Commit 03bd961f authored by Tran Huy Vu's avatar Tran Huy Vu

update all

parent a584184c
......@@ -7,4 +7,4 @@ a receiver located 3 to 4 meters away. \name couples this harvesting with
smart event triggering to duty cycle a full wearable device that uses 40 or
more mW of power. We demonstrated this via detailed systems and user study
results with a prototype wearable sensing device that records and transmits
sensor data at XXX Hz. \raj{what do we say here?}
sensor data at 10 Hz.% \raj{what do we say here?}
......@@ -7,8 +7,8 @@ We now present results on the evaluation of our wearable prototype, used in cons
\begin{figure}
\centering
\includegraphics[height=1.9in,scale=0.38]{setup.jpg}
\label{fig:exprsetup}
\caption{Experimental setup for office room user study.}
\label{fig:exprsetup}
\end{figure}
We conducted all our experiments in a meeting room setup to mimic a typical
......@@ -64,7 +64,7 @@ Figure~\ref{fig:energy1hour} plots the average differential power (measured over
\subsection{Office--Longer Term}
This next study is identical to the previous one, except that it involves only 1 user and is conducted over a longer observation period (4 hours), This larger observation period helps captures the user's natural movement dynamics over effectively one half of a typical working day: in this study, the user occasionally left the office room (e.g., to visit the restroom). For these study, we enabled both the continuous sensing and the periodic (once every 1.5 minutes) data transfer components. Accordingly, this study is meant to monitor the worst-case energy drain; in practice, a user will exhibit \emph{significant motion} only intermittently, and the sensing and data transfer overheads will thus be dramatically lower.
Figure~\ref{energy4hour} plots the differential average power of the user, wearing the energy harvesting wearable. For a baseline comparison, we utilize a setting where the WiFi RF transmission are turned off--i.e., all energy harvesting is disabled. The baseline thus indicates the total average power drain on the wearable, in the absence of any energy harvesting. The figure shows that, in the absence of any harvesting, the wearable device drains approx. 65$\mu$W; in comparison, the power drain on the supercapacitor with harvesting enabled is only 20$\mu$W. The average power drain from the sensing+ data transfer components is 30 - 40$\mu$W because when the sensing is active, it will wake the microcontroller up to read the data and transfer the data to the RF module. Accordingly, as the use of motion triggering on these components will dramatically cut down this power drain, the overall harvesting power will become positive, allowing the wearable to operate continually.
Figure~\ref{fig:energy4hour} plots the differential average power of the user, wearing the energy harvesting wearable. For a baseline comparison, we utilize a setting where the WiFi RF transmission are turned off--i.e., all energy harvesting is disabled. The baseline thus indicates the total average power drain on the wearable, in the absence of any energy harvesting. The figure shows that, in the absence of any harvesting, the wearable device drains approx. 65$\mu$W; in comparison, the power drain on the supercapacitor with harvesting enabled is only 20$\mu$W. The average power drain from the sensing+ data transfer components is 30 - 40$\mu$W because when the sensing is active, it will wake the microcontroller up to read the data and transfer the data to the RF module. Accordingly, as the use of motion triggering on these components will dramatically cut down this power drain, the overall harvesting power will become positive, allowing the wearable to operate continually.
\begin{figure}[!htb]
\centering
......
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......@@ -14,7 +14,7 @@
\emph{Energy} remains perhaps the greatest challenge in the pervasive
deployment of sensing systems, such as wearable devices for human activities (e.g.
