  The Internet of Things (IoT) envisions billions of sensors deployed around us
and connected to the Internet, where the mobile crowd sensing technologies are
widely used to collect data in different contexts of the IoT paradigm. Due to
the popularity of Big Data technologies, processing and storing large volumes
of data has become easier than ever. However, large scale data management tasks
still require significant amounts of resources that can be expensive regardless
of whether they are purchased or rented (e.g. pay-as-you-go infrastructure).
Further, not everyone is interested in such large scale data collection and
analysis. More importantly, not everyone has the financial and computational
resources to deal with such large volumes of data. Therefore, a timely need
exists for a cloud-integrated mobile crowd sensing platform that is capable of
capturing sensors data, on-demand, based on conditions enforced by the data
consumers. In this paper, we propose a context-aware, specifically, location
and activity-aware mobile sensing platform called C-MOSDEN ( Context-aware
Mobile Sensor Data ENgine) for the IoT domain. We evaluated the proposed
platform using three real-world scenarios that highlight the importance of
'selective sensing'. The computational effectiveness and efficiency of the
proposed platform are investigated and is used to highlight the advantages of
context-aware selective sensing.
