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Journal of Sensors
Volume 2015, Article ID 809201, 10 pages
http://dx.doi.org/10.1155/2015/809201
Research Article

A Low-Power Microcontroller with Accuracy-Controlled Event-Driven Signal Processing Unit for Rare-Event Activity-Sensing IoT Devices

1School of Electronics Engineering, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 702-701, Republic of Korea
2Department of Computer Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 712-749, Republic of Korea

Received 8 January 2015; Accepted 23 March 2015

Academic Editor: Young-Sik Jeong

Copyright © 2015 Daejin Park et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A specially designed microcontroller with event-driven sensor data processing unit (EPU) is proposed to provide energy-efficient sensor data acquisition for Internet of Things (IoT) devices in rare-event human activity sensing applications. Rare-event sensing applications using a remotely installed IoT sensor device have a property of very long event-to-event distance, so that the inaccurate sensor data processing in a certain range of accuracy error is enough to extract appropriate events from the collected sensing data. The proposed signal-to-event converter (S2E) as a preprocessor of the conventional sensor interface extracts a set of atomic events with the specific features of interest and performs an early evaluation for the featured points of the incoming sensor signal. The conventional sensor data processing such as DSPs or software-driven algorithm to classify the meaningful event from the collected sensor data could be accomplished by the proposed event processing unit (EPU). The proposed microcontroller architecture enables an energy efficient signal processing for rare-event sensing applications. The implemented system-on-chip (SoC) including the proposed building blocks is fabricated with additional 7500 NAND gates and 1-KB SRAM tracer in 0.18 um CMOS process, consuming only 20% compared to the conventional sensor data processing method for human hand-gesture detection.