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SUMMARY:Deep Learning-Enhanced Analytics on Collaborative Edge-Cloud - Mar
 yleen Ndubuaku
DTSTART:20200306T100000Z
DTEND:20200306T110000Z
UID:TALK140068@talks.cam.ac.uk
CONTACT:Prof Neil Lawrence
DESCRIPTION:Internet of Things (IoT) ubiquitous sensors and devices are ge
 nerating massive data streams continuously. These streams need to be proce
 ssed on-the-fly to extract knowledge for several applications like video s
 urveillance\, autonomous vehicles\, smart city\, web monitoring\, etc. The
  existing approach for data stream processing is designed for centralised 
 systems where all the data is sent to the data centres for storage and ana
 lytics. However\, it is often not feasible to migrate all the data to the 
 cloud for cost\, performance and privacy concerns. In distributed systems 
 like IoT networks\, other agents like end devices\, edge nodes\, and cloud
 lets can cooperatively participate in the processing pipeline. This talk w
 ill focus on the design and deployment of deep learning algorithms on dist
 ributed nodes to tackle the challenges of data stream processing in distri
 buted systems. We will explore how these algorithms can be optimised to me
 et system requirements in terms of scalability\, low-latency and resource 
 constraints. The potentials of deep autoencoders for data preprocessing on
  the edge using dimensionality reduction\, anomaly detection and clusterin
 g techniques will be presented.
LOCATION:FW26\, Computer Lab\, 15 JJ Thomson Avenue\, Cambridge
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