Wireless sensor networks (WSNs) offer an attractive solution to many environmental, security and process monitoring. However, their lifetime remains very limited by battery capacity. Through the use of piezoelectric energy harvesting techniques, ambient vibration can be captured and converted into usable electricity to create selfpowering WSN which is not limited by finite battery energy. This paper investigates analytically and experimentally the performance of a WSN powered by a Piezoelectric Energy Harvesting System (PEHS) and a material block-level modeling considering most key energy consumption of a wireless sensor node in a star topology network is proposed. By using real hardware parameters of existing components, the proposed model is used to evaluate the energetic budget of the node. The sensor node performance is evaluated regarding transmit packet size, duty cycle and the number of nodes that can be deployed. From the spectral properties of the available vibration inside two moving vehicles (automobile and train), the maximal recoverable power for each type of vehicle is estimated. Using a PEHS based on a cantilever beam optimized for low-frequency applications, 6 mW power is recovered in the case of the train while a 12.5 mW power is reached in the case of the automobile. It is observed that the sink may not operate with the recovered energy. However, the sensor node can sense and transmit data with a maximum size of 105.5 kbits when the duty cycle is 4 × 10 -15. It is also achieved that the node is most effective when the measured physical phenomena vary slowly, such as the variations in temperature due to thermal inertia. Considering an optimized PEHS based on non-linear processing, it is shown that the sink can operate for 190% improvement of the recovered power.
In the recent years, energy harvesting techniques are being investigated as a mean to convert unusable forms of energy to electrical energy sufficient to power unattended or inaccessible low power systems [
The possibility of using ambient energy to increase the lifetime of the sensor nodes is linked to other developments in related technologies such as microelectronics and micromechanics to be used to design ultra-low power sensors nodes with a reasonable cost [
Specifically, energy harvesting consists of gathering freely-available energy from the environment. Several techniques of energy harvesting exist and they differ one from another by the nature of the considered primary energy source. Some of these ambient energy sources are well known now. The sun [
In [
The main objective of this work is then to assess the relevance of such micro generators through the quantification of the performance of a sensor node, powered by vibrational recovered energy. More precisely, the following questions will be addressed throughout this work: the maximum size of data that can be measured regarding available energy; the extent of the network (number of nodes); the type of physical phenomenon that can be measured by the autonomous network and the surface that can be covered by the autonomous network.
To achieve the objectives, a comprehensive energy model considering most key energy consumption of a wireless sensor node in a star topology network is proposed in Section 2. By using the hardware parameters of existing components and commonly used (Chipcorn CC1000 radio [
Section 4 presents simulations and experimental results about the performances of the piezoelectric powered sensor node and a solution for optimizing the performance of the autonomous WSN is also proposed.
Finally, the conclusion and prospects for this work are presented in Section 5.
The general architecture of a wireless sensor node is shown in
Energy consumption in the different modules of a sensor node is linked to the activity of the node in the network [
considers the energy efficiency criterion to choose the network topology to be studied. In [
As shown in
The diagram in
The sensing system is the interface to the physical world to carry out the acquisition of the data. In [
energy for data recording in the memory
with:
All the parameters used to assess the energy consumption of the sensor node are defined in
The energy consumed during the transmission of the data depends on the size of the transmitted data (b), the sink-node distance (d) and the path loss exponent n. To send a b-bit packet at a distance d, the dissipated energy is defined by [
where n is the distance based path loss exponent [
The energy for processing and aggregation of the data mainly consumed by the micro- controller
