TY - GEN
T1 - Poster Abstract
T2 - 2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
AU - Aguiari, Davide
AU - Ferlini, Andrea
AU - Cao, Jiannong
AU - Guo, Song
AU - Pau, Giovanni
PY - 2019/4
Y1 - 2019/4
N2 - Mobile autonomous systems are supposed to deeply impact in manufacturing, space exploration, rescue, defense, transportation, and everyday life. Autonomous air-ground vehicles, for example, will become normal tools in the next few years, providing a natural platform for distributed artificial intelligence applications including, for example, disaster rescue and recovery, area surveying, autonomous driving, etc. The raise of autonomous cooperating robots will pose new challenges in networking, distributed systems and resource management. Heavy computational tasks will be dispatched to the closest edge node for processing and the core-cloud will be involved as last resort in an effort to reduce latency and increase the global system capacity leveraging application and resource locality. Massive amounts of data and computations will be required. For example, in the autonomous driving scenario Intel estimates that each driver-less vehicle will produce over 4 TeraBytes of data each day1. While most of this data is consumed in-car, cooperating autonomous vehicles will have to exchange some percentage of the 4TB and eventually offload some computation and data to the local edge or the core-cloud. This is particularly relevant when locally gathered and labeled data can be used to refine the model and ultimately increase the global 'intelligence'. This approach is often taken by autonomous driving automakers.
AB - Mobile autonomous systems are supposed to deeply impact in manufacturing, space exploration, rescue, defense, transportation, and everyday life. Autonomous air-ground vehicles, for example, will become normal tools in the next few years, providing a natural platform for distributed artificial intelligence applications including, for example, disaster rescue and recovery, area surveying, autonomous driving, etc. The raise of autonomous cooperating robots will pose new challenges in networking, distributed systems and resource management. Heavy computational tasks will be dispatched to the closest edge node for processing and the core-cloud will be involved as last resort in an effort to reduce latency and increase the global system capacity leveraging application and resource locality. Massive amounts of data and computations will be required. For example, in the autonomous driving scenario Intel estimates that each driver-less vehicle will produce over 4 TeraBytes of data each day1. While most of this data is consumed in-car, cooperating autonomous vehicles will have to exchange some percentage of the 4TB and eventually offload some computation and data to the local edge or the core-cloud. This is particularly relevant when locally gathered and labeled data can be used to refine the model and ultimately increase the global 'intelligence'. This approach is often taken by autonomous driving automakers.
KW - cloud computing
KW - edge computing
KW - name data networking
KW - v2i
KW - v2v
UR - http://www.scopus.com/inward/record.url?scp=85073245171&partnerID=8YFLogxK
U2 - 10.1109/INFCOMW.2019.8845170
DO - 10.1109/INFCOMW.2019.8845170
M3 - Conference article published in proceeding or book
T3 - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
SP - 1053
EP - 1054
BT - INFOCOM 2019 - IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 April 2019 through 2 May 2019
ER -