Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.
The previous two decades have witnessed an unprecedented advancement in radio frequency (RF) based equipment, ranging from personal and medical devices to complex civil structures’ monitoring and military systems, all with reliably precise specifications. Several wireless systems have replaced their wired counterparts, for example, personal communication using cellular phones (significantly reducing the use of landline phones) and wireless data and computer communication networks (WLANS). The advent of extremely low power processors and energy efficient RF devices has further assisted drastic development and deployment of wireless sensor networks (WSN). Examples of such applications include structural health monitoring (SHM), healthcare systems, habitat monitoring, and precision agriculture [
In both of the aforementioned alternatives, since the charging process is not instant, there are time intervals when a sensor node has not enough amount of energy to send data packets, and hence the communication is intermittent. Such a scenario creates the need for development of new protocols for communication among different nodes. The charging time
Our main contribution is the development of a 3D mathematical model that presents the real behavior of RSSI and
For data acquisition, a series of experiments were performed on P2110-EVAL-01 PowerCast Energy Harvesting Development Kit for Wireless Sensors. RF survey was also conducted to assess the capability of ambient RF energy for the purpose of energy harvesting by PowerCast energy harvester.
The rest of the paper is organized as follows. Section
The RF survey is performed to investigate the possibility of harvesting the energy from the ambient RF energy due to several sources like cellular mobile transmitters, radio stations, Wi-Fi networks, and so forth. This survey was performed by scanning the available RF power spectrum at six different locations inside the King Fahd University campus using GW Instek spectrum analyzer. Instead of showing the spectral peaks recorded at all six locations, we have shown them only for one location. In the following data, Table
Spectral power peaks.
Peak number | Peak frequency (MHz) | Peak power (dBm) |
---|---|---|
952 | −37 | |
2 | 939.5998 | −39 |
3 | 922.24 | −43.9 |
4 | 177 | −42.7 |
5 | 178 | −42.7 |
6 | 179 | −42.7 |
7 | 181 | −42.9 |
8 | 183 | −43.2 |
9 | 184 | −43.4 |
All of the following readings are taken using PowerCast omnidirectional (dipole) antenna.
Power through the system: whole spectrum (1 MHz–2.7 GHz) = −14.4 dBm; band (900 MHz–950 MHz) = −31.0 dBm; band (902 MHz–928 MHz) = −33.8 dBm; band (500 MHz–1500 MHz) = −20.0 dBm.
From the spectral peaks and powers across several bands, it was observed that none of them is even close to −10 dBm from which we may conclude that PowerCast harvester is not capable of harvesting from ambient RF energy.
It is important to note that these measurements were collected via an antenna which is optimally designed for the band 902 MHz–928 MHz. Promising measurements can be observed if an array of antennas is used, expecting the power of magnitude to be −10 dBm or even more, making the energy harvesting possible from ambient RF energy. The interested readers may read the recent paper [
The following subsections describe the hardware, experiment scenarios, data acquisition, and analysis tools followed by outdoor and indoor experiments. The parameters of interest are shown graphically as well as in tabular form.
The hardware components used in the experiments are listed as follows: P2110-EVAL-01 PowerCast Energy Harvesting Development Kit for Wireless Sensors; LabJack U6 data acquisition card; notebook computer.
The constituent components of P2110-EVAL-01 PowerCast Energy Harvesting Development Kit are listed as follows: 915 MHz, 3 W transmitter; 915 MHz directional (patch) antenna; 915 MHz omnidirectional (dipole) antenna; wireless sensor board (WSN-EVAL-01); access point: Microchip 16-bit XLP Development Board (DM240311); 2.4 GHz, IEEE 802.15.4 radio: Microchip MRF24J40 PICtail/PICtail Plus Daughter Board (AC164134-1).
Figure
Hardware components used in data acquisition.
The data acquisition was performed for two different scenarios: outdoor free-field and indoor reverberant environment.
The outdoor scenario is straightforward to imagine where there is no source of reflection for the transmitted energy reaching the harvester, except the negligible ground reflections. Such situation can be termed as an ideal one for the RF energy harvesting with no obstacle amid transmitter-receiver and zero reflections.
The indoor data acquisition is performed in two rooms of different dimensions. The smaller room has dimensions 20.5 × 9 × 8.5 ft while the larger room has dimensions 40 × 25 × 8.5 ft. Both rooms are carpeted. Two faces of each room are concrete walls with the other two faces made of steel partitions. All of these four faces are enameled. Ceilings of both rooms are made of stainless steel. Both rooms with such five faces out of total six have certain reflection coefficients with significant magnitudes, thereby making them reverberant for RF signals.
