Predictive Data Reduction in Wireless Sensor Networks using Selective

Filtering

David James McCorrie

, Elena Gaura

, Keith Burnham

and Nigel Poole

Cogent Computing ARC, Coventry University, Priory Street, Coventry CV1 5FB, U.K.

Control Theory and Applications Centre, Coventry University, Priory Street, Coventry CV1 5FB, U.K.

Keywords:

Signal Reconstruction, Optimization Problems in Signal Processing, Change Detection Problems Instrumen-

tation Networks and Software.

Abstract:

In a wireless sensor network, transmissions consume a large portion of a node’s energy budget. Data reduction

is generally acknowledged as an effective means to reduce the number of network transmissions, thereby

increasing the overall network lifetime. This paper builds on the Spanish Inquisition Protocol, to further reduce

transmissions in a single-hop wireless sensor system aimed at a gas turbine engine exhaust gas temperature

(EGT) monitoring application. A new method for selective ﬁltering of sensed data based on state identiﬁcation

has been devised for accurate state predictions. Low transmission rates are achieved even when signiﬁcant

temperature step changes occur. A simulator was implemented to generate ﬂight temperature proﬁles similar to

those encountered in real-life, which enabled tuning and evaluation of the algorithm. The results, summarized

over 280 simulated ﬂights of variable duration (from approximately 58 minutes to 14 hours) show an average

reduction in the number of transmissions by 95%, 99.8% and 91% in the take-off, cruise and landing phases

respectively, compared to transmissions encountered by a sense-and-send system sampling at the same rate.

The algorithm generates an average error of 0.11 ± 0.04 °C over a 927 °C range.

1 INTRODUCTION

Research into the use of wireless instrumentation

in the aerospace industry is growing, both within

academia and industry. In the UK alone several large

projects are currently reporting positively on wireless

sensor network (WSN) based developments for this

sector (Mitchell et al., 2011), (Pinto et al., 2010).

Generically, wireless measurement is considered

an attractive option particularly for aircraft engines.

It could reduce the complexity, weight, and cost of

engine monitoring as well as provide increased sen-

sor density, higher data rates, and enhanced sen-

sor deployment ﬂexibility (Yedavalli and Belapurkar,

2011).

Whilst in-ﬂight engine monitoring and control

based on WSN is a long term aim, in the short-to-

medium term, the use of wireless instrumentation is

envisaged mainly for engine design and test environ-

ments.

The research the authors have engaged with is to-

wards in-ﬂight engine monitoring. It aims to create

Highly Efﬁcient Autonomous Thermocouple (HEAT)

system prototypes for EGT monitoring. A primary

goal is to deliver robust, long lived wireless thermo-

couple systems which sample at rates of over 1 Hz.

The drive towards long lived, low power wireless sys-

tems is essential to the domain, given that nodes need

to run until the next service of the engine to avoid dis-

ruption of operation and need to be powered by exist-

ing energy harvesting technology (Adnan and Harb,

2011).

The HEAT hardware system developed, is com-

posed of multiple battery powered thermocouple sen-

sor nodes, located around the circumference of an en-

gine casing. Each sensor node can sample at 1 Hz

- 5 Hz from a Type-K thermocouple and reports the

EGT data back to the sink node. Nodes use the

CC2530 low powered ZigBee radio from Texas In-

struments (TI) and a MAX6675 cold junction com-

pensation chip. The nodes support Low Power Sleep.

When a HEAT sensor node’s power consumption

was analysed, it was found that a transmission of a

single sample accounts for 75 % of the total consump-

tion, whilst sleeping and sensing account for 24 % and

1 % of the consumption, respectively. Reducing the

number of transmissions will thus provide consider-

able savings in power consumption and increase the

165

James McCorrie D., Gaura E., Burnham K. and Poole N..

Predictive Data Reduction in Wireless Sensor Networks using Selective Filtering.

DOI: 10.5220/0004010601650170

In Proceedings of the 9th International Conference on Informatics in Control, Automation and Robotics (ICINCO-2012), pages 165-170

ISBN: 978-989-8565-21-1

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

network lifetime.

The contribution brought by this paper consists

on a method for drastically reducing transmissions

in EGT wireless sensing systems. The work builds

on previous research in the area of dual predic-

tion schemes (DPSs) for wireless sensor systems.

In particular, we propose an application bespoke

state prediction and selective ﬁltering method to be

used in conjunction with Spanish Inquisition Proto-

col (SIP) (Goldsmith and Brusey, 2010) (described

in Section 4). The proposed method is generically

suitable for applications where the data stream ex-

hibits a combination of steady states and signiﬁcant

step changes. The performance of the algorithm de-

veloped is tightly correlated with the absolute values

of the sensor readings. Thus in order to tune and eval-

uate its performance an EGT simulator was also de-

veloped.

