Commit 137e1b77 authored by Tran Huy Vu's avatar Tran Huy Vu

format figures section 6 and 7

parent 05c49337
......@@ -59,7 +59,7 @@ evaluate how the wearing of the device affect the harvested energy.
In this experiment, we want to capture the amount of harvested energy when the user sit and work at the table. In this experiment only, the data packet transmission is disable to isolate the effect of RF transmission because it is currently the most energy consuming module. Two user join this experiment, each user sit at the table for 1 hour.
\begin{figure}
\centering
\includegraphics[scale=0.5]{energy1hour.pdf}
\includegraphics[scale=0.25]{energy1hour.pdf}
\caption{Residual energy after 1 hour at an office desk.}
\label{fig:energy1hour}
\end{figure}
......@@ -67,13 +67,13 @@ In this experiment, we want to capture the amount of harvested energy when the u
In this experiment, the user work at the table for longer time, so it includes periods when the user go out for a while, for example, go to the restroom. The figure shows the power it consumes from the super capacitor. \textcolor{blue}{This shows power consumption, not the residual because both columns are negative values} \textcolor{blue}{}
\begin{figure}
\centering
\includegraphics[scale=0.5]{energy4hour_neg.pdf}
\includegraphics[scale=0.25]{energy4hour_neg.pdf}
\caption{Consumed energy after 4 hour at an office desk.}
\label{fig:energy4hour}
\end{figure}
\begin{figure}
\centering
\includegraphics[scale=0.5]{accelerometer.pdf}
\includegraphics[scale=0.25]{accelerometer.pdf}
\caption{Accelerometer data recorded by our wearable device.}
\label{fig:accel4hour}
\end{figure}
......@@ -88,15 +88,15 @@ In this experiment, the user work at the table for longer time, so it includes p
\subsection{Multi-user Scenarios}
\begin{figure}
\centering
\includegraphics[scale=0.5]{2user_neg.pdf}
\includegraphics[scale=0.25]{2user_neg.pdf}
\caption{Beam adaptation for multi-user scenarios with separate beams.}
\label{fig:2user}
\label{fig:2userseparated}
\end{figure}
\begin{figure}
\centering
\includegraphics[scale=0.5]{2user_time.pdf}
\includegraphics[scale=0.25]{2user_time.pdf}
\caption{Beam adaptation for multi-user scenarios with time multiplexing.}
\label{fig:2user}
\label{fig:2usertime}
\end{figure}
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......@@ -43,7 +43,7 @@ to reduce inteference.
\begin{figure}
\centering
\includegraphics[scale=0.5]{nouser.pdf}
\includegraphics[scale=0.25]{nouser.pdf}
\caption{Residual power of our device during WiFi charging.}
\label{fig:nouser}
\end{figure}
......@@ -89,7 +89,7 @@ positions L2 and L3 having the highest charging potential.
\begin{figure}
\centering
\includegraphics[scale=0.5]{dutycycle.pdf}
\includegraphics[scale=0.25]{dutycycle.pdf}
\caption{Duty cycling the AP power transmission.}
\label{fig:dutycycle}
\end{figure}
......@@ -113,7 +113,7 @@ more than the required power for the wearable to work.
\subsection{Effect of Number of Antennas}
\begin{figure}
\centering
\includegraphics[scale=0.5]{number_antenna.pdf}
\includegraphics[scale=0.25]{number_antenna.pdf}
\caption{Effect of number of antennas on the harvested power.}
\label{fig:numberantenna}
\end{figure}
......@@ -124,19 +124,19 @@ Theoretically, with a larger number of antennas, the transmission beam is thinne
\begin{figure}
\centering
\includegraphics[scale=0.5]{multiplexclose.pdf}
\includegraphics[scale=0.25]{multiplexclose.pdf}
\caption{Time-multiplexing of narrow beam to 2 devices at 30cm separation.}
\label{fig:multiplexclose}
\end{figure}
\begin{figure}
\centering
\includegraphics[scale=0.5]{multiplexseparated.pdf}
\includegraphics[scale=0.25]{multiplexseparated.pdf}
\caption{Time-multiplexing of narrow beam to 2 devices at 1.7m separation.}
\label{fig:multiplexfar}
\end{figure}
\begin{figure}
\centering
\includegraphics[scale=0.5]{2device2beams.pdf}
\includegraphics[scale=0.25]{2device2beams.pdf}
\caption{Time-multiplexing of narrow beam to 2 devices at 1.7m separation.}
\label{fig:edev2beam}
\end{figure}
......@@ -149,7 +149,7 @@ On contrast, in concurrent beams mode, the separated spacing shows significantly
\subsection{Transfer Energy at Maximum Tx Power}
\begin{figure}
\centering
\includegraphics[scale=0.5]{maxpower.pdf}
\includegraphics[scale=0.25]{maxpower.pdf}
\caption{Maximum power transfer.}
\label{fig:maxpower}
\end{figure}
......@@ -161,7 +161,7 @@ Currently we set Tx gain at 35/63. This will show the energy at a gain of
\begin{figure}
\centering
\includegraphics[scale=0.5]{placeholder.pdf}
\includegraphics[scale=0.25]{placeholder.pdf}
\caption{Efficiency of the system.}
\label{fig:efficiency}
\end{figure}
......
