The Problem of Measurement Accuracy in Sensor Networks for IAQ

Monitoring

Andrzej Szczurek, Monika Maciejewska and Tomasz Pietrucha

Laboratory of Sensor Technique and Indoor Air Quality Studies, Wroclaw University of Technology,

Wybrzeze Wyspanskiego 27, 57-370, Wroclaw, Poland

Keywords:

Indoor Air Quality, Sensor, Monitoring, Accuracy, Network.

Abstract:

These days, the problem of indoor air quality (IAQ) attracts increasing attention. Presently, IAQ is usually

characterised on the basis of the following parameters: temperature, relative humidity and carbon dioxide

concentration. Because of spatial and temporal variation of these parameters multi-point monitoring systems

which operate continuously are preferred. The aim of this work was to show that accuracies of sensors being

elements of a network have serious implications for a continuous, ﬁxed-point monitoring of IAQ. The analysis

was based on four-point IAQ monitoring study performed in a lecture hall. With reference to the measurement

accuracy we computed how likely it was that sensors located in different points recorded the same value of

measured quantity and how frequently such situations occurred. It was found that: (1) number of sensors and

their displacement affect information provided by the measurement system; (2) these aspects should be con-

sidered individually for each parameter describing IAQ ; (3) the sensor device dedicated to each measurement

point should be considered individually. By considering these issues in the design process the cost of IAQ

monitoring network as well as information redundancy may be reduced.

1 INTRODUCTION

In last decades, indoor air quality (IAQ) has drawn

considerable attention in both the public and scien-

tiﬁc domains (Fanger, 2006). Due to rising energy

costs, buildings are willingly built or renovated to be

air tight. In this way, the air exchange between indoor

and outdoor environmentis seriously reduced. In con-

sequence, the unwanted heat loss is reduced. The neg-

ative effect of energy saving is the degradation of air

quality in such objects. Bad indoor air quality has a

signiﬁcant impact on human health, safety, produc-

tivity and comfort (Sundell, 2004; Sarbu and Pacu-

rar, 2015). This is especially important in developed

countries where people spend major fraction of their

time indoors.

Recently, much effort has gone into improving in-

door air quality (Persily, 2015). In order to perform

this task, there is required monitoring of IAQ which

provides valuable information to building managers,

policy makers, health professionals as well as scien-

tiﬁc researchers. In this work, we want to discuss

some aspects of the accuracy of measurement devices

which should be taken into account in the design of

IAQ monitoring system (Hughes and Hase, 2010).

Our attention was focused on the inﬂuence of sensor

accuracy on the location and the composition of sen-

sor unit.

Currently, indoor air quality is rarely monitored

(Kim et al., 2015; Varas-Muriel, 2014). Although

the need for IAQ monitoring is great, the availabil-

ity of cost-effective systems is low. Majority of

homes and commercial buildings built today are not

equipped with IAQ control systems. Regular indoor

air monitoring is typically limited to smoke and car-

bon monoxide (CO) detectors. Some advanced heat-

ing, ventilation and air conditioning (HVAC) systems

use carbon dioxide (CO

) sensors to control ventila-

tion (Hesaraki and Holmberg, 2015). HVAC engi-

neers have known for a long time that CO

measure-

ments coupled with temperature and humidity read-

ings give an indication of the effectiveness of the

HVAC system in the building.

Typical environmental analysis consists of tak-

ing single-point measurements of pollutant concen-

trations. This approach is controversial in the case

of IAQ investigation, because parameters describing

physical and chemical conditions inside building may

vary signiﬁcantly even within the same room. Espe-

cially, indoor pollutants distribution can be spatially

non-uniform. Therefore, IAQ monitoring requires a

Szczurek, A., Maciejewska, M. and Pietrucha, T.

The Problem of Measurement Accuracy in Sensor Networks for IAQ Monitoring.

