Kent Gordon Lindquist

Accessing sensor data using meta data: a virtual object ring buffer framework

With the proliferation of sensors it is becoming increasingly difficult to discover and access sensor data of interest. Currently, most researchers and sensor data users access data from sensors that they build by themselves or from known sensor network run by their friends; rarely do they try to find sensor data of interest that are maintained by other users and groups. Even if they are able to find these data streams, accessing them is rather difficult because of login requirements in remote systems as well as diversity in protocols needed to access and interpret them. In our project, ROADNet, we have defined a novel way of discovering and accessing real-time data from sensor networks. Our approach is to abstract sensor streams and define systemic, descriptive and structural meta data properties for them in a catalog. Then a user can easily discover sensors of interest from the catalog, and get access to their streams. We describe a system that has more than 20,000 multi-disciplinary sensor data streams that are discovered/accessible using this approach.

ROADNet: A network of SensorNets

As sensor networks become denser and more widely deployed, the potential develops for interconnecting these networks to combine datasets, share technological solutions, and to conduct cross-disciplinary research and monitoring operations that rely on several signal domains simultaneously. To that end, the real-time observatories, applications and data management network (ROADNet) research project is connecting multiple sensor networks deployed by collaborating research projects into a single network in order to support a variety of research topics including coastal ocean observing, microclimatology and seismology. This paper gives a brief overview of the ROADNet project and discusses some of the implementation challenges we uncovered while building and maintaining the ROADNet system. We encountered challenges on several fronts including building effective programming abstractions for sensor networks, building tools for managing large-scale data in a scalable manner, and building efficient tools for deploying and managing hundreds of sensors. We discuss how these challenges were addressed and some of the lessons learned from collaborations with domain scientists using our network to conduct their research