Developing Low Cost Edge Devices for the Manufacturing Floor – Challenges & Opportunities
With the advent of low cost connectivity options for IoT (“Internet of Things”), there has been a surge in the number of connected devices deployed by organizations. This is driven by democratization achieved by open source hardware. This has enabled collection of a large volume of data from the field to optimize product throughput, monitor performance, predict failures etc. In a typical IoT application, every single data point is uploaded to the cloud for further analysis. It can be very expensive to upload and store data points from thousands of IoT devices.
Edge computing provides an opportunity to run AI toolsets right next to a sensor node instead of the cloud. This eliminates the need to upload every datapoint to the cloud and problems could be detected at the “edge”. The reaction time is also relatively faster when the inference happens at the edge.
There are challenges to overcome such as identifying the right toolset, identifying the “low hanging fruit” when it comes to new product development, data collection, finding the appropriate business model etc. In this talk, I will highlight the different opportunities and challenges when it comes to developing an edge device. I will also share my strategy for meeting product cost targets and saving time and resources during the development cycle.
IoT Applications Engineer at Linde
Sai Yamanoor has over 6 years of experience as an embedded systems expert and currently works for an industrial gases manufacturer in Buffalo, NY. He has worked on software development, hardware development including IoT system design, testing and deployment. Along with Srihari, he is a co-author of two books on the use of Raspberry Pi to execute DIY projects, and they have also presented a Personal Health Dashboard systems at Maker Faires across the country. Sai’s current interests include Edge Computing, IIoT and AI. Sai is also currently working on machine learning projects aimed at improving Quality of Life (QoL) for people with chronic health conditions. His profile can be viewed at: https://www.linkedin.com/in/saiyamanoor/ . A portfolio of his projects is available at http://saiyamanor.com/
AI, Edge Computing, IoT
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