Commit 0e7b0c16 authored by Tran Huy Vu's avatar Tran Huy Vu
parents 8b191fcd 0cfdd949
\section{Discussion}
\label{sec:discussion}
Our research results, using an admittedly `proof-of-concept' wearable platform, show that the \name paradigm of beamformed WiFi energy harvesting can be used to monitor the gestural activities of individuals, at least in an office-like environment. There are, however, several open issues and considerations that need further exploration.
Our results, using an admittedly `proof-of-concept' wearable platform, show that \namef paradigm of beamformed WiFi energy harvesting can be used to monitor the gestural activities of individuals, at least in an office-like environment. There are, however, several open issues to explore further.
\textbf{Beamforming for Multiple Receivers:} The bulk of our studies have considered just a single wearable device. Limited studies (in Section~\ref{sec:multiuser}) show that, when multiple client devices are present, an AP can judiciously either time-multiplex its power packet transmissions to different devices, or adjust its beamwidth to simultaneously cover multiple devices, albeit with lower harvesting power/device. Such multi-device environments require the WiFI AP to optimize its operations carefully considering coverage vs. energy density tradeoffs (as a function of beamwidth), similar to the prior exploration of rate vs. coverage tradeoffs for multicast wireless traffic~\cite{chou2006} or throughput maximization under directional transmissions~\cite{sen2008}. In addition, different client devices may have different residual energy and/or different priorities in energy harvesting policies. In particular, clients can transmit `ping' packets that encode the criticality of their energy demand, thereby allowing an individual WiFi AP to dynamically adjust its optical choices of (beamwidth, direction, timeslice).
\textbf{Beamforming for Multiple Receivers:} The bulk of our studies have considered just a single wearable device. Limited studies (in Section~\ref{sec:multiuser}) show that, when multiple client devices are present, an AP can judiciously either time-multiplex its power packet transmissions to different devices, or adjust its beamwidth to simultaneously cover multiple devices, albeit with lower harvesting power/device. Such multi-device environments require the WiFI AP to optimize its operations carefully considering coverage vs. energy density tradeoffs (as a function of beamwidth), similar to the prior exploration of rate|throughput|coverage tradeoffs in broadcast wireless networks~\cite{chou2006,sen2008}. In addition, different client devices may have different residual energy and/or different harvesting priorities. In particular, clients can transmit `ping' packets that encode the criticality of their energy demand, thereby allowing an individual WiFi AP to dynamically adjust its optical choices of (beamwidth, direction, timeslice).
\textbf{Multi-AP Operation:} Our controlled user studies have been performed using a single WiFi AP. In a practical campus or factory environment, multiple APs are likely to `cover' a specific location (from measurements, the typical number of APs overhead at a location in our campus buildings is 5-6). This opens up additional possibilities. First, the presence of multiple APs can lead to an additive increase in the harvested energy, as a single client device can receive RF energy from multiple transmitters. On the other hand, as suggested by Figure~\ref{fig:edev2beam}, beams on the same channel may also interfere destructively. However, this will also require enhancements to the RF harvester module on the wearable (similar to the design innovations in~\cite{talla2015powering}), to allow the harvester to simultaneously have multiple resonant frequencies, each corresponding to a different AP's operating frequency. The benefits of such added harvested energy vs. additional receiver complexity are unclear and require further investigation. Second, our current WARP-based AP implementation focused only on power packet transmissions. In practice, each AP will have to perform CSMA-based channel access to avoid contentions with the data traffic transmissions. Moreover, if RF charging may be viewed as a secondary benefit of WiFi APs, it is important to adjust the schedule \& duty cycle of power packet transmissions (e.g., by using multiple virtual queues similar to NAPman~\cite{rozner2010}) to ensure that they do not cause unacceptable loss or latency of data packets.
\textbf{Multi-AP Operation:} Our controlled user studies have been performed using a single WiFi AP. In a practical campus or factory environment, multiple APs are likely to `cover' a specific location (from measurements, the number of APs overheard at a typical location in our campus buildings is 5-6). This opens up additional possibilities. First, the presence of multiple APs can lead to an additive increase in the harvested energy, as a single client device can receive RF energy from multiple transmitters. On the other hand, as suggested by Figure~\ref{fig:edev2beam}, beams on the same channel may also interfere destructively. However, this will also require enhancements to the RF harvester module on the wearable (similar to the design innovations in~\cite{talla2015powering}), to allow the harvester to simultaneously utilize multiple resonant frequencies, each corresponding to a different AP's operating channel. The benefits of such added harvested energy vs. additional receiver complexity are unclear and require further investigation. Second, our current WARP-based AP implementation focused only on power packet transmissions. In practice, each AP will have to perform CSMA-based channel access to avoid contentions with the data traffic transmissions. Moreover, as RF charging may be viewed as a secondary benefit of WiFi APs, it is important to adjust the schedule \& duty cycle of power packet transmissions (e.g., by using multiple virtual queues, similar to NAPman~\cite{rozner2010}) to ensure that they do not cause unacceptable loss or latency of data packets.
