Abstract:
Mobile crowd sensing is human centric computing technology with sensing and computing
devices in the mobile users. The tendency to the recent technology, the smartphone user rapidly
increases from day to day. This provides the good opportunity to sense with smartphone about
the environment and urban events without deploying special sensor devices. There are numbers
of application and data uploading models that motivates users to contribute data by using their
smartphones. Recently, mobile crowd sensing (MCS) architecture have two components in
sensing and uploading architecture, those are, sensing devices that receive information form
environment and back-end server for analysis of sensor data. However, each data contributing
user collect and send the redundant sensed data to the cloud server. There are no common
devices or mechanism that faces the common challenges such as resource allocation, data
collection and data processing tasks. In this paper, we designed a model for improvement of
network efficiency in mobile crowd-sensing by providing device to device communication with
their group leader and data fusion before uploading to the backend server. We conducted
simulation with NS2 network simulator for experimentation and to evaluate the performance of
our model versus raw data uploading model with different evaluation metrics. The result show
that our scenario model improves the performance and reduce the data redundancy in mobile
crowd sensing network.
