Commit 8db425de authored by Tran Huy Vu's avatar Tran Huy Vu

remove red text overview

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......@@ -31,7 +31,7 @@ googling.. are supercaps as reliable as normal caps? do they tend to leak
more? how long will it store charge? for an expert reviewer, no issues.
for someone less expert, these questions might come out}
Figure~\ref{fig:XXX} shows the overall flow of \names. In this system, the wearable or embedded device (the `client') periodically transmits an omni-directional `ping' message (Step 1) . A WiFi AP computes the AoA (angle of arrival) of such a `ping' message and thereby establishes the client device's direction (angular orientation) relative to the AP (Step 2). The WiFi AP then transmits \emph{electronic beamformed} energy packets, effectively delivering a more concentrated dose of RF energy in the direction of the client device (Step 3). The client device then utilizes an RF energy-harvesting circuit (\am{Vu: say a bit more here}) to convert this passively-harvested RF energy into an electrical current, and store the resulting energy in a super-capacitor (Step 4). This supercapacitor thus acts as a nano-battery, providing the power needed to activate the sensing and communication modules of the client device, which then collects and transmits the sensor data (an accelerometer stream in our current implementation) to the backend infrastructure (Step 5).
Figure~\ref{fig:overview} shows the overall flow of \names. In this system, the wearable or embedded device (the `client') periodically transmits an omni-directional `ping' message (Step 1) . A WiFi AP computes the AoA (angle of arrival) of such a `ping' message and thereby establishes the client device's direction (angular orientation) relative to the AP (Step 2). The WiFi AP then transmits \emph{electronic beamformed} energy packets, effectively delivering a more concentrated dose of RF energy in the direction of the client device (Step 3). The client device then utilizes an RF energy-harvesting circuit to harvest the energy in WiFi signal and convert this passively-harvested RF energy into an electrical current, and store the resulting energy in a super-capacitor (Step 4). This supercapacitor thus acts as a nano-battery, providing the power needed to activate the sensing and communication modules of the client device, which then collects and transmits the sensor data (an accelerometer stream in our current implementation) to the backend infrastructure (Step 5).
We shall see that the harvested RF energy levels, while several orders of magnitude higher than prior systems, is still insufficient to power the (sensing, communication) modules continuously. Accordingly, the client device (a wrist-worn ``wearable" prototype in our implementation) must employ a set of smart sensing and communication \emph{activation} strategies, turning on these components intermittently and only when needed. It is worth mentioning that the feasibility of the overall \name approach has become feasible only recently, with the development of appropriate AoA and beamforming techniques for commodity WiFi APs. It is also worth stating that the \name architecture is not WiFi-specific: its core ideas relate to wireless power transfer and harvesting, and can be performed using other wireless transmission technologies (e.g., micro-wave) as well. However, we shall demonstrate the viability of \name using a WiFi-based implementation, simply because WiFi offers the most commonplace wireless technology for our target environments.
......@@ -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{xx} to obtain the AoA information for both direct path and multipath signals. We defer implementation-specific details of such AoA estimation to Section~\ref{xxx}. However, 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 \am{complete this--what do we see?} 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{xx} to obtain the AoA information for both direct path and multipath signals. We defer implementation-specific details of such AoA estimation to Section~\ref{xxx}. However, 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 loaction 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 done 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
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