Low-Power Wake-Up System based on Frequency Analysis for Environmental Internet of Things

Being used in for environmental and military Internet of Things (IoT), a low power wake-up system based on frequency analysis is presented in this paper. It aims at detecting continuously the presence of specific very high frequencies in the input acoustic signal of an embedded system. This can be used for detecting specific animal species, and for triggering a recording system or generating alerts. Used for harmful species detection, this helps to save harvests or to protect strict nature reserves. It can also be used for detecting the presence of drones in a specific restricted area.This acoustic low power wake-up system uses a simple 16 bits micro-controller (MCU), with a strong emphasis on the low power management of the system, having a target of continuous detection for at least one year on a single standard 1.2Ah - 12V lead battery. For that, it makes the most of mixed analog and digital low power MCU modules. They are including comparators, timers and a special one present on Microchip MCU, called Charge Time Measurement Unit (CTMU). This is a driven constant current source for making time to frequency conversions at a very low power and algorithmic cost.Optimizing low power modes, this low power wake-up system based on frequency analysis has a power consumption of 0.56mW, leading to approximately 3 years of battery life on a single standard 1.2Ah - 12Vlead cell.

[1]  Yonghong Yan,et al.  Deep neural network based wake-up-word speech recognition with two-stage detection , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  C. Negrescu,et al.  Low computational method for siren detection , 2015, 2015 IEEE 21st International Symposium for Design and Technology in Electronic Packaging (SIITME).

[3]  Vinod Kulathumani,et al.  Hibernets: Energy-Efficient Sensor Networks Using Analog Signal Processing , 2011, IEEE J. Emerg. Sel. Topics Circuits Syst..

[4]  Juan José Burred,et al.  Audio event detection based on layered symbolic sequence representations , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[5]  Hiroshi Sawada,et al.  Bayesian Nonparametrics for Microphone Array Processing , 2014, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[6]  David V. Anderson,et al.  Glass Break Detector Analog Front-End Using Novel Classifier Circuit , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[7]  Keisuke Nakamura,et al.  Robot audition based Acoustic Event Identification using a Bayesian model considering spectral and temporal uncertainties , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Yoohwan Kim,et al.  Implementation of detection and tracking mechanism for small UAS , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[9]  Hermann Ney,et al.  Silence is golden: Modeling non-speech events in WFST-based dynamic network decoders , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Carol Nehring,et al.  National Audubon Society Field Guide to North American Mammals , 1980 .

[11]  Kazuhiro Nakadai,et al.  Semi-automatic bird song analysis by spatial-cue-based integration of sound source detection, localization, separation, and identification , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[12]  Juan José Serrano,et al.  RFID Based Acoustic Wake-Up System for Underwater Sensor Networks , 2011, 2011 IEEE Eighth International Conference on Mobile Ad-Hoc and Sensor Systems.

[13]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[14]  Leonhard M. Reindl,et al.  Smartphone remote control for home automation applications based on acoustic wake-up receivers , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.