Commit bf149628 authored by Archan MISRA's avatar Archan MISRA

first full related work

parent 137e1b77
......@@ -273,4 +273,87 @@ title={Wearable textile microstrip patch antenna for multiple ISM band communica
year={2013},
doi={10.1109/APS.2013.6711588},
month={July},
}
\ No newline at end of file
}
@inproceedings{hester2017,
author = {Hester, Josiah and Sorber, Jacob},
title = {Flicker: Rapid Prototyping for the Batteryless Internet-of-Things},
booktitle = {Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems},
series = {SenSys '17},
year = {2017},
location = {Delft, Netherlands},
doi = {10.1145/3131672.3131674},
publisher = {ACM},
}
inproceedings{ryokai2014,
author = {Ryokai, Kimiko and Su, Peiqi and Kim, Eungchan and Rollins, Bob},
title = {EnergyBugs: Energy Harvesting Wearables for Children},
booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
series = {CHI '14},
year = {2014},
location = {Toronto, Ontario, Canada},
doi = {10.1145/2556288.2557225},
publisher = {ACM},
address = {New York, NY, USA},
}
@article{hande2007,
author = {Hande, Abhiman and Polk, Todd and Walker, William and Bhatia, Dinesh},
title = {Indoor Solar Energy Harvesting for Sensor Network Router Nodes},
journal = {Microprocess. Microsyst.},
issue_date = {September, 2007},
volume = {31},
number = {6},
month = sep,
year = {2007},
doi = {10.1016/j.micpro.2007.02.006},
publisher = {Elsevier Science Publishers B. V.},
}
@inproceedings{lin2005,
author = {Lin, Kris and Yu, Jennifer and Hsu, Jason and Zahedi, Sadaf and Lee, David and Friedman, Jonathan and Kansal, Aman and Raghunathan, Vijay and Srivastava, Mani},
title = {Heliomote: Enabling Long-lived Sensor Networks Through Solar Energy Harvesting},
booktitle = {Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems},
series = {SenSys '05},
year = {2005},
location = {San Diego, California, USA},
doi = {10.1145/1098918.1098974},
publisher = {ACM},
}
@article{xu2013,
author = {Xu, Guodong and Yang, Yang and Zhou,Yixin and Liu, Jing},
title = {Wearable thermal energy harvester powered by human foot},
publisher = {Front. Energy},
year = {2013},
journal = {Frontiers in Energy},
volume = {7},
number = {1},
eid = {26},
doi = {10.1007/s11708-012-0215-9}
}
@ARTICLE{sample2008,
author={A. P. Sample and D. J. Yeager and P. S. Powledge and A. V. Mamishev and J. R. Smith},
journal={IEEE Transactions on Instrumentation and Measurement},
title={Design of an RFID-Based Battery-Free Programmable Sensing Platform},
year={2008},
volume={57},
number={11},
pages={2608-2615},
doi={10.1109/TIM.2008.925019},
month={Nov},}
@inproceedings{vasisht2016,
author = {Vasisht, Deepak and Kumar, Swarun and Katabi, Dina},
title = {Decimeter-level Localization with a Single WiFi Access Point},
booktitle = {Proceedings of the 13th Usenix Conference on Networked Systems Design and Implementation},
series = {NSDI'16},
year = {2016},
location = {Santa Clara, CA},
pages = {165--178},
publisher = {USENIX Association},
}
\section{Related Work}
\label{sec:relatedwork}
Related work.
There has been a wide variety of related work in the broad areas of energy harvesting, including WiFi/RF energy harvesting, low-power wearable design and WiFi beamforming.
We classify related work into categories related to our two key concepts: WiFi-based energy harvesting and indoor localization.
