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.

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Critical infrastructure systems (CISs), such as power  grids, transportation systems, communication networks and water systems are the backbone of a country’s national security and industrial prosperity. These CISs execute large numbers of workflows with very high resource requirements that can span through different systems and last for a long time. The proper functioning and synchronization of these workflows is essential since humanity’s well-being is connected to it. Because of this, the challenge of ensuring availability and reliability of these services in the face of a broad range of operating conditions is very complicated.  This paper proposes an architecture which dynamically executes a scheduling algorithm using feedback about the current status of CIS nodes. Different artificial neural networks (ANNs) were created in order to solve the scheduling problem. Their performances were compared and as the main result of this paper, an optimal ANN architecture for workflow scheduling in CISs is proposed. A case study is shown for a meter data management system with measurements from a power distribution management system in Serbia. Performance tests show that significant improvement of the overall execution time can be achieved by ANNs.

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Abstract-This paper introduces a simulation environment developed for analyzing crowd-sensing based applications in the Smart City application domain. As a case study, an urban parking application scenario is investigated and presented. In this scenario, smart citizens collect and share parking related events, such as leaving or occupying a free parking spot. These events can be presented on a real-time city map and used in navigation software, thereby helping others alleviate parking related issues, such as the time spent while cruising for parking. The simulation environment, implemented in Java, allows to investigate and assess the critical user base of crowd-sensing based smart city applications, and the requisites (benefits, challenges) of introducing such applications. Our simulation results show that considerable gain (approx. 15%) can be achieved in the cruising time even with relatively low (30%) user base in a medium size city (Novi Sad, Serbia). Moreover, the proposed simulation environment can be used also in real field measurements by replacing/extending input data from real users.