A website to unite some of the work being done on Linked Data Analytics for the Internet of Things.
The Internet of Things is currently beset by product silos. To unlock the commercial potential there is a need for open ecosystems based upon open standards. This includes standards for identification, discovery andinteroperation of services across platforms from different vendors, and will involve the need for rich descriptions and shared data models, as well as close attention to security, privacy, scalability and accessibility.
WEB OF THINGS AT W3C
Applications and their utility to end users have been known to drive the adoption of technology just as with the iPhone and data services. Before the iPhone, data services had infrastructure in place but low adoption.
For applications in the IoT to demonstrate the benefits of interconnecting entities and their data, there exist the challenges of aggregating & integrating multiple sources of data to derive insight and foresight from the data and creating an environment where entities can inter-operate and share data.
Hence the need to perform analytics on IoT data that uses open standards to model and describe the data for integration. Furthermore, this has to be performed on a platform suitable for the distributed, heterogeneous nature of IoT deployments.
Lightweight computers, like the Raspberry Pi, are cost efficient and compact enough for broad or mobile deployments but possess significant storage and compute capabilities unlike sensors.
In IoT scenarios like Smart Homes, Smart Offices and Smart Cities, Lightweight Computers can serve as local platforms to perform integration and aggregation of data for analytics and applications. An additional benefit is fine-grained control over data privacy, protection and physical access by device owners.
Linked Data, which semantically interconnects structured data, has demonstrated its feasibility as a means of connecting and integrating web data using current infrastructure. ‘Things’ in the IoT, however, produce data that is markedly different and unique from Web data. This has to be handled differently to achieve good performance for analytics on the limited resources of Lightweight Computers.
The following projects, publications and research go towards this effort to provide powerful Linked Data solutions on distributed Lightweight Computers that allow data analytics for applications on the Internet of Things.