Automated home applications are to ease the use of technology and devices around the house. Most of the electronic devices, like shutters or entertainment products (Hifi, TV and even WiFi), are constantly in a standby mode, where they consume a considerable amount of energy. The standby mode is necessary to react to commands triggered by the user, but the time the device spends in a standby mode is considered long.
In our work, we present a receiver that is attached to home appliances that allows the devices to be activated while they are completely turned off in order to reduce the energy consumed in the standby mode. The receiver contains a low power wake-up module that reacts to an addressable acoustic 20-kHz sound signal that controls home devices that are connected to it. The acoustic wake-up signal can be sent by any kind of speaker that is available in commercial smartphones. The smartphones will operate as transmitters to the signals.
Our wake-up receiver consists of two parts: a low power passive circuit connected to a wake-up chip microcontroller and an active micro-electromechanical system (MEMS) microphone that receives the acoustic signal. A duty cycle is required to reduce the power consumption of the receiver, because the signal reception occurs when the microphone is active. The current consumption was measured to be 15 μ A in sleep mode and 140 μA in active mode. An average wake-up range of 10 m using a smartphone as a sender was achieved.
ACOUSTIC WAKE-UP RECEIVER DESIGN
The microcontroller uses a duty cycle approach to switch the relay on and off to power the amplifier and the microphone. In an active period, the microphone samples audio signals and routes these signals after filtering them through the amplifier to the wake-up chip. Upon detecting a wake-up signal, the wake-up chip activates the microcontroller, which in turn switches the relay on or off to power the device, as in Figure 1.
Initially, we have simulated several circuits that include high pass, low pass and bandpass filters to achieve a possible upper and a lower cut-off frequency of 1 kHz–20 kHz. Since most of the simulated filter circuit models require up to 63 mH inductance, we chose this since the induction is not available at all values. In addition, a special induction costs a great deal.
A higher induction value needs more space in the circuit, as well. Therefore, we selected a value that considers the requirements of availability, cost and space. An LCfilter and an LC half-section filter are selected for filtering the signals. In order to avoid long interconnection, the filter is placed close to the microphone output, as seen in Figure 3. Therefore, we minimize the noise interference before the amplifier stages.
WAKE-UP SIGNAL TRANSMITTER
In order to choose the right operating frequency to generate the wake-up signal, the speaker of several smartphones is characterized in the aspect of normalized amplitude vs. frequency. The results can be seen in Figure 4. We have seen that the the signals descend after the 6-kHz frequency, and a rise in the amplitude of the signals is found around 16 kHz, independent of the smartphone type. However, a 16-kHz frequency is found in the human hearing range. Transmitting the acoustic wake-up signals at this frequency will generate a noise that might affect human hearing.
RESULTS AND DISCUSSION
The core component of the acoustic wake-up receiver is the wake-up chip. It consumes only 2.7 μ A. Although the wake-up circuit that is considered in this work is passive, the microphone is an active MEMS component, where the regular current consumption with the amplifier is measured using a Fluke 87 III True RMS Multimeter to be 140 μ A. In order to reduce the power consumption of the receiver, the power supply of the microphone should be switched on and off in a duty cycle similar to Figure 7. Several approaches to control energy consumption using a duty cycle in wireless sensor networks are discussed.
From each smartphone, we sent 50 wake-up signals to the receiver at every measurement point. Due to the different reachability of the smartphones, different measurement points are used. The percentage of received signals at each distance can be seen in Figure 9. In general, an average distance between a user and the home appliances is around 5 m in order for the user to operate any device. It can be seen in the figure that the required coverage distance of 5 m was achieved with a probability success rate of more than 70% for both the iPhone and Samsung Galaxy.
In this work, we presented a 16-bit addressable acoustic wake-up receiver, which can be used to operate home devices by powering them on and off from the main power. In this process, we power off the devices when they are not in use in order to reduce the power wasted from the continuous operation in standby mode. The receiver consists of off-the-shelf components, such as a MEMS microphone that is used to transform acoustic waves into electric voltages. In addition, the wake-up receiver includes a filter and amplifiers to reduce the noise and amplify the signals for a better detection.
A duty cycle approach is used and controlled by the microcontroller. In an active phase, the MEMS microphone is powered on to receive acoustic signals. A wake-up chip detects a signal with a valid address at a frequency of 20 kHz and triggers the receiver from sleep to active mode to turn on and off home appliances upon detecting the signals. In the active phase, a receiver needs 140 μA, whereas the receiver needs 15 μA in sleep phase. The setup enables a person that possesses a smartphone to wake-up the receivers in the surroundings. A wake-up distance of 30 m is achieved outdoors, whereas a 12-m wake-up distance is reached indoors.
Source: University of Freiburg
Authors: Amir Bannoura | Fabian Höflinger | Omar Gorgies | Gerd Ulrich Gamm | Joan Albesa | Leonhard M. Reindl