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.
Nowadays, when Future Internet and Internet of Things (IoT) research is an integral part of the rise of computing, we need to react to new challenges. According to prognoses of Gartner and ABI Research, by 2020 there will be nearly 30 billion devices connected to the Internet of Things wirelessly. Furthermore, a new computing concept has appeared which had a vision where computing is made to appear everywhere and anywhere. These technological advancements that have occurred during the past decade in various domains, including sensors, wireless communications, location positioning technologies, and the web, allow the efficient collection and processing of a wide range of data. These ideas are applied at our University Campus where we established an extensible data management architecture, on the top of which value-added services are provided for various people living or working on the Campus. Both the architecture and a couple of applications that make use of it will be discussed.
The technological advancements that have occurred during the past decade in various domains, including sensors, wireless communications, location positioning technologies and the web, allow the collection of a wide range of data. Possible sources of that data include intelligent devices (smartphones, tablets, etc.) containing various sensors, Web pages, and social networking sites. Collected data are subject to analysis (using data mining or pattern recognition approaches, for instance) and after processing new content might be inferred. This is a value-added service that can itself be used as a data source. In this paper, we use our University Campus as an example for establishing a data management architecture that integrates into a more general, extensible publish/subscribe based model of crowdsourced applications.
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