with:
Symbol | Description | Values |
---|---|---|
Transmit packet size | -- | |
Supply voltage to sensor | 2.7 V [ | |
Current: sensing activity | 25 mA [ | |
Time duration: sensor node sensing | 0.5 mS [ | |
Current: flash reading 1 byte data | 6.2 mA [ | |
Time duration: flash reading | 565 μS [ | |
Current: flash writing 1 byte data | 18.4 mA [ | |
Time duration: flash writing | 12.9 mS [ | |
Energy dissipation: electronics | 50 nJ/bit [ | |
Free space dissipation of antenna | 10 pJ/bit/m2 [ | |
Distance node-sink | 8 m [ | |
Active time of the node | 1 ms [ | |
Current: wakeup mode | 8 mA [ | |
Current: sleeping mode | 1 μA [ | |
Time duration: sleep → idle | 2450 μs [ | |
Time duration: idle → sleep | 250 μs [ | |
Sleeping time of the node | ||
Number of clock cycles per task | 0.97 × 106 [ | |
Average capacitance switched per cycle | 22 pF [ | |
Leakage current | 1.196 mA [ | |
Constant: depending on the processor | 21.26 [ | |
Thermal voltage | 0.2 V | |
Sensor frequency | 191.42 MHz [ | |
Distance sink to BS | 400 m [ | |
Two-ray dissipation of antenna | 0.0015 pJ/bit/m4 [ | |
Active time of the sink | -- | |
Sleep time of the sink | 299 mS [ |
During the operation of a sensor node, a certain amount of energy is dissipated due to the transition between the different states of the node elements (active, idle or sleep). The most relevant parts are the radio module and the MC (Micro Controller) unit. In [
The sleep time for our research varies with
Considering Equations (1), (3), (4) and (6), the total energy dissipated by the sensor node
It appears that the sink-node distance has very little influences the energy dissipated by the node. It is practically constant up to the distance of 8 m. This can be explained by the low dissipation value in free space. In this work, the value of 10 pJ/(bit/m2) is used [
The activity of the sink is much denser. The different steps performed during one cycle are shown in the diagram of
The energy dissipated during the data capture is the same as that of a classical node, namely:
In [
where N is the total number of the sensor nodes.
The energy for processing and aggregation of the data in the case of sink is defined as [
Energy dissipation due to transmission of the
A
As in the case of the classical node, the dissipated energy by the sink due to the change of state is defined by:
where
where
In
The total energy consumed by the sink node per round is expressed as:
The energy dissipated by the sink is therefore determined by the number of nodes and the sink to the base station distance.
The most popular piezoelectric generators are the cantilever structure which is very effective for low-frequency applications [
The piezoelectric beam comprises three main parts: the composite beam, the seismic mass, and the piezoelectric layers. The cantilever beam is used to amplify the relative displacement of the seismic mass to the displacement amplitude of the vibration source. The seismic mass increases the mechanical stress applied to the piezoelectric material, thus producing a high output power. The piezoelectric composite which is the active part of the structure is used to convert mechanical vibrations into electrical energy.
The alternative power generated by the piezoelectric transducer denoted in
where
with:
Regarding the cantilevers’ beams, some optimizations have been proposed. For example, a multi harvesting structure (using multiple beams) was envisaged [
Equation (17) shows that the recoverable power depends on the properties of the detected vibrations
Symbol | Description | Values |
---|---|---|
Elastic constant for the piezoelectric material | 63 Gpa [ | |
Piezoelectric charge coefficient | 320 × 10−12 C/N [ | |
Coupling coefficient | 0.43 [ | |
Equation (18) | 0.36 m | |
Damping ratio | 0.0541 [ | |
Relative permittivity | 3400 [ | |
Capacitance of the piezoelectric bender | 7.568 nF [ | |
Length of the proof mass | 17 mm [ | |
Height of the proof mass | 7.7 mm [ | |
width of the proof mass | 3.6 mm [ | |
Length of the beam | 11 mm [ | |
Length of the electrode | 11 mm [ | |
width of the beam | 3.2 mm [ | |
Thickness of the piezo layer | 0.28 mm [ | |
Thickness of the center shim | 0.1 mm [ | |
Maximum input acceleration | To measure | |
Resonance frequency | To measure |
node operating cycle can be defined by:
where
・
・
・
every 100 s; the maximum packet size, in this case, is 451 bits.
・
In the next section, the spectrum of actual, detected vibrations in two types of vehicle are investigated.
