 bits of data is acceptable. This inconsistency results in higher data traffic with no additional accuracy improvement. In order for the old system to improve, there is a need of allocating extra bits and changing the buffer requirements, which is costly. Assuming the resources required to make the tag requirement are available, there is still the existence of unnecessary data that requires additional storage space in addition to creating confusion for other SSW members. The only solution remaining is changing the hardware requirements, which requires a lot of resources.