  Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a
trending topic in the domain of ubiquitous computing. It has recently drawn
substantial attention of the smart cities and urban computing communities. In
fact, the built-in cameras of mobile devices are becoming the most common way
for visual logging techniques in our daily lives. MCS photo-based frameworks
collect photos in a distributed way in which a large number of contributors
upload photos whenever and wherever it is suitable. This inevitably leads to
evolving picture streams which possibly contain misleading and redundant
information that affects the task result. In order to overcome these issues, we
develop, in this paper, a solution for selecting highly relevant data from an
evolving picture stream and ensuring correct submission. The proposed
photo-based MCS framework for event reporting incorporates (i) a deep learning
model to eliminate false submissions and ensure photos credibility and (ii) an
A-Tree shape data structure model for clustering streaming pictures to reduce
information redundancy and provide maximum event coverage. Simulation results
indicate that the implemented framework can effectively reduce false
submissions and select a subset with high utility coverage with low redundancy
ratio from the streaming data.
