Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - AI on the Move
T2 - From On-Device to On-Multi-Device
AU - Flores, H.
AU - Nurmi, P.
AU - Hui, P.
PY - 2019/6/6
Y1 - 2019/6/6
N2 - On-Device AI is an emerging paradigm that aims to make devices more intelligent, autonomous and proactive by equipping them with machine and deep learning routines for robust decision making and optimal execution in devices' operations. On-Device intelligence promises the possibility of computing huge amounts of data close to its source, e.g., sensor and multimedia data. By doing so, devices can complement their counterpart cloud services with more sophisticated functionality to provide better applications and services. However, increased computational capabilities of smart devices, wearables and IoT devices along with the emergence of services at the Edge of the network are driving the trend of migrating and distributing computation between devices. Indeed, devices can reduce the burden of executing resource intensive tasks via collaborations in the wild. While several work has shown the benefits of an opportunistic collaboration of a device with others, not much is known regarding how devices can be organized as a group as they move together. In this paper, we contribute by analyzing how dynamic group organization of devices can be utilized to distribute intelligence on the moving Edge. The key insight is that instead of On-Device solutions complementing with cloud, dynamic groups can be formed to complement each other in an On-Multi-Device manner. Thus, we highlight the challenges and opportunities from extending the scope of On-Device AI from an egocentric view to a collaborative, multi-device view.
AB - On-Device AI is an emerging paradigm that aims to make devices more intelligent, autonomous and proactive by equipping them with machine and deep learning routines for robust decision making and optimal execution in devices' operations. On-Device intelligence promises the possibility of computing huge amounts of data close to its source, e.g., sensor and multimedia data. By doing so, devices can complement their counterpart cloud services with more sophisticated functionality to provide better applications and services. However, increased computational capabilities of smart devices, wearables and IoT devices along with the emergence of services at the Edge of the network are driving the trend of migrating and distributing computation between devices. Indeed, devices can reduce the burden of executing resource intensive tasks via collaborations in the wild. While several work has shown the benefits of an opportunistic collaboration of a device with others, not much is known regarding how devices can be organized as a group as they move together. In this paper, we contribute by analyzing how dynamic group organization of devices can be utilized to distribute intelligence on the moving Edge. The key insight is that instead of On-Device solutions complementing with cloud, dynamic groups can be formed to complement each other in an On-Multi-Device manner. Thus, we highlight the challenges and opportunities from extending the scope of On-Device AI from an egocentric view to a collaborative, multi-device view.
KW - Artificial Intelligence
KW - Cloud
KW - Cloudlet
KW - Data Analytics
KW - Device-to-Device
KW - Edge
KW - Serverless
KW - Artificial intelligence
KW - Clouds
KW - Decision making
KW - Deep learning
KW - Computational capability
KW - Multimedia data
KW - Opportunistic collaboration
KW - Robust decisions
KW - Ubiquitous computing
U2 - 10.1109/PERCOMW.2019.8730873
DO - 10.1109/PERCOMW.2019.8730873
M3 - Conference contribution/Paper
SN - 9781538691526
SP - 310
EP - 315
BT - 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
PB - IEEE
ER -