Poster Abstract: A Quantitative Error Analysis ofSynchronized Sampling on Wireless Sensor Networks forEarthquake Monitoring
Makoto Suzuki†, Shunsuke Saruwatari†, Narito Kurata‡,
Masateru Minami†, Hiroyuki Morikawa††Graduate School of Frontier Sciences

Kajima Corporation{makoto, saru, minami, mori}@mlab.t.u-tokyo.ac.jp

kuratan@kajima.com

Categories and Subject Descriptors: C.3 [Special-Purposeand Application-Based Systems]: Real-time and embedded
systems
General Terms: Design, Experimentation
Keywords: Wireless Sensor Networks, Synchronized Sampling, Error Analysis, Earthquake Monitoring

1.

INTRODUCTION

Wireless sensor network technology enables low-cost andhigh-density earthquake monitoring. The earthquake monitoring measures structural vibrations caused by earthquakes.
The earthquake monitoring contributes the progress of earthquake engineering and earthquake resistant technology. If
we acquire high quality vibration data, we can precisely estimate structural damage.
However, the high quality sensing is diﬃcult because of
the distribution manner of wireless sensor networks. In [3],we described overview of our earthquake monitoring. In thispaper, we focus on a synchronized sampling mechanism in
the earthquake monitoring: especially, its design and evaluation. Section 2 discusses what kind of factors cause sensing
error in wireless sensor networks, Section 3 propose a synchronized low-jitter sampling mechanism, which minimize
sensing error, and Section 4 evaluates the synchronized lowjitter sampling using a shaking table. Finally, Section 5
concludes this work.

2.

‡Kobori Research Complex

The University of TokyoERROR FACTORS ON WIRELESS SENSOR NETWORKS

Earthquake monitoring requires high accuracy sensing,
because inaccurate sensor data cannot be used for structural or earthquake analysis. In this section, we discuss whatkind of factors aﬀect sensing accuracy on wireless sensor networks.
There are three factors in sensing error in wireless sensor
networks: sampling error, sensor noise, and quantization error. Sampling error is caused by sampling jitter as shown in
Figure 1. Sampling jitters are caused by timing diﬀerences
between an ideal timing and an actual timing, and the sampling error depends on sampling jitter and frequency components of measured signals. The sensor noise is caused by

Gaussian white noise, which the sensor has. The quantization error is caused by the resolution of an AD converterand sensitivity of the sensor.
In these factors, the sampling error is the most important factor in wireless sensor networks. Ideal sampling forearthquake monitoring is as follows: all sensor nodes synchronously sample sensor data, and the sampling intervals
are constant at any time. The synchronous sampling is required to estimate correlation among the all sensor nodes.
If we acquire the correlation among nodes, we can estimate
story drift of the structure, and the story drift is a key indicator of structural safety. The constant intervals are required
to restore a precise original waveform.
The wireless sensor networks cannot achieve the ideal sampling because of two characteristics. First, sampling tasks
on sensor nodes are scheduled with software while existing
wired sensing is designed with hardware. The software basedtask-scheduling makes diﬃculty for time-sensitive applications. For example, in case of using TinyOS, a sampling task
is invoked after indeterministic delay. Therefore, TinyOS
cannot sample acceleration in the ideal timings [1].
Second, wireless sensor networks are a distributed system: each node has an individual clock, and the clocks haveinherent errors. The inherent errors make sampling jitter,and the jitter becomes larger when the measurement time
becomes longer because of the accumulation of sampling interval diﬀerence even if we use time-synchronization protocol
like FTSP [2]. On the other hand, since existing wired sensing systems are centralized systems, the wired sensing does
not have jitter on the sampling points.
There are some works, which focus on vibration monitoring with wireless sensor networks [1][4]. However, the works
did not consider sampling jitter. Although [1] achieved lowjitter sampling, the work lacks quantitative evaluation of thesampling error.

signalsampling
error

Ideal sample sampling jitter = task scheduling delay
actual sample+ time synchronization errort+ ∑ sampling interval difference

sampling jitter

Copyright is held by the author/owner(s).
SenSys’08, November 5–7, 2008, Raleigh, North Carolina, USA.
ACM 978-1-59593-990-6/08/11.