To measure ambient vibrations, an ACC103 laboratory accelerometer has been used. It has an output of
Taking into account previous work in which the maximum frequency observed was 28 Hz [
Two vehicles are considered in this work. A Kia Spectra brand automobile which has run about 126,000 km is used and a Canadian transport company (VIA Rail Canada) train. Many sets of measurements were made during the Montreal-Ottawa round trip (about 450 km) in the case of the train. More than 14,700 samples were taken. The results obtained for the car are shown in
In
frequency of 26 Hz is taken for an average peak acceleration of 0.55 g. This main frequency stays pretty close to the 28 Hz obtained in [
Using the spectral properties of the measured vibration and Equations (17)-(19), a representation of the power dissipated in a resistive load for both types of vehicles is shown in
In this section, Matlab simulations are used to evaluate the performance of the WSN. By using the maximum recoverable power, performance regarding transmitted packets size and the number of nodes for both vehicles are assessed. Given the small amount of available energy, the case of situations where measures may be made every 17 minutes were considered. Since
In the field of design of cantilever beams, it has been shown that non-linear techniques can increase the electromechanical coupling and therefore at the same time the recovered energy [
ing capacitive, the method consists of the addition in parallel of an inductance so as to form an oscillating system for amplifying the output power (
As shown in
technique allows a 160% increase of the harvested power compared to a standard energy harvesting circuit.
The relationship, between the number of nodes that can be deployed and the required gain is shown in
In this work, the performance of an autonomous WSN based on vibrational recovered energy has been studied, both numerically and experimentally. A comprehensive energy model of a sensor node, in the star topology network, is proposed and used to assess the energy budget of the node. The proposed energy consumption model is used to assess the power consumption of the sensor node. Using the parameters of existing components, it has been shown that the nodes can be deployed in an area of 200 m2 (each node being located 8 m from the sink). It is also observed that the network can be located up to 400 m from the base station.
Measurements of vibrations in two types of vehicles (automobile and train) are used to assess the maximum recoverable power. This maximum recoverable power is evaluated by using an optimized cantilever beam for low frequencies applications. Based on the measured vibrations, it is shown that powers of 12.5 mW and 6 mW can be recovered in the automobile and train respectively. This available power allows considering nodes that can measure and transmit data with a maximum size of 105 kbits, when the measurements can are taken every 17 min. However, the energy available does not allow the operation of the sink. Considering an amplification of 1.9 times the power recovered using nonlinear techniques, it is observed that the WSN can operate with a maximum capacity of 3.5 kbits when 5 nodes are deployed
N | G | N | G | N | G |
---|---|---|---|---|---|
1 | 1.774 | 11 | 1.954 | 21 | 2.134 |
2 | 1.792 | 12 | 1.972 | 22 | 2.152 |
3 | 1.81 | 13 | 1.99 | 23 | 2.17 |
4 | 1.828 | 14 | 2 | 24 | 2.188 |
5 | 1.846 | 15 | 2.026 | 25 | 2.206 |
6 | 1.864 | 16 | 2.044 | 26 | 2.224 |
7 | 1.882 | 17 | 2.062 | 27 | 2.242 |
8 | 1.899 | 18 | 2.08 | 28 | 2.260 |
9 | 1.917 | 19 | 2.098 | 29 | 2.278 |
10 | 1.936 | 20 | 2.116 | 30 | 2.296 |
around the sink. The method proposed in this work can be used to enslave a network to any ambient recoverable energy.
Although the method proposed in this work allows considering a sensor node enslaved to ambient vibrations, it would be boring to design a backup power supply for powering the node in the event of a malfunction of the vibration source (motor stop for example). The use of a hybrid micro generator (several sources of primary energy) would make robust the recovery system.
Mouapi, A., Hakem, N. and Delisle, G.Y. (2016) Autonomous Wireless Sensors Network Based on Piezoelectric Energy Harvesting. Open Jour- nal of Antennas and Propagation, 4, 138- 157. http://dx.doi.org/10.4236/ojapr.2016.43011