DOI: 10.5220/0005645101490157

In Proceedings of the 5th International Confererence on Sensor Networks (SENSORNETS 2016), pages 149-157

ISBN: 978-989-758-169-4

149

cost-effective, widely-accessible, distributed, station-

ary sensor network, which is capable of measuring se-

lected IAQ parameters at various locations over time,

simultaneously (Liu and Zhai, 2009). Basically, this

system can be formed by a number of small measur-

ing units which are able to obtain information from

their surroundings by means of sensors and transmit

it towards a base station using wire or wireless com-

munications. The data from individual monitoring

locations are compiled and analyzed. The locations

chosen for sensor units depend on the purpose of the

monitoring. Using this system, a more detailed repre-

sentation of IAQ is gained.

Several factors like cost, power consumption,

space utilization or measurement characteristics de-

cide about the applicability of sensor networks (Shan

et al., 2012; Jelicic et al., 2013). The stationary mon-

itoring network has a serious limitation. It can deter-

mine IAQ only at points where sensors are installed.

Therefore the appropriate number of ﬁxed monitoring

points and reliable sensor localization is a key issue

in the design of this system. In practice, the applied

sensor units may be too expensive for large-scale and

ﬁne grained deployment (Kumar et al., 2011). Thus,

existing structures, or even new buildings, with sen-

sor networks is a costly process. The selection of the

number of sensors to install and the site for each sen-

sor is one of the most critical aspects to be considered

for the overall monitoring system effectiveness. Plac-

ing the sensor in the wrong location will defeat the

purpose for which it is intended.

IAQ relates to a number of environmental factors,

inside a building, which can impinge on the health,

comfort or work performance of the buildings occu-

pants. Therefore, real IAQ monitoring systems re-

quire multiple types of devices for continuous, in real-

time detection and measurements of temperature, hu-

midity, and numerous toxic (hazardous) gases. Cur-

rently, different models of sensors are available in the

market for monitoring these parameters. IAQ mon-

itoring involves the application of technologies that

can provide information at different level of reliabil-

ity (Peng et al., 2013). Achieving high-quality IAQ

evaluation with small number of inexpensive sensors

is challenging now.

The aim of this work is to show that the number

and location of sensors in IAQ monitoring networks

is strongly affected by the accuracies of these mea-

surement devices. Taking this into consideration may

allow to reduce data redundancy and build more cost

effective and power efﬁcient sensor systems. This is

especially important for wireless sensor networks. It

should be noted that professionalIAQ monitoring sys-

tems very often have to face constraints of high power

consumption and the excessive cost of applied sensor

units.

The rest of the paper is structured as follows. We

start from the Experimental part which presents the

scope of sensors-based IAQ measurement study. The

next section, Methods contains the description of an

approach proposed to study the performance of a sen-

sor set with reference to the accuracies of individual

sensors. With this approach we analyzed the out-

come of IAQ monitoring study. The obtained re-

sults are presented and commented in the Results and

discussion section. Individual subsections of it are

dedicated to temperature, relative humidity and CO

measurements. The generalization of our results is

proposed in Conclusions.

2 EXPERIMENTAL

Indoor air quality monitoirng study was carried out in

a university lecture hall, see Fig. 1. The room may be

considered as representative for this category of in-

door spaces. It has an amphitheatrical layout. Down

at the front, a narrow dais is the place for a lecturer.

The ﬁrst row of seats for students starts about some

distance from the dais and the last row touches the

back wall. Hall dimensions are given in Fig. 2. Desk-

tops and seats form a compact zone, which is a place

for 90 listeners (10 rows, 9 seats in each). Lecture

hall is ﬁtted with openable windows, which take up

one wall. The air is exchanged via natural ventilation.

Figure 1: Lecture hall in which there was conducted IAQ

monitoring study.

Measurement session took place in Spring 2015

(April, May, June) and it lasted 8 days. Experiments

were performed on Wednesdays in subsequent weeks.

We were interested in lecture time i.e. the period from

9:15 to 18:15. Experiments consisted in instrumental

SENSORNETS 2016 - 5th International Conference on Sensor Networks

150

Figure 2: Distributionof measurement points in lecture hall.