The remainder of this paper is structured as fol-

lows, Section 2 brieﬂy describes related work in the

area of data reduction for wireless sensor systems.

Section 3 describes the simulator used to evaluate the

proposed algorithm. Section 4 considers the SIP as

the fundamental the data reduction method within the

HEAT system. Section 5 presents the selective ﬁlter-

ing (SF) algorithm integrated with SIP. Results are

given in Section 6, and concluding remarks are pre-

sented in Section 7.

2 RELATED WORK

Whist it is important to reduce the number of trans-

missions in a wireless networked system, it is equally

important to accurately capture the phenomena being

monitored. For the application at hand, transmissions

reductions through sampling rate reduction can not be

considered; HEAT nodes need to ensure sampling at

least at 1 Hz. Data compression and reduction is how-

ever an alternative approach to long lived networks

with speciﬁed requirements for data quality.

Many methods for data compression and reduc-

tion within a WSN have been proposed. Dictionary

based compression algorithms such as Lossless En-

tropy Compression (LEC) (Marcelloni and Vecchio,

2009) provide a byte reduction on a per packet ba-

sis, although would require sensor readings to be

buffered in order to reduce the number of transmis-

sions. Schoellhammer et al. (2004) model the sensor

readings using a liner model, by buffering the read-

ings until the residual error of a liner ﬁt exceeds a

predeﬁned error threshold. Due to the real-time re-

quirement of the HEAT system, buffering approaches

are not applicable.

The class of DPS algorithms solve the problem of

having to buffer sensor readings. By using a model on

the sensor node and the sink node, new readings can

be predicted without having to transmit further data.

When the error between the models exceeds a toler-

able threshold, new model parameters are transmit-

ted. Large reductions in the the numbers of transmis-

sions can be accomplished using the DPS approach,

while keeping a real-time knowledge of the system’s

state (Anastasi et al., 2009). A variety of implemen-

tations exist for the approach described above. Jain et

al. (2004) use a Dual Kalman Filter (DKF) as the sys-

tem model. Santini and Römer (2006) use an Least

Mean-Square (LMS) ﬁlter. Le Borgne et al. (2007)

present a general method for adaptively selecting the

model using a statistical procedure termed racing.

Such a method allows the most optimal model, from

a discrete set of models stored on the sensor node, to

be learned over time. Although considerable trans-

mission reductions are reported for a variety of case

studies, none of these approaches respond well to step

changes in the sensed data. (A summary of the re-

sults of these algorithms can be found in (Goldsmith

and Brusey, 2010) and (Borgne et al., 2007).) These

works have, however inspired the authors here to-

wards the reported developments.

3 EGT SIMULATOR

The EGT simulator attempts to produce ﬂight like

data to be used in evaluating the proposed algorithms.

The simulator consists of an EGT phenomena model

(PM) and a thermocouple sensor model (SM), as

shown in Figure 1. For the purpose of this paper, the

output from the PM, denoted s, is considered as the

actual EGT. The output from the SM is taken to be

the thermocouple readings, denoted y.

Flight proﬁles are split into three main phases:

take-off, cruise and landing. At the start of the ﬂight

the EGT is relatively low. During take-off, the EGT

rises sharply to around 1000 °C where it remains

fairly constant for the majority of the ﬂight. During

the ﬁnal landing phase, the EGT decreases to ambi-

ent temperature, with some oscillation to replicate ob-

served practice.

For the purpose of this simulation study, the take-

off and landing sequences, shown in Table 1, are con-

sidered to be consistent. The cruise sections of the

ﬂight are of variable duration, denoted d, which is

regarded as an input to the simulator. To simulate

a typical cruise phase of the ﬂight, a nominal EGT

of 830 °C is chosen. Furthermore, to accommodate

different altitudes and weather conditions, a uniform

ICINCO2012-9thInternationalConferenceonInformaticsinControl,AutomationandRobotics

166

Noise

Flight

profile

Smooth

profile

EGT model

Sensor

model

(actual

EGT)

(sensor

reading)

(cruise

duration)

Take-off

& landing

sequences

smoothing

factor

Noise

Std Dev.

Simulator

Figure 1: Simulator architecture.

distribution of the EGT about the nominal value of

±30 °C is randomly selected at the start of each cruise

section. Over the course of the cruise phase, there are

additional adjustments to the ﬂight pattern, at random

times during every 90-120 minutes, i.e. in a uniformly

distributed random manner. These transients consist

of an increase in EGT by 25 °C for 60 seconds, fol-

lowed by a further cruise section and the cycle repeats

until the cruise phase is complete.

