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

There has been many advancements in Semantic Sensor Web Technologies (SSW), and as a result, SSW is utilized in various fields such as 1) Agriculture 2) Catastrophe Management 3) Building Management 4) Laboratory Management, and more.

Agriculture

Monitoring various environmental attributes are 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 on Australian farms where temperature, humidity, barometric pressure, wind speed, and wind direction, in addition to 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. The goals of this research are to set thresholds to receive alerts in case environmental attributes exceed defined limits.

Catastrophe management

Natural catastrophes, as earthquakes, tsunami, heavy rainstorms, take many people’s lives all around the world. Fortunately, indicators exist that measure the occurrence and circumstances of these kinds of disasters, by which centralizing data can analyze.

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. Although the need for such toola was realized a long time ago, only recently through creation of SSW has the ability to get notifications become possible. There have been many advancements in the SSW field, especially in the building management industry, such as getting notification when water leaks, controlling apartment temperature via smartphone, and more [4]. Below is an example of SSW architecture that could be utilized for managing industrial plants.

Laboratory Management: Managing laboratory tests can be quite challenging, especially in areas where longevity tests are performed. This sorts of test could be a creep test for a material, reaction test of a certain chemical or wireless transmission test of a circuit. Managing tests can be quite difficult in complex infrastructures where many tests occurs at the same time. Moreover, managing tests across multiple locations can also be quite challenging. Major advancements in SSW allow for real-time monitoring of laboratory variables via utilization of sensors. The major advancement is this field through introduction of SSW is to have dependable alerts that 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.

Future direction

The future direction of Semantic Sensor Web is toward having a common and standardized language and tags, where information about every sensor can be easily interpreted by various web applications. In more detail, the vision of Semantic Sensor Web is to marry physical sensors and Semantic web technologies and enabling the encoding of sensor description and data with languages standardized by semantic web. Ontologies and other semantic technologies can be potentially the missing bridge between sensor network and web because they can be utilized to improve semantic integration and interoperability. The future Semantic Sensor Web allows the data to be organized, managed, shared, analyzed, and controlled easily. Furthermore, currently real-time extension of the SSW is under development named as sensor wiki. The motivation behind this development is to allow real-time access of the physical world. This would allow for users to be able to surf through their community physical sensors.