Abstract-Much effort has been spent on the optimization of sensor networks, mainly concerning their performance and power efficiency. Furthermore, open communication protocols for the exchange of sensor data have been developed and widely adopted, making sensor data widely available for software applications. However, less attention has been given to the interoperability of sensor networks and sensor network applications at a semantic level. This hinders the reuse of sensor networks in different applications and the evolution of existing sensor networks and their applications. The main contribution of this paper is an ontology-based approach and architecture to address this problem. We developed an ontology that covers concepts regarding examinations as well as measurements, including the circumstances in which the examination and measurement have been performed. The underlying architecture secures a loose coupling at the semantic level to facilitate reuse and evolution. The ontology has the potential of supporting not only correct interpretation of sensor data, but also ensuring its appropriate use in accordance with the purpose of a given sensor network application. The ontology has been specialized and applied in a remote patient monitoring example, demonstrating the aforementioned potential in the e-health domain.
Crucial to the success of Body Area Sensor Networks is the flexibility with which stakeholders can share, extend and adapt the system with respect to sensors, data and functionality. The first step is to develop an interoperable platform with explicit interfaces, which takes care of common management tasks. Beyond that, interoperability is defined by semantics. This paper presents the analysis, design, implementation and evaluation of a semantic layer within an existing BASN platform for the purpose of improving the semantic interoperability among sensor networks and applications. We adopt an ontology-based approach but rather than having a single overall ontology, we find that using clear semantic domains and mappings between them improves composability and reduces interoperability problems. We discuss the design choices and a reference implementation on an Android phone and actual sensor devices. We show by a qualitative evaluation that this semantic interoperability indeed provides significant improvements in flexibility.
Abstract-The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images.
Background The lack of knowledge about the intra- and interindividual attack frequency variability in chronic cluster headache complicates power and sample size calculations for baseline periods of trials, and consensus on their most optimal duration. Methods We analyzed the 12-week baseline of the ICON trial (occipital nerve stimulation in medically intractable chronic cluster headache) for: (i) weekly vs. instantaneous recording of attack frequency; (ii) intra-individual and seasonal variability of attack frequency; and (iii) the smallest number of weeks to obtain a reliable estimate of baseline attack frequency. Results Weekly median (14.4 [8.2–24.0]) and instantaneous (14.2 [8.0–24.5]) attack frequency recordings were similar (p = 0.20; Bland-Altman plot). Median weekly attack frequency was 15.3 (range 4.2–140) and highest during spring (p = 0.001) compared to the other seasons. Relative attack frequency variability decreased with increasing attack frequency (p = 0.010). We tabulated the weekly attack frequency estimation accuracies compared to, and the associated deviations from, the 12-week gold standard for different lengths of the observation period. Conclusion Weekly retrospective attack frequency recording is as good as instantaneous recording and more convenient. Attack frequency is highest in spring. Participants with ≥3 daily attacks show less attack frequency variability than those with <3 daily attacks. An optimal balance between 90% accuracy and feasibility is achieved at a baseline period of seven weeks. The ICON trial is registered in ClinicalTrials.gov under number NCT01151631.
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