In each location there were monitored: temperature, relative

humidity and CO

concentration.

measurements of indoor air parameters and the obser-

vation of factors inﬂuencing IAQ.

The instrumental part of IAQ study involved the

monitoring of three basic indoor air parameters: tem-

perature, relative humidity and carbon dioxide con-

centration. For this purpose we used sensor devices

which measured these three parameters in parallel.

Most important technical speciﬁcations of sensors are

presented in Table 1. They represent a preset standard

offered in terms of measurement solutions for the rou-

tine determination of indoor air quality. Measurement

data was recorded with time resolution of 1 min.

Monitoring was performed at four places within

the room. The location of sensor devices is shown in

Fig. 2. One of them (no. 1) was placed by the lec-

turer’s seat. Three others were distributed along the

hall’s central axis in the third (no. 2), sixth (no. 3)

and ninth (no. 4) row of seats. Sensor devices were

assigned to measurement points. Upon selection of

sensors locations we took into account the amphithe-

atrical layout of the room and students distribution,

see Fig. 9. Measuring instruments were placed on

desktops i.e. in students’ breathing zone. Typically,

in the immediate vicinity of sensors the seats were

left free in order to eliminate the direct inﬂuence of

occupants on the readouts.

In addition to sensor measurements there were ob-

served factors which inﬂuence IAQ. We considered:

number of students present in the room, their spatial

distribution, degree of opening for each window, time

and duration of windows and door opening, blinds

use. The data was collected concerning temporal

variation of these factors. The obtained information

helped to interpret the results of analysis presented in

this work.

3 METHODS

Accuracy of measurement is deﬁned as the closeness

of agreement between a measured quantity value and

a true quantity value of a measurand (i.e. quantity

intended to be measured (JCGM, 200:2008). Accord-

ing to some references (JCGM, 200:2008), the con-

cept ”measurement accuracy” is not a quantity and it

is not given a numerical value. A measurement is said

to be more accurate when it offers a smaller measure-

ment error.

Nevertheless, producers of measurement devices

oftentimes provide the numerical information about

the accuracy of measurement. It is typically under-

stood as the maximum distance between the true and

measured value of a quantity.

We made several assumptions for the purpose of

our analysis.

1. The true value x

true

of the the measurand X be-

longs to the 2A-wide interval around the measured

value x

true

∈ hx

− A, x

+ Ai (1)

where A is the measurement accuracy.

2. Two values x

true1

and x

true2

may be considered

different if the interval which hosts the true value

true1

and the interval which hosts the true value

true2

do not have common part. Namely, when

− A

, x

+ A

i ∩ hx

− A

, x

+ A

i = ∅

(2)

where A

and A

are the accuracy of measurement

1 and measurement 2 of the same quantity X, re-

spectively.

3. Two values x

true1

and x

true2

may be considered

equal when the two intervals have common part

i.e.

− A

, x

+ A

i ∩ hx

− A

, x

+ A

i 6= ∅.

(3)

4. Using I

to denote the interval around the mea-

sured value which includes the true value

= hx

− A

, x

+ A

i (4)

one may formulate the generic versions of crite-

ria, given by eq. 2 and eq. 3, which are applicable

to i = 1...N measurement results. Namely, we as-

sume that N true values x

true1

, x

true2

, ...., x

trueN

are

different, if there is no common part shared by the

associated I

intervals

The Problem of Measurement Accuracy in Sensor Networks for IAQ Monitoring

151

Table 1: Measuring characteristics of sensors applied for indoor air monitoring.

Measured quantity Type of sensor Measuring range Accuracy Resolution

carbon dioxide concentration Non dispersive infrared (NDIR) 0 ... 500 ppm 50 ppm + 3 % m.v. 1 ppm

temperature Thermistor NTC 10 kΩ -20 ... 60

◦

C 0.2

◦

C or 0.15% m.v. 0.1

◦

relative humidity Capacitive sensor 5 ... 100% 2 % 0.1 %

∩ I

∩ ...I

= ∅. (5)

Otherwise, i.e. when N intervals have a part which

is common for all of them

∩ I

∩ ...I

6= ∅. (6)

then N true values x

true1

, x

true2

, ...., x

trueN

may be

recognized as equal.

