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In this paper we focus on mobile crowdsensing applications for community sensing where sensors and mobile devices jointly collect and share data of interest to observe and measure phenomena over a larger geographic area. Such applications, e.g., environmental monitoring or crowdsourced traffic monitoring, involve numerous individuals that on the one hand continuously contribute sensed data to application servers, and on the other hand consume the information of interest to observe a phenomenon typically in their close vicinity. Energy-efficient and context-aware orchestration of the sensing process with data transmission from sensors through mobile devices into the cloud, as well as from the cloud to mobile devices such that information of interest is served to users in real-time, is essential for such applications, primarily due to battery limitations of both mobile devices and wearable sensors. In addition, the latency of data propagation represents their key quality measure from the user's perspective. Publish/subscribe middleware offers the mechanisms to deal with those challenges: It enables selective real-time acquisition and filtering of sensor data on mobile devices, efficient continuous processing of large data volumes within the cloud, and near real-time delivery of notifications to mobile devices. This paper presents our implementation of a publish/subscribe middleware system which is tailored to the requirements of mobile and resource-constrained environments with a goal to reduce the overall energy consumption in such environments, and proposes a general architecture for mobile crowdsensing applications. We demonstrate the usability of both the architecture and middleware through our application for air quality monitoring, and discuss the energy footprint of the proposed solution.