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Example for DecentralizedDataAggregation Sung‐Han Sim
This application is for data acquisition and processing based on a decentralized hierarchical sensor network. This application supports multiple sensor clusters, in which data processing is conducted independently to other clusters. The output of DecentralizedDataAggregation is either correlation or random decrement function estimated in each local sensor community.
This document describes an example for a dynamic testing to provide a clear picture how DecentralizedDataAggregation incorporated with Imote2 sensors can be used.
1. Description of Testing
Test bed
This example testing utilizes a box fan (see Figure 1) that can be easily prepared and provides substantial vibration as well. The adjustable speed control allows three different vibration levels.
Figure 1. Imote2 sensors on the box fan
Sensor nodes and sensor groups
Four Imote2 sensors are attached to the box fan as shown in Figure 1. There are three types of Imote2 nodes according to their role in the sensor network.
• Gateway node: Herein, Node 116 is the gateway node that is the interface between the user and the sensor network. The gateway node is connected to the base station via the USB‐miniB cable (see Figure 2). The gateway node conveys the user input to the sensor network and retrieves the sensor data and the correlation function.
• Cluster head: The cluster heads, which are Node 40 and 86, interact between the gateway node and leaf nodes. The cluster head shares the received commands from the gateway node with
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the leaf nodes in its sensor group, and also serves as the reference when calculating the correlation function in each group.
• Leaf node: The leaf node receives commands and reference from the cluster head to calculate the correlation function that is sent back to the cluster head.
To fully illustrate the decentralized network feature implemented in DecentralizedDataAggregation, the sensors are divided into two local sensor groups.
• Sensor group 1: Node 40 and 83 • Sensor group 2: Node 86, 83 and 88
Nodes 40 and 86 (with underlines in the above) are the cluster heads in each group.
Figure 2. Gateway node connected to the base station
Parameters
The selected parameters for this example are the same as the default parameters except the sensing channels, which are all three (x, y, and z) channels.
• NFFT = 1024 • number of averages = 20 • number of overlaps = 512 • window option = 3 (Hanning window) • filter = 0 (no filter is used.) • normalize = 1 • sampling rate = 280 • cutoff frequency = 100 (because the filter is disabled, this cutoff frequency is not applied). • reference channel = 3 • channel mask = 123 (All three channels are used). • save data = 1 (Time history records are retrieved). • save CFE = 1 (Correlation functions are retrieved).
Running the Application
Prepare the testing as follows.
• Compile the DecentralizedDataAggregation application • Download the compiled binary file to each imote2 sensors including the gateway node and each
sensor node • Enter autocomm –d COMx in the Cygwin window and press the ENTER key until the BluSH
prompt is shown.
More detailed information for the above procedure can be found in README.TXT in the directory where this document is located.
Once the BluSH prompt is available, input the following commands to collect the correlation/random decrement functions.
1. Set up sensor topology
BluSH> SetDDASensorTopology <group 1 nodeId> <group 1 nodeId> ... <group 1 nodeId> 0 <group 2 nodeId> <group 2 nodeId> ... <group 2 nodeId> 0 ...
or
BluSH> SetDDASensorTopology <group 1 nodeId> <group 1 nodeId> ... <group 1 nodeId>
BluSH> SetDDASensorTopology <group 2 nodeId> <group 2 nodeId> ... <group 2 nodeId>
...
*The local sensor groups are separated by 0. The first nodes of each group are the cluster heads, and the rest of them are leaf nodes.
2. Use either SetDDANExTParameters or SetDDARDTParameters to provide appropriate sensing and data processing parameters. SetDDANExTParameters and SetDDARDTParameters result in correlation and random decrement functions, respectively.