%Energy harvesting papers \cite{talla2017battery} and WISP platform, some commercial products
%
%Localization/Activity recognition \cite{xiong2013arraytrack} \cite{pu2013whole}
%
%Passive sensing \cite{bharadia2013full} and PIR sensors
%
%Simultaneous Wireless Information and Power Transfer (SWIPT)
\subsection{Energy Harvesting for Client Devices}
There is a significant body of literature on energy harvesting for batteryless wearable and embedded sensing devices. The most popular modes of energy harvesting are light, kinetic energy and thermal gradients. Ambient and solar lighting provides some of the highest amount of harvested power: over a decade ago, Heliomotes~\cite{lin2005} were designed to support embedded sensors using solar power, whereas Hande et al~\cite{hande2007} demonstrated the feasibility of powering indoor wireless routers (albeit in a non-wearable form factor) using energy harvested from indoor fluorescent lights. Kinetic energy is another popular source for energy harvesting. For example, the EnergyBug prototype~\cite{ryokai14} focused on harvesting kinetic energy, generated by the body movements of children, to power a wrist-mounted wearable device, whereas SolePower~\cite{XXX} developed a shoe insole that harvests kinetic energy (when a person is walking). More recently, the Flicker prototype~\cite{hester2017} presented a platform for rapid prototyping of energy harvesting-based sensors: the software abstractions allow a device to harness the combined capabilities of different energy harvesting modes, including solar, RFID and kinetic energy, in a federated storage infrastructure. Thermal energy harvesting is based on the principle of using temperature gradients between the human body and the surrounding environment to generate an electrical charge. Examples of sensing prototypes include Thermes~\cite{campbell2014}, an embedded sensor which uses such temperature gradients to detect water usage events in buildings and the work by Xu et al~\cite{xu2013}, which demonstrated the ability to harvest energy using the thermal gradient between a shoe's insole and the external ground. All of this work has two broad characteristics: (i) their power generation capability is dependent on certain context (e.g., kinetic harvesters work only when the user exhibits movement), and (ii) when applied to wearable form factors, they tend to perform relatively low-power tasks and do not usually operate power-hungry sensors, such as an accelerometer.
\subsection{WiFi \& RF harvesting}
Harvesting of wireless transmission power has also been explored, especially in the context of charging RFID tags and devices. Most of the existing solutions for delivering RF power transfer require custom-designed hardware. The WISP platform~\cite{sample2008}, which operates a 16-bit ultra low power microcontroller based exclusively on harvested RF energy, is probably the best known energy harvesting-based embedded sensing platform, and supports the operation of sensors such as ambient light, temperature and orientation. Recent work has specifically looked at the promise of harvesting WiFi-based RF energy for batteryless operation. The PoWiFi system~\cite{talla2015powering} demonstrates the potential of power transmission using the existing Wi-Fi chipsets by modifying the firmware of the access point (AP), and showed that it is practically possible to harvest energy from WiFi transmissions. However, this system does not explore the possibility of beamforming to improve the harvesting energy. Limited additional work has explored the possibility of energy harvesting from beamformed RF transmissions, but only via simulations. Huang et.al~\cite{huang2016performance} explore the operation of a wirelessly-powered communication network (WPCN), by considering a multi-antenna AP, and deriving the optimal energy harvesting time that maximizes the throughput achievable via an energy-harvesting client. Liu et.al.~\cite{liu2014multi} use a similar multi-antenna AP model to maximize a client's throughput in a WPCN environment, by optimally dividing the time between uplink data transmissions and and downlink energy beamforming.
\subsection{WiFi-based Localization \& gesture recognition}
Our proposed \name framework is based on the ability to perform sufficiently accurate tracking of a wearable, potentially mobile, device, so that the WiFi AP can deliver an adequately high quantity of RF energy to the receiver. To be able to steer a beam toward a user, we need to know his/her relative direction to the AP. Recent work has utilized multi-antenna deployments to track a mobile device with high accuracy. For example, ArrayTrack \cite{xiong2013arraytrack}, uses multiple multi-antenna APs to localize a device (smartphone) in office environment, with an error bound of ~20cm. Each AP calculates the angle of arrival of the WiFi signal independently; these estimates are then combined to infer the device's precise location. More recently, the Chronos prototype~\cite{vasisht2016} uses sub-nanosecond estimation of time of flight to perform indoor localization of client devices, using a single AP, to a median accuracy of 65 cm. However, these approaches require active RF transmissions by the mobile device.
More recently, researchers have also investigated \emph{device-free} approaches, which recognize an individual's location or gestures without requiring the user to wear any device. As an example, the WiSee system~\cite{pu2013whole} proposed a technique to recognize hand gestures, based on the doppler shift of a transmitted signal that is captured and analyzed by a separate receiver device. To support operation on a single AP, past work~\cite{bharadia2013full,jain2011practical} has demonstrated the ability to achieve full-duplex RF transmission, which implies that a single WiFi AP's antennas can be used to both transmit and receive (the reflected signal) simultaneously. Using such techniques, recent approaches such as IndoTrack~\cite{li2017} have demonstrated how passive tracking of RF signals reflected by the human body can be used for localization. However, such device-free approaches are still not robust enough to be deployed in arbitrary environments, especially when multiple human occupants are present.
%\subsection{Simultaneous Wireless Information and Power Transfer (SWIPT)}
%\am{Jie--can you add a little text and some references here?}
\ No newline at end of file
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment