sor close to its maximum operational range at 16 ft from a
Po WiFi router; the sensor is set to transmit a temperature
value over a UART interface once every minute. The router
implements the joint-optimization algorithm from “Rectifier-
aware Po WiFi transmissions section.”
We run the experiments for a total of 10 min and observed
that the temperature sensor achieves a mean time between
updates of 59. 93 s with a 0.43 s variance. More importantly,
in contrast to transmitting at high channel occupancies
(>90%) all the time, our algorithm estimated that the router
should transmit for a duration of 9 s with a 80% cumulative
occupancy and stay quiet for the remaining time. Figure 7b
shows the throughput of an ongoing TCP flow in a neigh-
boring Wi-Fi router–client pair, which shows that the aver-
age throughput significantly improves over high-occupancy
Po WiFi and is much closer to the baseline throughput with-
out any power packets. Figure 7c shows that rectifier aware
transmissions have an average per-channel occupancy of
3.3%, compared to 40% per-channel occupancy for PoWiFi
transmissions—a 10× reduction in average occupancy.
Scalable concurrent power transmissions. Finally, we provide a proof-of-concept evaluation of our concurrent transmission mechanism. Wi-Fi hardware is designed to turnaround
between decoding a packet and transmitting within a Short
Interframe Space (SIFS) duration and hence can, in principle,
easily achieve the timing requirement in Figure 4d. With only
software access to the router, we are limited to implementing Po WiFi timing using high-speed timers and the high-priority queue. Our current software system has 36. 15 µs mean
turnaround time with 4. 61 µs variance.
Using the above mean turnaround time as the silence period,
we do a proof-of-concept evaluation. To simplify implementation, we setup a USRP N210 to transmit the pattern in
Figure 4 at 30% channel occupancy. The Po WiFi routers join
this USRP transmission and concurrently transmit power
packets. We evaluate the impact on the TCP throughput of a
neighboring Wi-Fi router-client pair as we increase the number of Po WiFi routers. Figure 7d shows that as the number of
devices increases, the throughput variance slightly increases.
This is because as the number of devices increases, the variance in the turnaround time between Wi-Fi power transmissions increases. The figure, however, shows that the mean
throughput is only minimally affected as the number of
Po WiFi devices increases from 1 to 6. This shows the feasibility of scaling to multiple Po WiFi routers.
5. SENSOR APPLICATIONS
We develop Wi-Fi harvesting sensors at two ends of the
energy consumption spectrum: a temperature sensor and a
camera. We build both battery-free and battery-recharging
versions of each.
5. 1. Wi-Fi powered temperature sensor
We use our Wi-Fi harvester to convert incoming Wi-Fi signals
into DC and power an LMT84 temperature sensor and an
MSP430FR5969 microcontroller. The microcontroller reads
and transmits sensor data.
14 We optimize our sensor for power
and each temperature measurement and transmission oper-
ation consumes only 2. 77 µJ. In the battery-recharging sensor,
we use two AAA 750mAh 2. 4 V low discharge current NiMH
battery and recharge with our battery-charging harvester
14 for more details).
Experiments. We evaluate our temperature sensor by mea-
suring the update rate of the sensor as function of operating
distance. Specifically, we use a Po WiFi router and place both
the battery-recharging and battery-free sensor at increasing
distances. In the battery-free case, we measure the update
rate by computing the time between successive sensor
readings. In the battery-operated case, we measure the bat-
tery voltage and the charge current flowing into it from the
harvester. Since, each temperature measurement and data
transmission takes 2.77µJ, we compute the ratio of the
incoming power to this value to ascertain the sensor update
rate for energy-neutral operation. The average cumulative
occupancy in our experiments was 91.3%.
Results. Figure 8 shows that the update rate of both battery-recharging and battery-free version of our sensors decrease
with distance from the router. This is a result of less power
being available and consequently less power being harvested
as the distance between the router and sensor increases.
Furthermore, we observe that the battery-free sensor operates upto a distance of 20 ft whereas the battery-recharging
sensor, optimized for lower input power, has better update
rate at distances beyond 15 ft and can operate up to 28 ft from
5. 2. Wi-Fi powered camera
We use OV7670, a low-power VGA image sensor from
Omnivision, interface it with an MSP430FR5969 microcontroller and power it with our harvester. We optimize our
firmware for power and achieve a per-image capture energy
of 10. 4 mJ. On our battery-free camera, we use an ultra-low
leakage AVX BestCap 6.8mF super-capacitor as the storage element. Our battery-recharging camera consists of the
same hardware as before, but uses our wirelessly rechargeable 1 mAh lithium-ion coin-cell battery at 3.0 V (see Ref.
Experiments 1. We evaluate the camera by measuring the
time between successive frames as a function of distance from
the router. As before, we use a Po WiFi router—the observed
average cumulative occupancy was 90.9% across experiments.
At each distance, we wait for the camera to take at least six
frames and measure the time between consecutive frames.
For the battery-recharging camera, we ascertain the interframe duration for an energy-neutral image capture.
0 5 10 15 20 25 30
0 5 10 15 20 25
Figure 8. Sensor update rate. The temperature (camera) sensor can
operate up to 20 ( 17) and 28 ( 23) ft as battery-free and energy-neutral
battery-recharging, respectively. (a) Temperature Sensor (b) Camera.