Before the description of experimental details for both scenarios, in the following, the spatial coordinates are explained where the data acquisition was performed.
In order to characterize the power harvesting, a spherical coordinate system was chosen, since the radiation patterns are well described in this coordinate system. The characteristics are more meaningful without any confusion.
The power harvester system was tested along certain radial lines, that is, for fixed azimuth (
As compared to [
Spherical coordinates for testing harvester (
Point number | Line number | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
1 | (10, 0, 0) | (10, 5, 0) | (10, 0, 16.29) | (10, 0, 30) | (10, 5, 16.29) | (10, 5, 30) |
2 | (10, 0, 0) | (15, 5, 0) | (15, 0, 16.29) | (15, 0, 30) | (10, 5, 16.29) | (15, 5, 30) |
3 | (10, 0, 0) | (20, 5, 0) | (20, 0, 16.29) | (20, 0, 30) | (10, 5, 16.29) | (20, 5, 30) |
4 | (10, 0, 0) | (25, 5, 0) | (25, 0, 16.29) | (25, 0, 30) | (10, 5, 16.29) | (25, 5, 30) |
5 | (10, 0, 0) | (30, 5, 0) | (30, 0, 16.29) | (30, 0, 30) | (10, 5, 16.29) | (30, 5, 30) |
6 | (10, 0, 0) | (35, 5, 0) | (35, 0, 16.29) | (35, 0, 30) | (10, 5, 16.29) | (35, 5, 30) |
Spherical coordinates of test points (red lines for
In order to characterize the power harvester the time taken by the capacitors to get charged, denoted by
A snapshot of data received at access point.
Charging waveform and its power spectrum.
The outdoor experiments were performed in one of the football grounds inside King Fahd University. The data was acquired for both types of antennas. In the following, we analyze the acquired data for each radial line one by one. Numerical values of the evaluated parameters are shown in tables and their trends are shown graphically. The standard deviations for mean RSSI values are also mentioned in brackets just below them in the tables. The reader should follow the convention in Table
It will be found in Tables
Parameters for radial line 1 in the outdoor experiment, azimuth = elevation = 0°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 1.0003 |
0.4000 |
3.7 | 9.1 |
2 | 15 | 0.4191 |
0.2012 |
8 | 21.3 |
3 | 20 | 0.2770 |
NC* | 16 | NC |
4 | 25 | 0.1857 |
NC | 32 | NC |
5 | 30 | 0.13 |
NC | 197.8 | NC |
Parameters for radial line 2 in the outdoor experiment, azimuth = 0°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.8311 (0.047) | 0.3317 (0.00029) | 3.7 | 12.8 |
2 | 15 | 0.4063 (0.00093) | NC | 8 | NC |
3 | 20 | 0.2216 (0.00011) | NC | 25.6 | NC |
Parameters for radial line 3 in the outdoor experiment, azimuth = 16.29°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.9309 (0.0027) | 0.2522 (0.00039) | 3.2 | 16 |
2 | 15 | 0.3778 (0.0012) | 0.155 (0.00068) | 10.6 | 64 |
3 | 20 | 0.2050 (0.00011) | NC | 21.3 | NC |
Parameters for radial line 4 in the outdoor experiment, azimuth = 30°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.6528 (0.001) | 0.2036 (0.0001) | 5.3 | 32 |
2 | 15 | 0.2379 (0.000012) | NC | 21.3 | NC |
3 | 20 | NC | NC | NC | NC |
Parameters for radial line 5 in the outdoor experiment, azimuth = 16.29°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.8885 (0.0607) | 0.320 (0.0155) | 3.5 | 17.3 |
2 | 15 | 0.4557 (0.0231) | 0.1667 (0.0115) | 17.2 | 150.3 |
3 | 20 | 0.2000 (0.01) | NC | 95.5 | NC |
4 | 21 | 0.19 (0.01) | NC | 187 | NC |
Parameters for radial line 6 in the outdoor experiment, azimuth = 30°; elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.75 (0.0346) | 0.2175 (0.0126) | 4.3 | 31.5 |
2 | 12 | 0.5672 (0.0222) | 0.1875 (0.0096) | 7.6 | 27.3 |
3 | 14 | 0.3843 (0.0222) | 0.1100 (0) | 10.9 | 114 |
4 | 15 | 0.2929 (0.0111) | NC | 12.5 | NC |
5 | 20 | 0.222 (0.011) | NC | 20.6 | NC |