eating behaviour~\cite{thomaz2015}, smoking~\cite{parate2014}, or stress
levels~\cite{ertin2011}) or embedded devices used for environmental sensing(e.g.,~\cite{campbell2014}). In particular,
levels~\cite{ertin2011}) or embedded devices used for environmental sensing~(e.g.,~\cite{campbell2014}). In particular,
sensors such as accelerometers or gyroscopes simply consume too much energy
to operate continuously without either a dedicated power source or a large
battery. However, using battery power introduces two distinct
......@@ -54,9 +54,9 @@ techniques (that further extend the energy-driven intermittent sensing paradigm
Our solution, called \names, uses beam-formed transmissions, by a
multi-antenna AP, of WiFi ``power packets'' (transmissions performed
explicitly to transfer RF energy) to deliver bursts of directed WiFi energy
to a client device. To form the beam towards the client, \name utilizes AoA
(angle-of-arrival) estimation techniques~\cite{xiong2013arraytrack}. These AP-side
techniques are paired with novel energy-conserving features on the wearable
to a client device. To point the beam towards the client, \name utilizes AoA
(angle-of-arrival) estimation techniques~\cite{xiong2013arraytrack}.
These AP-side techniques are paired with novel energy-conserving features on the wearable
device, which activates its communication and sensing components
intelligently and selectively, to help capture only key events. While the
core ideas were articulated in our preliminary work~\cite{tran2017}, this
......
......@@ -70,7 +70,7 @@ Figure~\ref{fig:residual1} shows the results of this experiment. We see that, i
We next study how the residual average power varies with a change in the WARP transmitter's duty cycle (varied between 20-100\%). At present, the WARP transmitter does not implement any CSMA/CA mechanism. Accordingly, a duty cycle of 100\% would imply that the AP was constantly transmitting `power packets', without leaving any opportunity for data packet transmission. Experimenting with smaller duty cycles allows us to mimic the case of a realistic WiFi AP, where such power packet transmissions occur only intermittently, only when the channel is free from other data packet transmissions.
Figure~\ref{fig:dutycycle} plots the residual energy (computed, as before, from the voltage change in the wearable's supercapacitor) as function of the duty cycle. As expected, the average differential power is approximately linear with the duty cycle. More interestingly, even with a duty cycle of 20\%, the differential power is positive ($\sim 50 \mu$W), indicating that the harvested energy is more than sufficient to operate the entire sensing device. From our prior studies~\cite{XXX}\vt{which paper}, we know that the average WiFi AP utilization in our campus deployment is below 20\%. These results thus strongly suggest that, in a well-engineered WiFi deployment, it may be possible to support the coexistence of useful WiFi RF energy harvesting with regular WiFi communication.
Figure~\ref{fig:dutycycle} plots the residual energy (computed, as before, from the voltage change in the wearable's supercapacitor) as function of the duty cycle. As expected, the average differential power is approximately linear with the duty cycle. More interestingly, even with a duty cycle of 20\%, the differential power is positive ($\sim 50 \mu$W), indicating that the harvested energy is more than sufficient to operate the entire sensing device. From our prior studies~\cite{tran2017}, we know that the average WiFi AP utilization in our campus deployment is below 20\%. These results thus strongly suggest that, in a well-engineered WiFi deployment, it may be possible to support the coexistence of useful WiFi RF energy harvesting with regular WiFi communication.
\subsection{Effect of Number of Antennas}
\begin{figure}
......
......@@ -26,10 +26,10 @@ In this section, we present the overall functional architecture of \names, detai
\label{fig:overview}
\end{figure*}
\raj{maybe some more info on the super cap. what is it? why use it? I found out by
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}
%\raj{maybe some more info on the super cap. what is it? why use it? I found out by
%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: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).
......@@ -37,7 +37,7 @@ Figure~\ref{fig:overview} shows the overall flow of \names. In this system, the
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.
\subsection{Beamforming Technique}
With the adoption of MIMO technologies in the latest 802.11n and 802.11ac WiFi standards, WiFi APs on the market are now equipped with multiple antennas: 4-antenna APs are quite commonplace, with 6\&8 antenna products also becoming increasingly available\footnote{Current examples in the market include the Aruba 320 series APs (http://www.arubanetworks.com/assets/ds/DS\_AP320Series.pdf) and XXX\am{TBD}}. The availability of such an antenna array provides us an opportunity to perform beamforming for significantly more efficient power transfer. Beamforming is traditionally used to enhance the signal strength in a specific direction for more reliable data communication. By carefully designing the amplitude and phase of the signal transmitter from each antenna, the signals at a desired direction can constructively add and the power can thus be concentrated and increased significantly. The \emph{beamwidth} is closely related to the number of antennas employed for beamforming: theoretically, the larger number of antennas, the thinner beam we can achieve and thus the higher RF power that can be concentrated at a specific location for power harvesting. Figure~\ref{fig:basicbeamwidth} shows the beamwidths that we observed in our own laboratory experiments, with both 4 and 8 antenna arrays.