......@@ -36,10 +36,10 @@ In our current effort, we do not focus on the development of the ``best harveste
\label{fig:harvester}
\end{figure}
The device also contains an RF front end, to transmit the collected sensor data (and any additional `ping' packets). This is the most power-hungry component in the overall system. It consumes 11.3 mA in transmission mode (when it is actively transmitting data at 0dBm), only and 0.9 \micro A in power-down mode (when only the SPI interface is running to maintain communication with the microcontroller). Because the instantaneous RF transmission power far exceeds the harvesting capacity of the device, it is infeasible to run the device using only the instantaneous harvested power. Accordingly, the wearable device include a supercapacitor, which effectively acts as a slowly-draining energy source. \am{Vu: say something about the capacitor size, discharge rate, etc.}
The device also contains an RF front end, to transmit the collected sensor data (and any additional `ping' packets). This is the most power-hungry component in the overall system. It consumes 11.3 mA in transmission mode (when it is actively transmitting data at 0dBm), only and 0.9 \micro A in power-down mode (when only the SPI interface is running to maintain communication with the microcontroller). Because the instantaneous RF transmission power far exceeds the harvesting capacity of the device, it is infeasible to run the device using only the instantaneous harvested power. Accordingly, the wearable device include a supercapacitor, which effectively acts as a slowly-draining energy source. We use a 0.47F super capacitor which has an equivalent series resistance of only 45mOhm as the energy storage. The capacitor is small and thin enough (21x14x3.2mm) to be integrated in a wearable device.
\subsection{The Microcontroller+ Sensor}
We utilize a commodity low-power microcontroller, the STM32L053~\cite{XXX}, which onsumes 6.6 mW power at normal operation, but only 1 \micro W power during stop mode. \am{Vu: explain what stop mode is, also indicate the clock frequency of the microcontroller.} The micro controller wakes up every 3 seconds to read 60 bytes of acceleration data from accelerometer, if the accelerometer is actually active. It also wakes up at every 1.5 minutes to transfer a block of about 3KB data back to the server, using the wireless interface.
We utilize a commodity low-power microcontroller, the STM32L053~\cite{XXX}, which onsumes 6.6 mW power at normal operation, but only 1 \micro W power during stop mode. In stop mode, all functions of the device is stopped. The microcontroller enter deep sleep mode, but the content of RAM is preserved. The device is waken up whenever an interrupt signal is fired. In this case, when the accelerometer records enough 3 sesonds of 10-Hz data, it generates an interrupt signal to wake up the microcontroller to read the buffer. The micro controller wakes up every 3 seconds to read 60 bytes of acceleration data from accelerometer, if the accelerometer is actually active. It also wakes up at every 1.5 minutes to transfer a block of about 3KB data back to the server, using the wireless interface.
Our device implementation utilizes the LIS3DHTR 3-axes accelerometer from STMicroelectronics. According to the manufacturer, this low-power sensor consumes 2 \micro A at 1 Hz, and 6 \micro A at 50Hz. For our experimental studies, we set the accelerometer sampling frequency at 10 Hz. At this frequency, the accelerometer can internally buffer 32 samples (i.e., approx. 3.2 seconds of sensor data).
