Optimizing cloud based video crowdsensing +91-9994232214,7806844441, ieeeprojectchennai@gmail.com, www.projectsieee.com, www.ieee-projects-chennai.com IEEE PROJECTS 2016-2017 ----------------------------------- Contact:+91-9994232214,+91-7806844441 Email: ieeeprojectchennai@gmail.com

1. Optimizing Cloud-Based Video Crowdsensing
Abstract:
Wearable and mobile devices are widely used for crowdsensing, as they come
with many sensors and are carried everywhere. Among the sensing data, videos
annotated with temporal–spatial metadata contain huge amount of information,
but consume too much precious storage space. In this paper, we solve the
problem of optimizing cloud-based video crowdsensing in three steps. First, we
study the optimal transcoding problem on wearable and mobile cameras. We
propose an algorithm to optimally select the coding parameters to fit more videos
at higher quality on wearable and mobile cameras. Second, we empirically
investigate the throughput of different file transfer protocols from wearable and
mobile devices to cloud servers. We propose a real-time algorithm to select the
best protocol under diverse network conditions, so as to leverage the intermittent
WiFi access. Last, we look into the performance of cloud databases for sensor-
annotated videos, and implement a practical algorithm to search videos
overlapping with a target geographical region. Our measurement study on three
popular opensource cloud databases reveals their pros and cons. The three
proposed algorithms are evaluated via extensive simulations and experiments.
The evaluation results show the practicality and efficiency of our algorithms and
system. For example, our proposed transcoding algorithm outperforms existing
approaches by 12 dB in video quality, 87% in energy saving, and one-quarter in
delivery delay. Another example is, by intelligently choosing a

2. proper cloud database, our system may reduce the insertion time by up to one-
third, or the lookup time by up to one-fourth.