Figure 1: Sampling Error(n + 1) * sampling_interval

sink node

TX

skew * n * sampling_interval + offset: sampling: interval calculation
local_time(≡global_time)

skew * (n + 1) * sampling_interval + offset

3000

calculated sampling interval
sensor node

2000

1000
500

RX

4.

-2

-1

01
Jitter (us)234

Figure 3: Jitter Histogram

We designed and implemented a synchronized low-jitter
sampling mechanism based on the discussion in Section 2.
The mechanism allows each node to synchronously sample
sensor data among all nodes by adjusting every samplinginterval, and distributes the sampling jitter to each sample.
Brief overview of the synchronized low-jitter sampling mechanism is as follows. The sink node samples acceleration withconstant interval and broadcasts synchronization packets periodically. Each sensor node synchronizes itself to the sink
node, like FTSP, with MAC-Layer time-stamping and linear
regression. Sensor nodes calculate adequate sampling interval per sample based on synchronization information suchas skew and oﬀset as shown in Figure 2, and the calculated
interval is set to a CPU timer compare register.
In the synchronized low-jitter sampling mechanism, a sampling task is deterministically invoked. We implemented themechanism on a compact hard real-time operating system
for sensor nodes, called as PAVENET OS [3]. PAVENET OS
provides three task priorities: high-priority, low-priority, andbest-eﬀort. Higher priority tasks can preempt tasks whichhave lower priorities. In this implementation, high priority
tasks are a wireless communication task and a sampling task,
and the other tasks are low-priority or best-eﬀort tasks. Toeliminate the indeterministic delay by wireless communication, we developed a sampling synchronized MAC protocol:
the wireless media is turned on 1 ms after sampling and isturned oﬀ 1 ms before next sampling.

EVALUATIONFirst, we evaluated the synchronization accuracy using
a logic analyzer. The results shows that all nodes synchronously sample acceleration within 3.4 us maximum jitterand 0.7 us average jitter, as shown in Figure 3: single-hop,sync interval=20.48 s, and linear regression points = 4.

2.5
2
1.5
10-3

SYNCHRONIZED LOW-JITTERSAMPLING

3

0.50

Figure 2: Synchronized Low-Jitter Sampling

sensor noise
1.0 Hz3.0 Hz10.0 Hz20.0 Hz

3.5

1500

local_time

3.

4
node 2
node 3
node 4
node 5
node 62500

RMS Error (Gal)constant sampling interval

number of times

n * sampling_interval

0200

400 600
Jitter (us)

800

1000

Figure 5: Error Evaluation

Second, we evaluated the synchronized low-jitter samplingmechanism using a shaking table as shown in Figure 4. A
shaking table is an instrument for earthquake engineering,and can experimentally shake the table with an arbitrary
input signal. Because the shaking table is rigid body, thetable gives same acceleration to all nodes. We compared theoutput from nodes, and evaluated the measurement error.
Our sensor
node has an acceleration sensor, which has√
50 uG/ Hz noise level (1 G=981 Gal), Vdd/5G sensitivity, a 50 Hz cut-oﬀ low-pass-ﬁlter, and a 16-bit resolution
AD converter. Therefore, the sensor nodes have 0.347 Gal
RMS Gaussian error and 0.0763 Gal identically-distributederror. We used sine waves as input, their frequencies are
20 Hz, 10 Hz, 3 Hz, and 1 Hz, and their amplitudes areabout 60 Gal. The waves assume frequency characteristics
of earthquake waves. To evaluate sampling error by sampling jitter, we used 6 nodes, including a sink node, threejitter injected nodes (the delay is 1 ms, 300 us, or 100 us),and two jitter-less sensor node.
The result is shown in Figure 5. The 1 ms jitter injected node shows 3.59 Gal RMS error at the 20 Hz sinewave. It means that inaccurate time-synchronization ortask-scheduling delay aﬀects sensing quality. On the other
hand, jitter-less sensor nodes show 0.357 Gal RMS error atthe 20 Hz sin wave. This value means that the mechanism
reduces sampling error to 2.8% of entire error.

5. CONCLUSION
We have proposed a synchronized low-jitter sampling mechanism, and the mechanism reduces the sampling error to
2.8% of entire error. The wireless sensor networks with themechanism achieve almost the same accuracy as existing
wired sensing systems. Currently, we are studying a limitation of measurement accuracy and the relation betweenaccuracy and power consumption on the wireless sensor networks.

6. REFERENCES

Figure 4: Shaking Table Test

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