\textbf{Additional \& Improved Energy Harvesting:} One of our key contributions is to demonstrate that, with appropriate beamforming, standards-compliant WiFi transmissions can provide harvestable power levels of O(100$\mu$W) at reasonable distances (2-3m). In contrast, as an exemplary alternative,
ambient light energy (indoors) is projected to provide~\cite{yildiz2007} a harvestable power density of 100$\mu$W/cm$^2$, implying a harvesting capability of roughly around 1200$\mu$W for a typical $12cm^2$ smartwatch surface. It is entirely possible that, depending on the use case, wearables may combine WiFi/RF harvesting with other other ``traditional'' harvestable energy alternatives, such as ambient light and vibrations. The attractiveness of the \name prototype comes from our belief that WiFi is more pervasive and that WiFi-based harvesting can occur continuously (e.g., at night, in a dark room). In addition, our wearable prototype uses a straightforward whip antenna for energy harvesting. The antenna has a gain of 2.1 dBi, but it's performance is poor in the vertical direction. It is very likely that alternative antenna form factors (e.g., a metallic strip-based ``patch antenna, illustrated in Figure~\ref{fig:patchantenna}) can increase the harvested energy significantly (see~\cite{chen2013})--the development of suitable design forms for such RF-powered wearables remains an open question.
ambient light energy (indoors) is projected to provide~\cite{yildiz2007} a harvestable power density of 100$\mu$W/cm$^2$, implying a harvesting capability of roughly around 1200$\mu$W for a typical $12cm^2$ smartwatch surface. It is entirely possible that, depending on the use case, wearables may combine WiFi/RF harvesting with other other ``traditional'' harvestable energy alternatives, such as ambient light and vibrations. The attractiveness of the \name prototype comes from our belief that WiFi permits more pervasive and continuous harvesting (e.g., at night, in a dark room). In addition, our wearable prototype uses a straightforward whip antenna for energy harvesting. The antenna has a gain of 2.1 dBi, but it's performance is poor in the vertical direction. It is very likely that alternative antenna designs (e.g., a metallic strip-based ``patch antenna, illustrated in Figure~\ref{fig:patchantenna}) can increase the harvested energy significantly (see~\cite{chen2013}). The development of suitable design forms for such RF-powered wearables remains an open question.
\begin{figure}[!h]
\centering
......@@ -16,6 +16,6 @@ ambient light energy (indoors) is projected to provide~\cite{yildiz2007} a harve
\label{fig:patchantenna}
\end{figure}
\textbf{Other Application Domains \& Paradigms:} Our investigations in this paper have been confined to the use of a single AP, with 1-2 users, in an office-like setting. Variations of the core \name concept may find applicability in other settings. For example, WiFi AP-based energy harvesting may be used to capture key locomotion and gesture-related behaviors (e.g., fall \& eating detection) by elderly inhabitants in smart home environments. Similarly, such WiFi harvesting may be used by static sensors, deployed in industrial sites, warehouses and offshore platforms. In such cases, the RF power may be delivered not by static WiFi APs, but by mobile drones which move around to both collect data (like a data mule) and deliver energy (like a postman). Our current experience, however, suggests that, even with the increased power efficiency of beamformed WiFi transmissions, the client devices will need to adopt an event-triggered sensing paradigm, as the energy is unlikely to be enough to sustain continuous sensing.
\textbf{Other Application Domains \& Paradigms:} Our investigations in this paper have been confined to the use of a single AP, with 1-2 users, in an office-like setting. Variations of the core \name concept may find applicability in other settings. For example, WiFi AP-based energy harvesting may be used to capture key locomotion and gesture-related behaviors (e.g., fall \& eating detection) of elderly inhabitants in smart homes. Similarly, such WiFi harvesting may be used by static sensors, deployed in industrial sites, warehouses and offshore platforms. In such cases, the RF power may be delivered by mobile WiFi drones which move around to both collect data (like a data mule) and deliver energy (like a postman). Our current experience, however, suggests that, even with the increased power efficiency of beamformed WiFi transmissions, client devices will require an event-triggered sensing approach, as the harvested energy is unlikely to sustain continuous sensing.
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