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RDF is Critical to a Successful Internet of Things

By   /  May 29, 2014  /  No Comments

Depiction of RDF and the internet of ThingsDo you still remember a time when a utility company worker came to your house to check your electric meter? For many of us already, this is in the past. Smart meters send information directly to the utility company and as a result, it knows our up-to-the-minute power usage patterns. And, while we don’t yet talk to our ovens or refrigerators through the Internet, many people routinely control thermostats from their smart phones. The emerging Internet of Things is real and we interact with it on the daily basis.

The term Internet of Things refers to devices we wouldn’t traditionally expect to be smart or connected, such as a smoke detector or other home appliance. They are being made ‘smart’ by enabling them to send data to an application. From smart meters to sensors used to track goods in a supply chain, the one thing these devices have in common is that they send data – data that can then be used to create more value by doing things better, faster, cheaper, and more conveniently.

The physical infrastructure needed for these devices to work is largely in place or being put in place quickly. We get immediate first order benefits simply by installing new equipment. For example, having a smart meter provides cost savings because there is no need for a person to come to our houses. Similarly, the ability to change settings on a thermostat remotely can lower our heating costs. However, far vaster changes and benefits are projected or are already beginning to be delivered from inter-connecting the data sent by smart devices:

  • Health: Connecting vital measurements from wearable devices to the vast body of medical information will help to improve our health, fitness and, ultimately, save lives.
  • Communities: Connecting information from embedded devices and sensors will enable more efficient transportation. When a sprinkler system meter understands weather data, it will use water more efficiently. Once utilities start connecting and correlating data from smart meters, they might deliver electricity more efficiently and be more proactive in handling infrastructure problems.
  • Environment: Connecting readings from fields, forests, oceans, and cities about pollution levels, soil moisture, and resource extraction will allow for closer monitoring of problems.
  • Goods and services: Connecting data from sensors and readers installed throughout factories and supply chains will more precisely track materials and speed up and smooth out the manufacture and distribution of goods.

Connecting this diverse data, however, presents an ever-growing challenge. Most organizations are finding that ad hoc data integration has reached its limits – especially with the emergence of big data and the growing need to harness a wide variety of data. The greater potential of the Internet of Things will not be achieved without standards-based vocabularies and reference data that can be mapped and connected to each other. XML Schemas have been providing such standards with many local vocabularies standardized by specific communities including commerce,   weather and smart grid.

There is no shortage of different standards, and this is understandable. Different communities have differing perspectives and needs. The problem is that these standards remain disconnected and usable only in limited areas of application with no practical way to re-use or connect common elements. This means that data based on the standards is not easily connectable either. While XML Schemas can enforce structural adherence of data to a specific standard, connecting, interlinking and re-using data across them is not their strong point. But even a single area of application such as the smart grid spans multiple domains from electric power generation to distribution, end-use devices and information technology.

This is where RDF-based technologies come in. Their strength is in connectivity. Smaller, modular vocabularies from different sources can be combined, linked, expanded and built upon to create information models for different domains. Unlike UML, these models are directly usable and query-able and can include reference data. Since models and data use the same data model, they can be queried with the same query language – SPARQL. Each entity has a unique global identifier or URI on the web. As a result, diverse data from different applications can be brought together easily. With the semantics of data directly accessible to applications, each can interpret merged data according to its needs.

One of the early adaptors of this approach is EPIM, a consortium of the oil and gas producers on the Norwegian Continental Shelf. After successfully deploying RDF-based solutions for the exchange and reporting of exploration, production and environmental data, EPIM is developing a LogisticsHub. With it, containers and loading equipment participate in the Internet of Things using smart devices to transmit supply chain information.

In the US, the Veterans Administration is looking at using RDF to integrate health-related observations from medical devices and sensors. The smart grid community is exploring RDF as a way to interoperate across different standards used to produce and distribute electrical power – from generation facilities, through the high-voltage transmission network and the distribution system, to industrial users and building automation systems, to energy storage installations and to end-use consumers and their thermostats, electric vehicles, appliances and other household devices.

So, even in this era of big data, when there is a growing awareness of the value of interconnecting a mind boggling array of repositories and streams of data of many kinds, one might say: “You ain’t seen nothing yet.”  Just wait until the Internet of Things takes hold and really expands. The many purposes and value of interconnecting data will grow accordingly, along with an understanding of the potential of RDF and semantic web technologies to meaningfully integrate information.

About the Author

Photo of Irene PolikoffIrene Polikoff has more than two decades of experience in software development, management, consulting and strategic planning. Since co-founding TopQuadrant in 2001 Irene has been involved in more than a dozen projects in government and commercial sectors. She has written strategy papers, trained customers on the use of the Semantic Web standards, developed ontology models, designed solution architectures and defined deployment processes and guidance.

Before starting TopQuadrant Irene was a Principal in the national Knowledge, Content Management and Portals Practice in IBM Global Services. Prior to that she was a Senior Development Manager and a Project Executive for IBM worldwide consultant’s tools and methods. Prior to IBM, Ms. Polikoff held IT management positions at Fortune 500 companies where she was responsible for development and deployment of enterprise-wide mission critical information systems. Irene has a Masters degree in Operations Research from Columbia University. She is co-author of a recently published book on software requirements and architecture – “Capability Cases: Solution Envisioning Approach”.

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