TY - GEN
T1 - Middleware for Proximity Distributed Real-Time Processing of IoT Data Flows
AU - Nakamura, Yugo
AU - Suwa, Hirohiko
AU - Arakawa, Yutaka
AU - Yamaguchi, Hirozumi
AU - Yasumoto, Keiichi
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/8/8
Y1 - 2016/8/8
N2 - EdgeComputing and Fog Computing are new paradigms where data processing is executed in or on the edge of networks to mitigate cloud server load. However, EdgeComputing and Fog Computing still need powerful servers on the edge of networks which impose additional costs for deployments. We proposed a platform called IFoT (Information Flow of Things) that efficiently performs distributed processing as well as distribution and analysis of data streams near their sources based on "Process On Our Own (PO3)" concept. In IFoT, processing of tasks for cloud servers is delegated to an ad-hoc distributed system consisting of proximity IoT devices for distributed real-time stream processing. In this demonstration, we show a face recognition system for person tracking developed on top of IFoT middleware which locally processes video streams in real-time and in a distributed manner by using computational resources of IoT devices.
AB - EdgeComputing and Fog Computing are new paradigms where data processing is executed in or on the edge of networks to mitigate cloud server load. However, EdgeComputing and Fog Computing still need powerful servers on the edge of networks which impose additional costs for deployments. We proposed a platform called IFoT (Information Flow of Things) that efficiently performs distributed processing as well as distribution and analysis of data streams near their sources based on "Process On Our Own (PO3)" concept. In IFoT, processing of tasks for cloud servers is delegated to an ad-hoc distributed system consisting of proximity IoT devices for distributed real-time stream processing. In this demonstration, we show a face recognition system for person tracking developed on top of IFoT middleware which locally processes video streams in real-time and in a distributed manner by using computational resources of IoT devices.
UR - http://www.scopus.com/inward/record.url?scp=84985920351&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84985920351&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2016.101
DO - 10.1109/ICDCS.2016.101
M3 - Conference contribution
AN - SCOPUS:84985920351
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 771
EP - 772
BT - Proceedings - 2016 IEEE 36th International Conference on Distributed Computing Systems, ICDCS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Conference on Distributed Computing Systems, ICDCS 2016
Y2 - 27 June 2016 through 30 June 2016
ER -