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Increasingly, the heterogeneity of devices and software that comprise the Internet of Things (IoT) is impeding innovation. IoT deployments amalgamate compute, storage, networking capabilities provisioned at multiple resource scales, from low-cost, resource constrained microcontrollers to resource rich public cloud servers. To support these different resource scales and capabilities, the operating systems (OSs) that manage them have also diverged significantly. Because the OS is the “API” for the hardware, this proliferation is causing a lack of portability across devices and systems, complicating development, deployment, management, and optimization of IoT applications. To address these impediments, we investigate a new, “clean slate” OS design and implementation that hides this heterogeneity via a new set of abstractions specifically for supporting microservices as a universal application programming model in IoT contexts. The operating system, called Ambience, supports IoT applications structured as microservices and facilitates their portability, isolation, and deployment time optimization. We discuss the design and implementation of Ambience, evaluate its performance, and demonstrate its portability using both microbenchmarks and end-to-end IoT deployments. Our results show that Ambience can scale down to 64MHz microcontrollers and up to modern x86_64 servers, while providing similar or better performance than comparable commodity operating systems on the same range of hardware platforms.
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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.
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- Award ID(s):
- 1801472
- PAR ID:
- 10603159
- Publisher / Repository:
- Association for Computing Machinery (ACM)
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2474-9567
- Format(s):
- Medium: X Size: p. 1-32
- Size(s):
- p. 1-32
- Sponsoring Org:
- National Science Foundation
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