Session 20: Next Gen Advancements
A "Flexible" solution for Internet of Things
Thursday, February 15, 2018
2:25 PM - 2:45 PM
A full-cycle Internet of Things (IoT) solution is a seamless integration of electronics, device-based in-situ processing, embedded communication, data infrastructure and analytics, designed to establish a complete machine to machine communication by avoiding any human intervention. In this talk, we will demonstrate IoT solution using flexible electronic platforms. We will demonstrate shipment, transportation and asset monitoring case studies using a flexible patch. Additionally, we will show sensor platforms with temperature, humidity and accelerometer measurement capabilities for improved monitoring and intervention capabilities from the cloud platform. The flexible sensor unit consists of a semi flexible temperature and humidity sensor patch, ultra-thin flexible lithium polymer battery, the necessary firmware to communicate the sensor data to a cloud data services using Wi-Fi. This hybrid electronics platform was developed on a kapton polyimide substrate and the firmware of the electronics support ultra-low power consumption for extended life. This patch acts as an hybrid electronic platform on which we are integrating range of sensors (for a wide array of physical measurements, gas detection etc), send the raw data to a secure private server and combine this with machine learning platforms to infer insights for different kind of applications like smart shipping & smart environment monitoring. We will demonstrate case studies to show how form factor, efficient battery usage and implementing the needs of analytics and machine learning in the front- end hardware development of IoT can create a ubiquitous application space for such devices and widen the range of advantages. SAAPE’s full-cycle IoT platform development from board to cloud has generated a lot of industry interest in using our architecture for integrating current sensor research with back-end machine learning and analytics to deliver invaluable insights from collected data.
Abhilash has a background in applied machine learning and embedded system development for consumer electronics applications. He works with his team on developing flexible electronic platforms for internet of things applications, which involve research in the areas like flexible electronic sensors, embedded systems and software development for these systems. He pursued a master's in electrical engineering from Drexel University and a bachelor’s in electronics and telecommunications from the University of Mumbai. Some of his previous work were related to interdisciplinary problems like implementing sensor fusion using inertial measurement units, classification of hand posture using machine learning etc