issue with each Po WiFi router independently introducing
power packets is that such a system would not preserve network performance in the presence of many Po WiFi routers.
Useful Wi-Fi capacity would degrade at least linearly with
the number of Po WiFi routers.
To address this scaling problem, we enable concurrent
transmissions from Po Wi Fi routers that are in decoding range
of one another. Our key insight is that since power packets
do not contain useful data, transmissions from multiple
Po WiFi routers can safely collide. Further, if each Po WiFi
router transmits a random power packet, we can ensure that
concurrent packet transmissions do not destructively interfere to reduce the power available to harvesters.
Specifically, in our system, we have a leader Po WiFi router
that transmits the energy pattern as shown in Figure 4. The
pattern consists of a short packet with a 1-byte payload transmitted at 54 Mbps, followed by a Distributed Interframe Space
(DIFS) period and then a power packet. Other Po WiFi routers
decode this short packet and join the packet transmission of
the leader router within the DIFS period. This strategy ensures
that all nearby Po WiFi routers transmit power packets concurrently and hence do not reduce the Wi-Fi network’s capacity.
As in previous work that used concurrent transmissions,
we enable follower routers to transmit simultaneously in
software by adjusting contention-window and noise-floor
parameters to prevent carrier-sense backoff, and by placing
power packets in the high-priority queue. However, Po WiFi
could not turnaround and begin transmission within from
the software layer within a DIFS duration; we believe that
with better access to the router’s hardware queues, Po WiFi
could turnaround within a DIFS period. Further, one can
design distributed algorithms to find the leader router
whose transmissions can be decoded by all other Po WiFi
routers, but we consider this to be outside the scope of this
We build our harvester prototypes using commercial off-the-shelf components on printed circuit boards. We implement
Po WiFi routers using three Atheros AR9580 chipsets that independently run the algorithm in Section 3. 1 on channels 1,
6, and 11. The chipsets are connected via amplifiers to 6 dBi
Wi-Fi antennas separated by 6.5cm. Our prototype router
provides Internet access to its associated clients on channel
1 via NAT and transmits at 30 dBm, the FCC limit for power in
the ISM band. All our sensor and harvester benchmark evaluations were performed in a busy office network where the
average cumulative occupancy across the three channels was
Both power and data packets contribute to our router’s
We next describe how Po WiFi can modulate its channel
occupancy using this empirical model, while minimizing its
effect on neighboring Wi-Fi networks.
Joint optimization for efficient power delivery. To reduce
the impact of power packets on neighboring Wi-Fi networks,
Po WiFi must minimize the total number of power packets
required to collect a sensor reading. Our key intuition is
that when there are packets on the air, the capacitor charges
exponentially. However, when there are no packets, the voltage on the capacitor discharges exponentially. To maximize
the effectiveness of power delivery, Po WiFi must minimize
capacitor leakage. We achieve this by using the channel-occupancy modulation scheme described above and shown
in Figure 3. In every sensor update time window ( T), the
router transmits no power packets for a period (T − δt), then
transmits power packets for a period of δt, targeting a channel occupancy of 0 < C ≤ 1. When the channel occupancy
is zero, the voltage on the capacitor is very low and there is
no leakage. However, when a sensor update is required, a
high channel occupancy continuously charges the capacitor (minimizing leakage) to maximizes the effectiveness of
power delivery. Our goal is to find δt and C to minimize the
mean of the power packet occupancy given by .
We find these values by substituting different C and δt in
our empirical model and computing the minimum value.
We reduce the search space by noting that for a given Pin,
there is a minimum value of C below which the threshold
voltage is not achievable. Further, given a channel occupancy, we know the time constant that limits δt to a maximum value of t (Pin, C). Finally, we limit the granularity by
which channel occupancy can be modulated to 10%. Using
these values we reduce the search space to 75 points.
We note two main points. First, the above description
assumes that the router can estimate the available power,
Pin, at the sensor. To bootstrap this value, Po WiFi initially
transmits power packets at a high occupancy of around 90%
and notes the times when the sensor outputs a reading.
Po WiFi uses our empirical model to estimate Pin for the next
cycle. At the end of every cycle it re-estimates Pin to account
for wireless channel changes. Second, in the presence of
multiple sensors, we can optimize the parameters to satisfy
the minimum duty-cycle requirement across all the sensors,
but we omit this simple extension for brevity.
Scaling with concurrent Po WiFi transmissions. A practical
Figure 3. Rectifier-aware power Wi-Fi transmissions and
corresponding rectifier voltages. The plot shows the optimized
rectifier-aware Po WiFi transmission and the corresponding voltage
at the storage capacitor. Vth = 2.4V and δt = 10s for a temperature
sensor reading every minute at the maximum operating distance.
Figure 4. Energy pattern for concurrent power packet transmissions.
It consists of a short packet with a 1-byte payload transmitted at
54 Mbps, followed by a DIFS period and a power packet transmission.