6 | 22 | 0.15 (0.01) | NC | 94.5 | NC |
Parameters for radial line 1 in the indoor experiment, azimuth = elevation = 0°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 5.0410 (0.26) | 1.5561 (0.0087) | 1.6 | 0.5 |
2 | 15 | 0.9243 (0.0078) | 0.3683 (0.00092) | 10.6 | 2.7 |
3 | 20 | 0.2506 (0.000711) | 0.17 (0.00013) | 64 | 16 |
4 | 25 | 0.2818 (0.0014) | 0.3807 (0.0015) | 10.6 | 12.8 |
5 | 30 | 0.2116 (0.00083) | 0.7040 (0.0020) | 5.3 | 21.3 |
6 | 35 | 0.7767 (0.0031) | 0.9802 (0.0040) | 3.7 | 4 |
Parameters for radial line 2 in the indoor experiment, azimuth = 0°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.5809 (0.0027) | 0.4023 (0.0024) | 5.8 | 9.1 |
2 | 15 | 0.2452 (0.00027) | 0.19 (0.2111) | 18.2 | 32 |
3 | 20 | 0.2182 (0.00025) | NC | 21.3 | NC |
4 | 25 | 0.2107 (0.00025) | NC | 32 | NC |
5 | 30 | 0.2146 (000.11) | 0.244 (0.00035) | 32 | 16 |
6 | 35 | NC | NC | NC | NC |
Parameters for radial line 3 in the indoor experiment, azimuth = 16.29°, elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 0.2614 (0.0006) | 0.3713 (0.0007) | 9.1 | 21.3 |
2 | 15 | 0.1975 (0.0024) | 0.2127 (0.0002) | 21.3 | 128 |
3 | 20 | 0.5585 (0.0016) | 0.2071 (0.0001) | 21.3 | 5.8 |
4 | 25 | 0.2783 (0.0015) | 0.2327 (0.0006) | 21.3 | 16 |
5 | 30 | 0.6685 (0.0043) | 0.4124 (0.0008) | 9.1 | 4.5 |
Parameters for radial line 4 in the indoor experiment, azimuth = 30°; elevation = 5°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 2.4960 (0.0164) | 0.2256 (0.00031) | 1 | 21.3 |
2 | 15 | 0.9080 (0.0011) | 0.1487 (0.0008) | 3.3 | 64 |
3 | 20 | NC | 0.4183 (0.00047) | NC | 8 |
Parameters for radial line 5 in the indoor experiment, azimuth = 16.29°, elevation = 0°.
Serial number | Radial distance |
RSSI [mW] |
RSSI [mW] |
|
|
---|---|---|---|---|---|
1 | 10 | 1.0604 (0.0504) | NC | 2.5 | NC |
2 | 15 | 0.64 (0.0390) | 0.4000 (0.0232) | 5 | 8.1 |
3 | 20 | 0.2318 (0.0272) | 0.1367 (0.0115) | 26.7 | 92.3 |
4 | 25 | NC | NC | NC | NC |
5 | 30 | NC | NC | NC | NC |
6 | 39 | 0.1786 (0.0146) | 0.2980 (0.0179) | 46.5 | 14.5 |
Parameters for radial line 6 in the indoor experiment, azimuth = 30°; elevation = 0°.
Radial distance |
RSS [mW] |
|
Radial distance |
RSSI [mW] |
|
---|---|---|---|---|---|
10 | 0.4900 (0.0230) | 6.61 | 10 | 0.3 (0.0132) | 13.4 |
15 | 0.2880 (0.0132) | 14 | 11 | 0.48 (0.0150) | 7.1 |
16 | 0.6900 (0.0339) | 6.3 | 12 | 0.23 (0.0141) | 17.5 |
17 | 0.8462 (0.0375) | 3.7 | 16 | 0.1940 (0.0114) | 25.25 |
18 | 0.2325 (0.0175) | 23.25 | 18 | 0.222 (0.011) | 19.6 |
19 | 0.2590 (0.0173) | 18.4 | 20 | 0.1310 (0.0208) | 99.6 |
The outdoor experiment setup. The RF harvester with omnidirectional antenna and 14.1 mF charging capacitor.
Tables
RSSI versus radial distance using directional antenna in the outdoor scenario.
Charging time
In a room with the dimensions 40 × 25 × 8.5 ft, the experiments were performed along the same radial lines as in the case of outdoor experiments and the results are documented in the same way in the following. Tables
RSSI versus radial distance using directional antenna in the indoor scenario.
The indoor experiments exhibit very interesting observations. As compared to outdoor experiments, the maximum range is 35 ft for both antennas. The trends of these parameters with respect to radial distance are not as regular as those for the outdoor experiments. The parameters have very poor values at some points nearer to the transmitter and very good values at farther points. This can be attributed to the fact that five faces of the room out of six are highly reflective and caused multiple reflections of the transmitted waves. These reflections might cause complete cancellation at a nearer point while allowing enhancement of the signal at some farther points.
