For the Internet of Things to finally become a reality, obstacles on different levels need to be overcome. This is especially true for the upcoming challenge of leaving the domain of technical experts and scientists. Devices need to connect to the Internet and be able to offer services. They have to announce and describe these services in machine-understandable ways so that user-facing systems are able to find and utilize them. They have to learn about their physical surroundings, so that they can serve sensing or acting purposes without explicit configuration or programming. Finally, it must be possible to include IoT devices in complex systems that combine local and remote data, from different sources, in novel and surprising ways. We show how all of that is possible today. Our solution uses open standards and state-of-the art protocols to achieve this. It is based on 6LowPAN and CoAP for the communications part, semantic web technologies for meaningful data exchange, autonomous sensor correlation to learn about the environment, and software built around the Linked Data principles to be open for novel and unforeseen applications
Abstract. We show that for any α > 1 there exists a deterministic distributed algorithm that finds a fractional graph colouring of length at most α(∆ + 1) in any graph in one synchronous communication round; here ∆ is the maximum degree of the graph. The result is near-tight, as there are graphs in which the optimal solution has length ∆ + 1.The result is, of course, too good to be true. The usual definitions of scheduling problems (fractional graph colouring, fractional domatic partition, etc.) in a distributed setting leave a loophole that can be exploited in the design of distributed algorithms: the size of the local output is not bounded. Our algorithm produces an output that seems to be perfectly good by the usual standards but it is impractical, as the schedule of each node consists of a very large number of short periods of activity.More generally, the algorithm shows that when we study distributed algorithms for scheduling problems, we can choose virtually any tradeoff between the following three parameters: T , the running time of the algorithm, , the length of the schedule, and κ, the maximum number of periods of activity for a any single node. Here is the objective function of the optimisation problem, while κ captures the "subjective" quality of the solution. If we study, for example, bounded-degree graphs, we can trivially keep T and κ constant, at the cost of a large , or we can keep κ and constant, at the cost of a large T . Our algorithm shows that yet another trade-off is possible: we can keep T and constant at the cost of a large κ.
Abstract. Due to protocols such as 6LoWPAN and CoAP, wireless sensor nodes have become an integral part of the Internet. While this makes their data available on the Internet, it still remains a strictly devicecentric approach. However, one is usually interested in the phenomena observed by one or more devices and not in the sensors themselves. To bridge this gap, we introduce and evaluate the concept of Service-Level Semantic Entities (SLSE). They support the automatic semantic annotation of real-world objects based on networked sensor devices. SLSE continuously collect observations of sensors attached to some objects, and aggregates them to descriptions of the objects themselves. It then makes this augmented object data available as (semantic) linked data. This removes several layers of indirection in the semantic data, and allows to directly access properties of real-world objects.
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