No abstract

There is an increasing trend to integrate sensor networks into the Internet, eventually resulting in an Internet of Things. Recent efforts of porting IPv6 to sensor networks turn sensor nodes into equitable Internet peers and RESTful Web Services on sensor nodes allow a distribution of the application logic among sensor nodes and more powerful Internet nodes. The touching point between a sensor network and the Internet is the gateway which translates between the link-layer protocols used in the Internet (Ethernet, Wi-Fi) and sensor networks (IEEE 802.15.4). So far, the functionality of those gateways was fixed and simple. We propose to turn these gateways into smart gateways by enabling them to execute application code. As only the gateway has full knowledge of and control over both the sensor network and the Internet, smart gateways can act as performance-enhancing proxies and intelligent caches to preserve the limited resources of the sensor network. Also, the smart gateway can perform application-specific protocol conversion between highly optimized but non-standard protocols in the sensor network and standardized, but less efficient protocols in the Internet. In this paper we present the design of a middleware for smart gateways that allows the execution of application code on the gateway by offering simplified interfaces to the sensor network and the Internet. We also report preliminary performance results for key functions of the middleware.

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL

Abstract-Billions of sensor (e.g., in mobile phones or tablet pcs) will be connected to a future Internet of Things (IoT), offering online access to the current state of the real world. A fundamental service in the IoT is search for places and objects with a certain state (e.g., empty parking spots or quiet restaurants). We address the underlying problem of efficient search for sensors reading a given current state -exploiting the fact that the output of many sensors is highly correlated. We learn the correlation structure from past sensor data and model it as a Bayesian Network (BN). The BN allows to estimate the probability that a sensor currently outputs the sought state without knowing its current output. We show that this approach can substantially reduce remote sensor readouts.

No abstract