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The recent years have seen a proliferation of community sensing or participatory sensing paradigms, where individuals rely on the use of smart and powerful mobile devices to collect, store and analyze data from everyday life. Due to this massive collection of the data, a key challenge to all such developments, is to provide a simple but efficient way to facilitate the programming of distributed applications on the embedded devices. We will demonstrate a novel system that provides a principled approach to developing distributed data clustering applications on networks of smartphones and other mobile devices. The system comprises three components: (a) a distributed framework, implemented on mobile phones that eases the programmability and deployment of applications on the devices using simple programming primitives, (b) a data gathering component that tracks the movement of wireless device users and collects sensor data (i.e., GPS and accelerometer sensor data), and (c) a distributed data clustering algorithm that allows users to combine their individual data, that is distributed and energy efficient. Using a road traffic monitoring application we demonstrate how MISCO can efficiently identify anomalies in the road surface conditions and illustrate that our system is practical and has low energy and resource overhead.