'What affects me?': a smart public alert system based on stream reasoning

Public alert services is gradually becoming popular in smart cities because this enhances the awareness of the citizen about activities within the city. Such a service also ensures the safety and security of the citizens. However the state of the art lacks in providing real-time alerts in a personalized, context-aware fashion utilizing the combined knowledge about the city, its events and its citizens. In this paper, a solution architecture is presented that uses stream reasoning as its backbone which suits the domain of a public alert system very well. The stream reasoner uses rule-based reasoning and queries. The rules are designed as atomic concepts. A fully functional prototype of the proposed system was developed and tested on data of a smart city. The experimental results support that the proposed methodology is very effective.

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