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Thanks to advancements in wireless networks, robotics, and artificial intelligence, future manufacturing and agriculture processes may be capable of producing more output with lower costs through automation. With ultra fast 5G mmWave wireless networks, data can be transferred to and from servers within a few milliseconds for real-time control loops, while robotics and artificial intelligence can allow robots to work alongside humans in factory and agriculture environments. One important consideration for these applications is whether the “intelligence” that processes data from the environment and decides how to react should be located directly on the robotic device that interacts with the environment - a scenario called “edge computing” - or whether it should be located on more powerful centralized servers that communicate with the robotic device over a network - “cloud computing.” For applications that require a fast response time, such as a robot that is moving and reacting to an agricultural environment in real time, there are two important tradeoffs to consider. On the one hand, the processor on the edge device is likely not as powerful as the cloud server, and may take longer to generate the result. On the other hand, cloud computing requires both the input data and the response to traverse a network, which adds some delay that may cancel out the faster processing time of the cloud server. Even with ultra-fast 5G mmWave wireless links, the frequent blockages that are characteristic of this band can still add delay. To explore this issue, we run a series of experiments on the Chameleon testbed emulating both the edge and cloud scenarios under various conditions, including different types of hardware acceleration at the edge and the cloud, and different types of network configurations between the edge device and the cloud. These experiments will inform future use of these technologies and serve as a jumping off point for further research.
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A New Emulation Platform for Real-time Machine Learning in Substance Use Data Streams
With 5G networks on the rise, it becomes more and more important to grant researchers access to tools that allow for development and experimentation in the field of 5G transmission. Healthcare can benefit greatly from these developments. In this paper a real-time transmission technique is described and tested that, if implemented, allows wearable devices to transmit multiple streams of data on various frequencies. These tests will be used to explain how this presented platform works, what drawbacks and benefits exist with the proposed scheme, and how to further develop the solution of real-time transmission of sensitive data, such as substance-use data, at higher frequencies.
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- Award ID(s):
- 1744272
- PAR ID:
- 10201826
- Date Published:
- Journal Name:
- 21st IEEE International Conference on Information Reuse and Integration
- Volume:
- 1
- Page Range / eLocation ID:
- 325 to 332
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IRI49571.2020.00054
