# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 5 Prototype  
To validate the proposed design experimentally, a hybrid RF node design has been implemented based on the PowerCast P21 XXCSR-EVB RF Energy Harvesting development kit (Powercast, Pittsburgh, PA, USA), which can harvest energy from six frequency bands and store energy in a built-in 50-mF capacitor or an external capacitor, connected through an extension port; see Figure 7. The 868-MHz ISM band has been selected as it has 865.6-867.6 MHz sub-band, which allows 3-W EIRP transmission in the UK [6]. The 868-MHz RF charging signal has been generated by a Rohde&Schwarz SMC 100 (Rohde&Schwarz, Munich, Germany) A signal generator with an AML 32210 Amp and a 6-dii transmitting antenna. The board harvests energy from \(-15\) to 15 dBm, provides three output voltage levels of 3.3 V, 4.1 V and 4.2 V and delivers up to 50 mA. With this setup, the board can harvest the energy from a 3-W 866-MHz source at a distance of 11 m. The purpose of this section is to demonstrate that the concept is practical and can be implemented using off-the-shelf hardware with minimum costs rather than reproducing and comparing with large-scale simulation experiments. This is because deployment and measurements within an RF energy harvesting environment over a large geographical area would be extremely challenging in terms of resources and logistics. Developing a case study, where RF energy harvesting platform is used for a real application, and reporting the results are potential future work.  
The XLP 16-bit development kit (Microchip Technology, Chandler, AZ, USA) used as the sensor node consists of a PIC24F16K102 microcontroller, the temperature sensor MCP9700 and a MiWi  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Hybrid Capacitor, F** & 0.01 & 0.02 & 0.03 & 0.04 & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 & 0.10 \\ \hline
**Energy, mJ** & 12.84 & 25.60 & 38.44 & 51.28 & 64.12 & 76.95 & 89.79 & 102.63 & 115.47 & 128.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Peak usable energy in energy storage near home stations.  
Figure 6: The number of sensor activations near charging stations in the fixed capacitor configuration drops rapidly with capacitor size as the node is less likely to boot on a larger capacitor.  
MRF24J40 MA short-range low power transceiver. The MCP9700 is a low power thermistor operating at 6 \(\upmu\)A. The sensor node is powered through an RF energy harvesting P21 XXCSR-EVB board and can change its energy storage through a low power switching circuit that we have designed, based on the P1 bi-stable V23026 relay, operating on 3 V. The algorithm has been implemented in MPLAB X IDE using the C 30 compiler, which produces a 51-kB HEX file with 81% of programming memory allocated for code and 24% of data memory for data. The impact of RF energy prediction on the overall energy consumption has been evaluated by measuring the energy consumption of the basic sensing application and then measuring the energy consumption of the same sensing application, but with the algorithm implemented. The energy consumption was measured by connecting an oscilloscope to the jumper 9 of the XLP development kit and the connecting 22-Ohm shunt resistor in the socket. The energy consumed for the basic sensing task in which the sensor was programmed to sense periodically, save data to flash and sleep was measured as 7.93 \(\times\) 10\({}^{-5}\) J; whereas when the sensor node executed the RF sensing with energy prediction, the task energy cost increased to 9.2 \(\times\) 10\({}^{-5}\) J, which represents only a 16% increase due to the need to sense Received Signal Strength (RSSI). Table 3 shows the measured power consumption in different modes. Figure 8 shows the energy profiles of a simple sensing application (a) and a sensing application with an implemented RF prediction algorithm.