With the adoption of MIMO technologies in the latest 802.11n and 802.11ac WiFi standards, WiFi APs on the market are now equipped with multiple antennas: 4-antenna APs are quite commonplace, with 6\&8 antenna products also becoming increasingly available\footnote{Current examples in the market include the Aruba 320 series APs (http://www.arubanetworks.com/assets/ds/DS\_AP320Series.pdf)}. The availability of such an antenna array provides us an opportunity to perform beamforming for significantly more efficient power transfer. Beamforming is traditionally used to enhance the signal strength in a specific direction for more reliable data communication. By carefully designing the amplitude and phase of the signal transmitter from each antenna, the signals at a desired direction can constructively add and the power can thus be concentrated and increased significantly. The \emph{beamwidth} is closely related to the number of antennas employed for beamforming: theoretically, the larger number of antennas, the thinner beam we can achieve and thus the higher RF power that can be concentrated at a specific location for power harvesting. Figure~\ref{fig:basicbeamwidth} shows the beamwidths that we observed in our own laboratory experiments, with both 4 and 8 antenna arrays.% {\color{red} Jie: Vu, there is no difference in your figure 2 between 4 and 8 antennas. There may be some errors here. Either remove 8 antenna beam or update the 8 antenna beam.}
\begin{figure}[!htb]
\centering
......@@ -47,11 +47,11 @@ 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 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.
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.
\begin{figure}[!htb]
\centering
\includegraphics[scale=0.6]{AoA.pdf}
\includegraphics[scale=0.5]{AoA.pdf}
\caption{AoA Estimation Error)}
\label{fig:musicerror}
\end{figure}
......
......@@ -41,7 +41,7 @@ Harvesting power from wireless transmissions has also been studied and
usually requires custom-designed hardware for the goal of charging RFID tags
and devices -- with WISP~\cite{sample2008} being a very well known example
that is used to power a variety of sensors. PoWiFi~\cite{talla2015powering} is the work closest
in spirit, and the precursor, to our approach. PoWiFi modifies AP firmware to transmit `power packets' (without using beamforming) on multiple free channels simultaneously, and harvests such RF energy using a matched filter on the receiver that can simultaneously harvest power across multiple channels. The authors demonstrate that such WiFi power harvesting can be used to operate low power embedded sensors at reasonably large distances (up to 20 ft away), but with relatively low duty cycles (e.g., a camera image once every 20 mins). Using beamforming to increase energy harvesting yield
in spirit, and the precursor, to our approach. PoWiFi modifies AP firmware to transmit `power packets' (without using beamforming) on multiple free channels simultaneously, and harvests such RF energy using a matched filter on the receiver that can simultaneously harvest power across multiple channels. The authors demonstrate that such WiFi power harvesting can be used to operate low power embedded sensors at reasonably large distances (up to 20 ft away), but with relatively low duty cycles (e.g., a camera image once every 20 mins). Using beamforming to increase energy harvesting
has been studied via simulations by Huang et. al~\cite{huang2016performance} and Liu et. al~\cite{liu2014multi}. To the
best of our knowledge, \name is the first working prototype to utilize directional WiFi transmissions and a triggered operation (of the wearable platform) to support sensing of human activities.
......@@ -51,7 +51,7 @@ best of our knowledge, \name is the first working prototype to utilize direction
to perform accurate beamforming to relieve sufficient RF energy. Prior
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
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
methods, such as Bharadia et. al~\cite{bharadia2013full}, Jain et.
......
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