TY - GEN
T1 - Machine learning systems in the IoT
T2 - 2nd IEEE International Conference on Cognitive Machine Intelligence, CogMI 2020
AU - Toussaint, Wiebke
AU - Ding, Aaron Yi
PY - 2020
Y1 - 2020
N2 - Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the tradeoffs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.
AB - Machine learning systems (MLSys) are emerging in the Internet of Things (IoT) to provision edge intelligence, which is paving our way towards the vision of ubiquitous intelligence. However, despite the maturity of machine learning systems and the IoT, we are facing severe challenges when integrating MLSys and IoT in practical context. For instance, many machine learning systems have been developed for large-scale production (e.g., cloud environments), but IoT introduces additional demands due to heterogeneous and resource-constrained devices and decentralized operation environment. To shed light on this convergence of MLSys and IoT, this paper analyzes the tradeoffs by covering the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. We position machine learning systems as a component of the IoT, and edge intelligence as a socio-technical system. On the challenges of designing trustworthy edge intelligence, we advocate a holistic design approach that takes multi-stakeholder concerns, design requirements and trade-offs into consideration, and highlight the future research opportunities in edge intelligence.
KW - Edge intelligence
KW - Internet of Things
KW - Machine learning systems
KW - Smart services
KW - Trade-offs
KW - Trustworthiness
UR - http://www.scopus.com/inward/record.url?scp=85100622799&partnerID=8YFLogxK
U2 - 10.1109/CogMI50398.2020.00030
DO - 10.1109/CogMI50398.2020.00030
M3 - Conference contribution
AN - SCOPUS:85100622799
T3 - Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
SP - 177
EP - 184
BT - Proceedings - 2020 IEEE 2nd International Conference on Cognitive Machine Intelligence, CogMI 2020
PB - IEEE
Y2 - 1 December 2020 through 3 December 2020
ER -