Abstract-Ubiquitous and pervasive networks and applications include a growing number of research themes. In most use cases, applications require location information to interpret their environment and behave accordingly. In this paper, location algorithms and positioning methods that can be used for wireless geolocation are presented.

The term "disaster management" comprises both natural and man-made disasters. Highly pervaded with various types of sensors, our environment generates large amounts of data. Thus, big data applications in the field of disaster management should adopt a modular view, going from a component to nation scale. Current research trends mainly aim at integrating component, building, neighborhood and city levels, neglecting the region level for managing disasters. Current research on big data mainly address smart buildings and smart grids, notably in the following areas: energy waste management, prediction and planning of power generation needs, improved comfort, usability and endurance based on the integration of energy consumption data, environmental conditions and levels of occupancy. This paper aims presenting a systematic literature review on the applications of big data in disaster management. The paper will first presents the visual definition of disaster management and describes big data; it will then illustrate the findings and gives future recommendations after a systematic literature review.

As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches

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