Ubiquitous computing is giving rise to applications that interact very closely with activity in the real world, usually involving instrumentation of environments. In contrast, we propose Cooperative Artefacts that are able to cooperatively assess their situation in the world, without need for supporting infrastructure in the environment. The Cooperative Artefact concept is based on embedded domain knowledge, perceptual intelligence, and rule-based inference in movable artefacts. We demonstrate the concept with design and implementation of augmented chemical containers that are able to detect and alert potentially hazardous situations concerning their storage.
Load sensing is a mature and robust technology widely applied in process control. In this paper we consider the use of load sensing in everyday environments as an approach to acquisition of contextual information in ubiquitous computing applications. Since weight is an intrinsic property of all physical objects, load sensing is an intriguing concept on the physical-virtual boundary, enabling the inclusive use of arbitrary objects in ubiquitous applications. In this paper we aim to demonstrate that load sensing is a versatile source of contextual information. Using a series of illustrative experiments we show that using load sensing techniques we can obtain not just weight information, but object position and interaction events on a given surface. We describe the incorporation of load-sensing in the furniture and the floor of a living laboratory environment, and report on a number of applications that use context information derived from load sensing.
Abstract. An increasing amount of valuable data sources, advances in Internet of Things and Big Data technologies as well as the availability of a wide range of machine learning algorithms offers new potential to deliver analytical services to citizens and urban decision makers. However, there is still a gap in combining the current state of the art in an integrated framework that would help reducing development costs and enable new kind of services. In this chapter, we show how such an integrated Big Data analytical framework for Internet of Things and Smart City application could look like. The contributions of this chapter are threefold: (1) we provide an overview of Big Data and Internet of Things technologies including a summary of their relationships, (2) we present a case study in the smart grid domain that illustrates the high-level requirements towards such an analytical Big Data framework, and (3) we present an initial version of such a framework mainly addressing the volume and velocity challenge. The findings presented in this chapter are extended results from the EU funded project BIG and the German funded project PEC.
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Abstract. We propose a lightweight localisation approach for supporting distance and range queries in ad hoc wireless sensor networks. In contrast to most previous localisation approaches we use a distance graph as spatial representation where edges between nodes are labelled with distance constraints. This approach has been carefully designed to satisfy the requirements of a concrete application scenario with respect to the spatial queries that need to be supported, the required accuracy of location information, and the capabilities of the target hardware. We show that this approach satisfies the accuracy requirements of the example application using simulations. We describe the implementation of the algorithms on wireless sensor nodes.
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