As compared to battery powered WSN, the placement of individual sensor nodes in wireless-powered WSN requires special attention. Firstly, the charging range of wireless power transmitter is limited by its power as well as radiation pattern. RSSI at a certain node is pragmatically supposed to be significantly different from those of others. This difference is further propagated into several communication parameters like data rate, range of transmission, and so forth. Secondly, wireless charging of the sensor nodes contributes to the intersample delay of a sensor node because a node cannot transmit a packet of data unless it is charged with enough energy, which takes a certain amount of time. Keeping these facts in view, the mathematical models of
It should be emphasized here that our work is much different from channel modeling. Channel modeling deals with the variation of different parameters of a signal in due course of propagation from transmitter to the receiver. Channel models are simply unable to give the information required in the scenario of wireless-powered WSNs. For example, it is not possible to compute charging time from channel model.
The models were obtained through data from directional antenna as described previously. A separate model is developed for each set of data with constant elevation, both for
In the modeling process, an attempt is made to reduce the number of coefficients to a possible minimum with acceptable goodness of fit. The following tables detail the obtained models. Tables
Mathematical model of
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 0.009726 |
|
Adjusted |
RMSE 0.02636 |
Mathematical model of
Model | |
| |
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 1.991 |
|
Adjusted |
RMSE 0.5334 |
Mathematical model of RSSI5 (outdoor, elevation = 5°).
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 0.009726 |
|
Adjusted |
RMSE 0.02636 |
Mathematical model of
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 1.163 |
|
Mathematical model of RSSI (indoor, elevation = 0°).
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 10.53 |
|
Mathematical model of
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 1513 |
|
Mathematical model of RSSI (indoor, elevation = 5°).
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 0.886 |
|
Adjusted |
RMSE 0.2977 |
Mathematical model of
Model | |
|
|
|
|
Coefficients | |
|
|
|
|
Goodness of fit | |
SSE 7.647 |
|
Adjusted |
RMSE 0.8745 |
The fitted surface for RSSI0 (outdoor, elevation =
The fitted surface for
The fitted surface for RSSI5 (outdoor, elevation =
The fitted surface for
The RSSI model for outdoor environment is illustrated in Figure
Figures
Figures
Comparison of (outdoor) experimental and fitted values of the parameters for radial line 1 (a, b) and radial line 5 (c, d).
Indoor modeling is very challenging and complex due to the surrounding environment. Our model, as described above, is simple and it can lend itself easily to similar environment like wholesale yard and so on. Figure
The fitted surface for RSSI0 (indoor, elevation =
On the other hand, though we have carried out several trials to enhance the correlation coefficient, the best obtained correlation coefficient is the one presented in Table
The fitted surface for
Considering the RSSI/
The fitted surface for RSSI5 (indoor, elevation =
The fitted surface for
Comparison of (indoor) experimental and fitted values of the parameters for radial line 1 (a, b) and radial line 5 (c, d).
Having studied the indoor and outdoor models, we will summarize the differences between the two models in the following points. The P2110-EVAL-01 Development Kit works well for both environments. However, the kit is optimized to work for indoor as it is observed in the obtained readings for RSSI and charging time. The harvesting can cover larger outdoor area compared to the indoor area. Nevertheless, the quality of the signal for indoor is better over the short range.
In the section, we present a few guidelines to use the above models. The RSSI models complement the For given position coordinates If we want to use the same kit, we can use these models to find the optimal positions for placing the sensors by simply differentiating RSSI with respect to For routing design, we can integrate our models with the route selection function. For a given RSSI, which can be extracted from any channel model, we can easily find the corresponding
This paper describes a series of data acquisition experiments and modeling of two important parameters for RF powered WSNs. These parameters are the charging time of a capacitor/battery powering the sensor node and the received signal strength indicator at a node. The mathematical models of these parameters are important in designing the routing protocols and WSN geometry. Extensive data are acquired in both indoor and outdoor environments. A detailed experimental procedure is explained in the paper. Modeling results are presented and discussed in detail. Then, a few guidelines for the usage of these models are suggested.
Additionally, the RF survey and the experimental results using PowerCast power harvester suggested that it is practically impossible to harvest sufficient energy for running the associated application with PowerCast. However, an optimized multiband antenna with further improvements in the electronics of the harvesting circuitry can be realized in the future to improve the level of harvested energy.
Our plan is to incorporate electromagnetic properties of reflecting walls to come up with similar models for the indoor scenario, which is much complicated as compared to the outdoor scenario.
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology (KACST) through King Fahd University of Petroleum and Minerals (KFUPM) for funding this Project, no. 09ELE758-04, as part of the National Science Technology and Innovation Plan.