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 -