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, 6 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 paper.

4. EVALUATION

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 about 90%.

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

δt

0T C

0

Vth

0

Occupancy

Voltage

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.

Power Pkt DIFS

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.