Table 1: Flight sequence for take-off and landing phases,

which is identical in every simulated ﬂight.

Phase EGT (

◦

C) Duration (s)

Start engine 400 40

410 5

390 30

Taxi 430 220

Manoeuvring 390 5

394 7

390 4

394 8

395 3

Take off 950 10

900 430

920 10

Pull back 880 224

High idle 500 200

Landing 463 10

442 5

459 7

441 4

465 8

Reverse thrust 750 15

Engine off 20 30

Once the ﬂight proﬁle sequences have been gener-

ated, smoothing is applied to provide a gradual transi-

tion between ﬂight phases. This facilitates a realistic

EGT of the engine between the various steady state

sections. To realise this, an exponentially weighted

moving average (EWMA) ﬁlter with a value for the

smoothing factor, denoted α, of 0.4 is applied to the

generated time series.

It is believed that the standard deviation of the

sensor noise at cruise temperature is around 0.52 °C.

Consequently, Gaussian noise, with a standard devia-

tion of 0.52 °C, is added to the PM output to simulate

realistic thermocouple data.

4 SPANISH INQUISITION

PROTOCOL

The Spanish Inquisition Protocol is a generic data

reduction algorithm developed by Goldsmith and

Brusey, designed to reduce the number of transmis-

sions in a WSN (Goldsmith and Brusey, 2010). The

underlying principle is that transmissions should only

be made when sensor readings are not as expected, i.e.

when some pre-deﬁned change threshold is violated.

By using a model of the system, sensor readings can

be reconstructed at the sink node within a deﬁned er-

ror tolerance.

A model state vector, denoted X

, is calculated on

the sensor node and shared with the sink. This state

vector is used on both the sensor and sink to predict

the future system state at every time step. As the

predicted state, denoted X

, diverges from the actual

measured state, so the reconstruction error, deﬁned as

ε =

− X

, increases. When this error exceeds a de-

ﬁned threshold, a new model state vector is calculated

and shared.

The data reduction that SIP provides is dependent

on model quality and the calculation of the predicted

state, denoted X

. In this paper a piecewise linear

model is used, as demonstrated in (Goldsmith and

Brusey, 2010). The model state vector is deﬁned as

= (x

, ∆x

)

. Where x

denotes the predicted value

and ∆x

denotes the predicted rate of change. It is

then possible to predict future states between trans-

missions using,



1 t

0 1



5 SIGNAL ESTIMATION USING

SELECTIVE FILTERING

The more accurate the predicted rate of change, the

longer the state prediction will remain within the al-

lowable error range. Hence, the more predictable the

signal the greater the reduction in transmissions. It is

important, therefore, to remove as much noise from

the signal as possible, which is done by ﬁltering the

data. Selective ﬁltering (SF) is a rule based method

of selecting between multiple ﬁlters in real time, each

optimised for a different part of the signal. The sig-

nal is modelled as a sequence of states and transitions.

PredictiveDataReductioninWirelessSensorNetworksusingSelectiveFiltering

167

State transitions are identiﬁed by a predeﬁned set of

rules speciﬁc to the application. Each state has it’s

own ﬁlter, selected from a bank of ﬁlters and it’s own

predictor, which estimates the rate of change. Two

states are deﬁned in this application, steady and vari-

able, shown in Figure 2.

Variable

State

Steady State

Figure 2: System states within the ﬂight proﬁle.

Steady states are deﬁned as periods of low rate of

change, and is the initial system state. The expected

value x

is found by ﬁltering the sensor sample x

with

an EWMA ﬁlter having a value for α of 0.01. A tran-

sition to variable state is identiﬁed when a sample x

deviates from x

by more than a speciﬁed threshold,

denoted b, so that

− x

> b. For this application,

an appropriate value for the breakout threshold b was

found to be 1.6 °C.

A variable state is deﬁned as a period of large

rates of change in the data. During this state a sec-

ond ﬁlter can be used, or as in this application, sam-

ples can remain unﬁltered. A moving window, de-

noted W , of the n most recent samples is stored. A

steady state resumes when the range of values in the

window is less than a given threshold, r, such that

max(W ) − min(W ) < r.

Accurate state prediction depends on the calcula-

tion of the two components in the SIP model state

vector. These are: the predicted value, denoted x

and

and the predicted rate of change, denoted ∆x

. The SF

method improves the quality of the prediction by re-

ducing signal noise. When the rate of change is accu-

rate, the model will take longer to diverge from the ac-

tual sensor readings, resulting in fewer transmissions.