......@@ -92,7 +92,7 @@ We utilize the WARP v3 platform~\cite{XXX} to implement our prototype AP. In our
Each WARP board can be connected to 4 antennas. We use one WARP board with a 4-antenna array to serve as the receiver to receive packets and estimate the Angle of Arrival, which is the angle of the device with respect to the antenna array. The energy transmitter is implemented with 2 WARP boards with a total 8 antennas placed in a linear array for beamforming. Ideally, the transmitter and receiver antennas should be at the same position. However, because the RF front-end in our wearable only works at 2.4GHz, we have to set the receiver antenna array at 2.4GHz as well. However, this causes interference between the WARP transmitter and receiver antenna array. (To our surprise, if the transmitter and receiver are in close proximity, this interference persists even if we set the transmitter to operate on 802.11b channel 1 and the receiver to operate on 802.11b channel 8. Even in this case, the receiver antenna is saturated by the transmitter, making the receiver WARP board unable to receive packets from the wearable client.)
Therefore, for our experimental studies, we place the two antenna-array at 2 edges of a table (1 meter from each other). In our implemented system, the wearable transmits 2 types of packets: (1) a `ping' packet (to aid AoA estimation) containing 1 preamble byte, 3 address bytes, 1 dummy data byte and 1 CRC byte; and (2) a data packet (containing the accelerometer data) containing \am{Vu:finish describing the wearable data packet}.
Therefore, for our experimental studies, we place the two antenna-array at 2 edges of a table (1 meter from each other). In our implemented system, the wearable transmits 2 types of packets: (1) a `ping' packet (to aid AoA estimation) containing 1 preamble byte, 3 address bytes, 1 dummy data byte and 1 CRC byte; and (2) a data packet contains the same preamble, the address is different by 1 bit, a 2-byte packetID and 30 bytes of accelerometer data which is corresponding to 3 seconds of recorded data.
\subsection{Wearable Client Device}
Via our experimental studies, we are interested in not only studying the wearable device in isolation, but when it is being used by regular users. To perform such studies, we need to ensure that the wearable device can be mounted on an individual's wrist. Clearly, our current prototype isn't a true wearable device: its form-factor is simply too unwieldy for constant wear. However, to perform experimental studies, we place the wearable device in a custom-fabricated container, which is then attached to a person's wrists using multiple velcro straps (see Figure~\ref{fig:XXX}).
......@@ -116,8 +116,8 @@ Via our experimental studies, we are interested in not only studying the wearabl
\label{fig:wearablecontainer}
\end{figure}
\noindent \textbf{Multiple Ping Packets:} In our initial implementation, the client device generated a single `ping' packet whenever the motion harvester indicated significant hand motion. However, the WARP RF transmitters continue to cause interference-induced packet loss at the WARP receiver, making it unable to correctly perform the AoA update on the ping packet. Eventually, we adopted a modified packet generation process, where the client device generate multiple (10 ping packets), generated randomly with a mean inter-packet gap of XXX \am{Vu-XXX} msecs. While this slightly increased the RF power consumption after a significant movement event, it dramatically improved the reliability of correct packet reception by the WARP receiver, which was then able to correctly track the motion trajectory of the wearable throughout the duration of our studies.
\noindent \textbf{Multiple Ping Packets:} In our initial implementation, the client device generated a single `ping' packet whenever the motion harvester indicated significant hand motion. However, the WARP RF transmitters continue to cause interference-induced packet loss at the WARP receiver, making it unable to correctly perform the AoA update on the ping packet. Eventually, we adopted a modified packet generation process, where the client device generate multiple (10 ping packets), with an inter-packet gap of 0.6 msecs. While this slightly increased the RF power consumption after a significant movement event, it dramatically improved the reliability of correct packet reception by the WARP receiver, which was then able to correctly track the motion trajectory of the wearable throughout the duration of our studies.
\noindent \textbf{Reliable Transmission of Data Packets:} \am{Vu: pls. add 2-3 sentences about how to transmit the accel data--what modulation, what data rate etc.}
\noindent \textbf{Reliable Transmission of Data Packets:} Currently the WARP board we use which is controlled by matlab is slow to capture all packets. So the receiver WARP board captures and decode only the preamble and address of ping packets to estimate the AoA and to distinct different devices. We employ a separate wearable board to record acceleration data, and to make sure the WiFi harvested devices is working.
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