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ENVISION SIGNED

Enabling Visual IoT Applications with Advanced Network Coding Algorithms

Total Cost €

0

EC-Contrib. €

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Partnership

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Project "ENVISION" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 195˙454 €
 EC max contribution 195˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2018
 Duration (year-month-day) from 2018-01-15   to  2020-01-14

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 195˙454.00

Map

 Project objective

The latest advances and integration of several key technologies such as wireless communications, low-power sensing, embedded systems, Internet protocols and cloud computing, have enabled the emergence of the Internet of Things (IoT) paradigm. However, the ever-growing deployment of the visual sensing applications within IoT deployments is expected to strain the network and cloud infrastructures used to deliver and store massive amounts of visual data. To partly address these challenges, prototypes of neuromorphic visual sensors, a.k.a. dynamic vision sensors (DVS), have been produced in the last two years. Instead of the conventional raster scan of video cameras, DVS devices record pixel coordinates and timestamps of reflectance events in an asynchronous manner, thereby offering substantial improvements in sampling speed and power consumption. ENVISION argues that, in order to fully exploit the advantages of neuromorphic sensing for IoT applications, such devices should be coupled with appropriate transmission and storage mechanisms that would take advantage of the visual data properties to achieve even higher bandwidth, power and storage efficiency. The ENVISION project aims at developing such data-driven delivery and storage algorithms based on advanced network coding techniques for data acquired by both conventional frame-based video cameras and DVS devices. Specifically, ENVISION is pursuing three interconnected research objectives: (i) designing advanced content-driven network codes for efficient transmission of the visual content captured by neuromorphic and conventional visual sensors to the cloud service under bandwidth and power constraints, (ii) developing novel content-aware network codes for storage of the visual content under the cost-performance optimisation framework, and (iii) investigating approximate decoding techniques including both the theoretical analysis of the performance and the implementation of practical low-complexity decoders.

 Publications

year authors and title journal last update
List of publications.
2019 Yin Bi, Aaron Chadha, Alhabib Abbas, Eirina Bourtsoulatze, Yiannis Andreopoulos
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision Sensing
published pages: , ISSN: , DOI:
2020-03-23
2018 Eirina Bourtsoulatze, Deniz Gunduz
Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks
published pages: , ISSN: , DOI:
2020-03-23
2019 Eirina Bourtsoulatze, David Burth Kurka, Deniz Gunduz
Deep Joint Source-Channel Coding for Wireless Image Transmission
published pages: 567-579, ISSN: 2332-7731, DOI: 10.1109/tccn.2019.2919300
IEEE Transactions on Cognitive Communications and Networking 5/3 2020-03-23
2019 Pantelis Maniotis, Eirina Bourtsoulatze, Nikolaos Thomos
Tile-Based Joint Caching and Delivery of 360° Videos in Heterogeneous Networks
published pages: 1-1, ISSN: 1520-9210, DOI: 10.1109/tmm.2019.2957993
IEEE Transactions on Multimedia 2020-03-23

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The information about "ENVISION" are provided by the European Opendata Portal: CORDIS opendata.

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