Modern Internet of Things (IoT) applications, from contextual sensing to voice assistants, rely on ML-based training and serving systems using pre-trained models to render predictions. However,
real-world IoT environments are diverse, with rich IoT sensors and
need ML models to be personalized for each setting using relatively
less training data. Most existing general-purpose ML systems are
optimized for specific and dedicated hardware resources and do
not adapt to changing resources and different IoT application requirements. To address this gap, we propose MLIoT, an end-to-end
Machine Learning System tailored towards supporting the entire
lifecycle of IoT applications. MLIoT adapts to different IoT data
sources, IoT tasks, and compute resources by automatically training,
optimizing, and serving models based on expressive applicationspecific policies. MLIoT also adapts to changes in IoT environments
or compute resources by enabling re-training, and updating models
served on the fly while maintaining accuracy and performance. Our
evaluation across a set of benchmarks show that MLIoT can handle
multiple IoT tasks, each with individual requirements, in a scalable
manner while maintaining high accuracy and performance. We
compare MLIoT with two state-of-the-art hand-tuned systems and
a commercial ML system showing that MLIoT improves accuracy
from 50% - 75% while reducing or maintaining latency.
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Mites: Design and Deployment of a General-Purpose Sensing Infrastructure for Buildings
There is increasing interest in deploying building-scale, general-purpose, and high-fidelity sensing to drive emerging smart building applications. However, the real-world deployment of such systems is challenging due to the lack of system and architectural support. Most existing sensing systems are purpose-built, consisting of hardware that senses a limited set of environmental facets, typically at low fidelity and for short-term deployment. Furthermore, prior systems with high-fidelity sensing and machine learning fail to scale effectively and have fewer primitives, if any, for privacy and security. For these reasons, IoT deployments in buildings are generally short-lived or done as a proof of concept. We present the design of Mites, a scalable end-to-end hardware-software system for supporting and managing distributed general-purpose sensors in buildings. Our design includes robust primitives for privacy and security, essential features for scalable data management, as well as machine learning to support diverse applications in buildings. We deployed our Mites system and 314 Mites devices in Tata Consultancy Services (TCS) Hall at Carnegie Mellon University (CMU), a fully occupied, five-story university building. We present a set of comprehensive evaluations of our system using a series of microbenchmarks and end-to-end evaluations to show how we achieved our stated design goals. We include five proof-of-concept applications to demonstrate the extensibility of the Mites system to support compelling IoT applications. Finally, we discuss the real-world challenges we faced and the lessons we learned over the five-year journey of our stack's iterative design, development, and deployment.
more » « less- Award ID(s):
- 1801472
- PAR ID:
- 10488602
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 32
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3580865