In the steady state ∆x

is calculated using the origi-

nal SIP method, ∆x

−x

, where l is the time of last

transmission. Recognising that the rates of change in

the variable and steady states are different, improve-

ment can be gained by reinitialising the model state

vector on each state change. In a steady state, there

is, by deﬁnition, little change in the temperature. The

best estimate of the initial ∆x

was found to be 0.

When transitioning from a steady to a variable

state, the initial expected value is deﬁned as x

= x

where c denotes the time of the transition to the vari-

able state. Here the expected rate of change is cal-

culated as ∆

−x

c−l

, where x

is the last expected

value from the preceding steady state at time l, and x

is the sensor reading at the time of transitioning. Sub-

sequent predicted values are calculated as x

= x

and

rate of change using ∆x

= x

− x

t−1

Moreover, since a variable state starts with a large

change in temperature followed by a convergence to

a steady state, it can be assumed that there is a high

probability that future ∆x

will be smaller than ∆x

t−1

Based on this assumption, it is postulated that ∆x

can

be better predicted with ∆x

= γ(x

− x

t−1

) where γ ∈

(0, 1]. Where γ determines the strength of the bias of

the rate of change towards a steady state. Figure 3

shows the reconstructed signal before and after bias.

320 330 340 350 360

Tim e (Min)

890

900

910

920

930

940

Tem perat ure (

noise

filtered

signal

reconstruct ed

transm ission

(a) Before bias

320 325 330 335 34 0 345 350 355

Tim e (Min)

880

890

900

910

920

930

940

950

Tem perat ure (

noise

filt ered

signal

reconstruct ed

transm ission

(b) After bias

Figure 3: More accurate rate of change estimation after bias.

6 RESULTS

Using the simulator described in Section 3, a compari-

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168

son is made between the performances of the Kalman

ﬁlter, the EWMA ﬁlter, and the SF, as described in

Section 5, when applied to the simulated EGT data.

In particular it is of interest to assess their ability to

ﬁlter the data, as well as the impact on the number of

transmissions when they are used in conjunction with

SIP.

SF reduces the noise while preserving the under-

lying signal, as observed by the reduction of the root

mean squared error (RMSE) between the ﬁltered and

noise free signal, while the EWMA and Kalman ﬁlters

increased the RMSE. In order to provide a fair com-

parison in further tests, the EWMA and Kalman ﬁlters

were tuned to have a maximum error from the original

signal of 3.5 °C. The simulations were run again with

the new ﬁlter parameters and resulting RMSE can be

seen in Figure 4.

Figure 4: RMSE error between the original signal and the

ﬁltered value.

Using an error threshold of 0.5 °C, 280 random

ﬂights are generated with cruise lengths from 0 to 13

hours at 3 minute intervals, Table 2 shows the percent-

age number of samples that would need to be trans-

mitted per hour for each algorithm. Figure 5 shows

the expected battery lifetime of a node running each

algorithm. It is important to note that the expected

lifetime shown is the relative effective lifetime. As-

suming the aircraft continuously serviced ﬂights of

the given duration, d. Each ﬂight immediately fol-

lowing the previous ﬂight, this would be the expected

number of days the HEAT node is expected to last.

Table 2: Mean percentage of samples transmitted per hour

in each phase of a ﬂight.

Samples transmitted per Hour (%)

Filter Take off Cruise Landing

EWMA 7.9 0.4 16.4

Kalman 7.5 0.3 14.8

SF 4.8 0.2 9.5

Figure 5: Comparing expected battery lifetime when using

different data reduction methods.

7 CONCLUSIONS

It has been found that the new SF algorithm, compris-

ing of SIP and SF, when applied to the HEAT sys-

tem has considerably increased the battery lifetime of

a sensor node. The new SF approach has be found

to reduce the sensor error, as well as the overall sys-

tem error. It is expected that the method could be

adapted to other steady state systems with signiﬁcant

step changes in signal values.

There are some assumptions that have been made

during the evaluation of this work, which may affect

performance in a real deployment. The simulation as-

sumes normally distributed noise, with a ﬁxed stan-

dard deviation. However, in reality this may not be

the case, and the system parameters may change over

time. If this is the case then the breakout threshold

values would need to be adjusted accordingly. Such

an observation would lead naturally to an adaptive

breakout threshold in response to the varying param-

eters.

A further assumption is that the temperature in the

steady state is constant, i.e. no variation; in reality one

could expect there would be some variation.

The encouraging results presented in this paper

provide an opportunity for further exploration. For

example, the algorithm presented uses a linear model

and it is considered that further reductions in trans-

mission is possible if a non-linear model were to be

used.

ACKNOWLEDGEMENTS

The Authors acknowledge the support of Meggitt

(UK) Limited, Basingstoke, UK; TRW Conekt, Soli-

hull, UK and EPSRC.

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169

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