BluSH> SetDDANExTParametres <nfft> <navg> <overlap> <window> <filter> <normalize> <samplingRate> <cutoff> <refChannel> <channelMask> <save data?> <save cf?>
• nfft: number of FFT points • navg: number of averaging windows • overlap: number of overlapping points between two consecutive spectral windows • window: specifies the spectral window
o 1 = Welch window o 2 = Hamming window o 3 = Hanning window
• filter: specifies if the low pass filter is applied. Use 1 to apply or 0 not to apply
• normalize: specifies if the mean value of sensor signal is subtracted to remove the DC component. Use 1 to subtract or 0 not to subtract
• samplingRate: sampling frequency in Hz • cutoff: cutoff frequency for the low pass filter • refChannel: the reference channel used in estimating correlation functions • channelMask: specifies sensing channels. (e.g., 1 for channel 1, 12 for channels 1 and 2, and 123
for channels 1, 2, and 3) • save data?: use 1 to collect raw sensor data at the base station, or 0 not to collect • save cf?: use 1 to collect the estimated correlation function, or 0 not to collect
BluSH> SetDDARDTParameters <data length> <RDF length> <trig level a> <trig level b> <sampling rate> <ref ch> <ch mask> <save data?> <save rdf?>
• data length: number of sensor data in a channel • RDF length: length of a random decrement function • trig level a: lower limit of triggering interval • trig level b: upper limit of triggering interval • sampling rate: sampling frequency in Hz • ref ch: the reference channel used in estimating correlation functions • ch mask: specifies sensing channels. (e.g., 1 for channel 1, 12 for channels 1 and 2, and 123 for
channels 1, 2, and 3) • save data?: use 1 to collect raw sensor data at the base station, or 0 not to collect • save rdf?: use 1 to collect the estimated random decrement functions, or 0 not to collect
3. Run DecentralizedDataAggregation
BluSH> StartDDA
Example for correlation function estimation
BluSH> SetDDASensorTopology 40 83 0 86 83 88 BluSH> SetDDANExTParameters 1024 20 512 3 0 1 280 100 3 123 1 1 BluSH> StartDDA
Example for random decrement estimation
BluSH> SetDDASensorTopology 40 83 0 86 83 88 BluSH> SetDDARDTParameters 10000 500 512 1 2 280 3 123 1 1 BluSH> StartDDA
Troubleshooting
The following problems are often reported.
• Sensing failure (The blue LED shall be turned off if sensing is successful). o Check if the batteries are fully charged. o Check if the sensor board is securely connected to the Imote2 (see Figure 3). o Too many USB‐connected Imote2’s may result in a sensing failure.
• The application does not proceed after sensing. o Check if the batteries are fully charged.
• A cluster head in two sensor groups will prevent the application from properly working. o A cluster head should belong to one group. It should NOT be shared with multiple
groups.
If any unknown failure persists, please report the problem.
(a) Securely connected (b) Loosely connected
Figure 3. Connection between the Imote2 and sensor board
2. Results
As previously specified, sensor data and correlation functions are collected to the gateway node. Figures 4, 5, and 6 show the sensor data for each speed. Figures 7, 8, and 9 show the correlation functions for Node 83 for each speed, and compare correlation functions from Imote2 sensors and MATLAB, which are shown to be identical.
3. Conclusion
DecentralizedDataAggregation is tested with the box fan. Four imote2 in two local sensor groups are used. The testing is repeated for three different fan speeds, and the sensor data and correlation functions are collected. Comparing with correlation functions calculated on MATLAB using the sensor data, the DecentralizedDataAggregation application successfully estimates the correlation function. The box fan example illustrated in this document is expected to be easily repeated by the users.
Figure 4. Sensor data: low speed.
Figure 5. Sensor data: medium speed.
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Figure 6. Sensor data: high speed.
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(a) Sensor group 1
(b) Sensor group 2
Figure 7. Correlation function for Node 83: low speed
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Correlation Function for Node 83 in Group 1
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Correlation Function for Node 83 in Group 2
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Figure 8. Correlation function for Node 83: medium speed
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Correlation Function for Node 83 in Group 1
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Correlation Function for Node 83 in Group 2
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Figure 9. Correlation function for Node 83: high speed
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Correlation Function for Node 83 in Group 1
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