Commit 8b191fcd authored by Tran Huy Vu's avatar Tran Huy Vu

fix errors

parent b368aa58
......@@ -22,7 +22,7 @@ In this section, we present the overall functional architecture of \names, detai
\label{fig:step3}
\end{subfigure}
\end{tabular}
\caption{3-step model of \name architecture. a) Step1: The wearable send a ping packet when triggered by gestures. b) Step2: The AP receive ping packets and estimates AoA of the device, and concentrates its energy toward the device. c) Step3: The device harvests the energy and operate its sensors, and transmit the data back the the server once available.}
\caption{3-step model of \name architecture. a) Step1: The wearable send a ping packet when triggered by gestures. b) Step2: The AP receives ping packets and estimates AoA of the device, and concentrates its energy toward the device. c) Step3: The device harvests the energy and operate its sensors, and transmit the data back the the server once available.}
\label{fig:overview}
\end{figure*}
......@@ -47,7 +47,7 @@ With the adoption of MIMO technologies in the latest 802.11n and 802.11ac WiFi s
\end{figure}
\subsection{Locating the Client Device}
For beamformed energy transfer to be effective, the WiFi AP needs to know the location of the client device. More specifically, the AP does not really need to know the client's precise location; what it needs is the \emph{angular direction} of the client, \emph{relative to the AP's own location}. To compute this, the WiFi AP utilizes its antenna array to determine the AoA of any wireless transmissions from the client device. The key principle for such angle/direction estimation is that the same signal propagates different amounts of distances to reach different antennas located at the AP, and thus manifests itself in slight shifts in the signal phase changes across the different antenna elements at the AP. As the spacing between the antennas is fixed and known, by measuring the signal phase difference between adjacent antennas, we can estimate the angle of arrival of the signal (device). In practice, the presence of multipath (reflected signals) causes errors in such AoA estimation. Accordingly, we employ the state-of-the-art MUSIC signal processing algorithm~\cite{schmidt1986multiple} to obtain the AoA information for both direct path and multipath signals. Figure~\ref{fig:musicerror} shows the AoA estimation error observed in our office room setting, utilize a 4-element antenna array: we can see that the multi path effect in office environment is quite strong. At 1 location with 10 ping packets, the highest peak in a AoA spectrum is not always the correct angle of the device. However, if the system observes a sufficient number of continuous packets, it can still estimate the real angle of the device. This has been demonstrated in \cite{xiong2013arraytrack}. Note also that this functional step is needed only for mobile clients (e.g., wearable devices worn by an individual), and is unnecessary for more static settings where the location of the sensor devices are predetermined.
For beamformed energy transfer to be effective, the WiFi AP needs to know the location of the client device. More specifically, the AP does not really need to know the client's precise location; what it needs is the \emph{angular direction} of the client, \emph{relative to the AP's own location}. To compute this, the WiFi AP utilizes its antenna array to determine the AoA of any wireless transmissions from the client device. The key principle for such angle/direction estimation is that the same signal propagates different amounts of distances to reach different antennas located at the AP, and thus manifests itself in slight shifts in the signal phase changes across the different antenna elements at the AP. As the spacing between the antennas is fixed and known, by measuring the signal phase difference between adjacent antennas, we can estimate the angle of arrival of the signal (device). In practice, the presence of multipath (reflected signals) causes errors in such AoA estimation. Accordingly, we employ the state-of-the-art MUSIC signal processing algorithm~\cite{schmidt1986multiple} to obtain the AoA information for both direct path and multipath signals. Figure~\ref{fig:musicerror} shows the AoA estimation error observed in our office room setting, utilize a 4-element antenna array: we can see that the multi path effect in office environment is quite strong. At 1 location with 10 ping packets, the highest peak in an AoA spectrum is not always the correct angle of the device. However, if the system observes a sufficient number of continuous packets, it can still estimate the real angle of the device. This has been demonstrated in \cite{xiong2013arraytrack}. Note also that this functional step is needed only for mobile clients (e.g., wearable devices worn by an individual), and is unnecessary for more static settings where the location of the sensor devices are predetermined.
\begin{figure}[!htb]
\centering
......
......@@ -53,7 +53,7 @@ work, such as ArrayTrack~\cite{xiong2013arraytrack} and
Chronos~\cite{vasisht2016} have shown how to leverage active client RF
transmissions, coupled with precise AoA computations to very precisely
locate the client. We use similar methods in \names. Device-free
localisation approaches, such as WiSee~\cite{pu2013whole}, and single AP
localization approaches, such as WiSee~\cite{pu2013whole}, and single AP
methods, such as Bharadia et. al~\cite{bharadia2013full}, Jain et.
al~\cite{jain2011practical}, and IndoTrack~\cite{li2017} were also
considered. But they were not robust enough for our arbitrary deployment
......
......@@ -89,7 +89,7 @@ Each WARP board can be connected to 4 antennas. We use one WARP board with a 4-a
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:wearablecontainer}).
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:wearablecontainer}).
\subsection{Access Point \& Directional Beams}
\begin{figure}
......@@ -112,6 +112,6 @@ Via our experimental studies, we are interested in not only studying the wearabl
\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:} 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.
\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 decodes 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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