Smart cities offer services to their inhabitants which make everyday life easier beyond providing a feedback channel to the city administration. For instance, a live timetable service for public transportation or real-time traffic jam notification can increase the efficiency of travel planning substantially. Traditionally, the implementation of these smart city services require the deployment of some costly sensing and tracking infrastructure. As an alternative, the crowd of inhabitants can be involved in data collection via their mobile devices. This emerging paradigm is called mobile crowd-sensing or participatory sensing. In this paper, we present our generic framework built upon XMPP (Extensible Messaging and Presence Protocol) for mobile participatory sensing based smart city applications. After giving a short description of this framework we show three use-case smart city application scenarios, namely a live transit feed service, a soccer intelligence agency service and a smart campus application, which are currently under development on top of our framework.
In the last few years semantic aspects earned more and more interest and have ever more applications. Ontologies and semantic frameworks are started applied more fields than we could imagine. Nowadays, when Future Internet and Internet of Things (IoT) research became an integral part of the rise of computing, we need to react to new challenges utilizing these aspects. Our University is not an exception, it is a perfect place to study and apply these possibilities. Furthermore, a new computing concept has appeared which had a vision where computing is made to appear everywhere and anywhere. These technological advancements made possible to collect and process a wide range of data. Several ideas are applied at our University Campus where we established a framework which could provide value-added services for various people living or working on the Campus.The main goals of our project -inside the Future Internet field -are twofold. First, establishing an intelligent platform as an extensible architecture which is used to drive the central part (as we call Smart Campus Central Intelligence -SCCI) through an extensible and open interface with data. That data is used not only for data analysis but recommendations as well. Second, the interface should be open for the crowd to made available crowdsourcing inside the system for creating new services. This paper highlights our applied usage scenarios and our vision where we could continue.
The representative-based approximation has been widely studied in rough set theory. Hence, rough set approximations can be defined by the system of representatives, which plays a crucial role in set approximation. In the authors' previous research a possible use of the similarity-based rough set in first-order logic was investigated. Now our focus has changed to representative-based approximation systems. In this article the authors show a logical system relying on representative-based set approximation. In our approach a three-valued partial logic system is introduced. Based on the properties of the approximation space, our theorems prove that in some cases, there exists an efficient way to evaluate the first-order formulae.
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