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Cooperative Video Processing in Multimedia IoT Systems

Imagine the Possibilities

Usually videos are captured by surveillance cameras in multimedia IoT systems. These are then sent to analyze by remote IoT servers. As the network bandwidth is limited, the transmission may cause congestions and delays if the distance is long and the volume of video chunks is large. Modern mobile devices, such as smart phones and tablets etc, are resource-abundant in computation and communication capabilities. These devices can be used to extract features from videos for the remote IoT servers. We can avoid the bandwidth starvation of delivering original video chunks by sending back only a few video features to the remote servers.

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A Challenging Project

Sharing My Work with Others

I have always been intrigued by the subject of this project, and wanted to explore it further. Taking on this task gave me the opportunity to conduct serious research and produce high-quality work. Moreover, while documenting the entire process, I learned a lot about various methodologies and testing techniques. I’m thrilled about the result and look forward to the next challenge. 

We've made a minor change in the initial project plan. Here is the updated timeline of the project:


Task                                                                               Completion Date 

Initial research                                                                     15-Jun

Data preparation and network topology design              22-Jun

Simulation tool set-up for the experiment                        24-Jun

Parameter tuning and evaluation                                       27-Jun

Presentation/demo                                                              29-Jun

Final report                                                                           06-July

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Initial research

Related work

1. Edge computing framework for cooperative video processing in multimedia IoT systems
2. Energy-delay-cost Tradeoff for Task Offloading in Imbalanced Edge Cloud-Based Computing
3. A cooperative computing system for heavy-computation and low-latency processing in wireless sensor networks
4. Social-aware video multicast based on device-to-device communications
5. SaW: video analysis in social media with web-based mobile grid computing
6. Coordinating distributed algorithms for feature extraction offloading in multi-camera visual sensor networks

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Project Video

Watch Highlights of My Work

Here is the video presentation of my course project. Please feel free to comment, give suggestions in connex to improve the work.

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“What would life be if we had no courage to attempt anything?”

Vincent Van Gogh

Edge Computing
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Victoria, BC, Canada

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