5. The degree of overlap between intervals I

may be

represented by the width of the interval which is

their common part. We used it as the basis for

constructing index L

L =

∩ I

∩ ...I

|2A|

(7)

which is the ratio between the width of the ac-

tual common part of N intervals and the maximum

width of the common part, i.e. 2A. The index is

equal zero in case of lack of overlap. For a com-

plete overlap L equals one.

L may be interpreted as the likelihood that N mea-

surement results refer to the same real value of the

measured quantity. In other words, it is a likeli-

hood that a single point measurement would be

sufﬁcient to provide the true value of the mea-

surand in the space covered by N measurement

points.

We applied the above listed assumptions to analyse

the data collected during a four-point IAQ monitoring

study which was carried out in a lecture hall. We were

interested in the importance of the measurement accu-

racy in the design of sensor network for IAQ monitor-

ing .

Essentially, we compared measurements per-

formed by N = 4 sensors, each located in different

measurement point (see Fig. 2). The analysis was

performed for pairs of sensors (1-2, 1-3, 1-4, 2-3, 2-4,

3-4), for sensor triplets (1-2-3, 1-2-4, 2-3-4) and for

the quartet of sensors (1-2-3-4).

Temperature, relative humidity and carbon diox-

ide concentration were examined individually. The

measurement accuracies used in calculations were

following, T: 0.2

◦

C, RH: 2 % RH and CO

concen-

tration: 50 + 3% measured value, as given in Table

The analysis was performed in time steps. A sin-

gle time step was 1 minute long. Entire monitoring

period was divided into such intervals. For a single

time step it was determined: 1) whether individual

sensors recorded equal values of the measured param-

eter, 2) the L index.

Based on (1) we computed how frequently a par-

ticular set of sensors recorded the same value of the

measured quantity during one day. Following (2), all

nonzero L values were averaged within the period of

one day. The obtained index represented the average

degree of overlap between information provided by

different measurement points at times when the I in-

tervals overlap existed. The results obtained for indi-

vidual days were aggregated for the purpose of pre-

sentation.

Box and whiskers plot was applied in order to vi-

sualize the aggregate results. In the plot, a single box

refers to one set of data. The central mark in the box

is the median of the data set. The edges of the box are

the 25

and 75

percentiles. The whiskers extend to

the most extreme data points not considered outliers.

Outliers are plotted individually using crosses.

All necessary scripts were written in Matlab.

4 RESULTS AND DISCUSSION

4.1 Temperature

In Fig. 3 we show the results of temperature monitor-

ing in the lecture hall during an exemplary day. More

precisely, we plotted the limits of intervals I (see eq.

4) which are expected to host true values of temper-

ature in each measurement point on the subsequent

minutes of the monitoring period.

In Fig. 4 we present how frequently sensors lo-

cated in different measurement points recorded tem-

perature values which could be considered equal. In

Table 2 we show the L index which envisages the like-

lihood that one sensor would be sufﬁcient to provide

a true value of temperature, which is representative

for all places where the particular set of sensors was

distributed.

Based on our results, the existence of common

part between I intervals for temperature measured in

different locations within lecture hall was basically

limited to pairs of sensors. As shown in Fig. 4, an

overlap was most frequently observed for measure-

ment points 1-2 (70 % of measurement period), 1-3

SENSORNETS 2016 - 5th International Conference on Sensor Networks

152

0 1 2 3 4 5 6 7 8 9

Time [h]

I intervals for T [C]

measurement point 1

measurement point 2

measurement point 3

measurement point 4

Figure 3: I intervals around temperature values recorded by

four sensors located in different measurement points. Re-

sults come from an exemplary day of the monitoring study.

1−2−3−41−2−3 1−2−4 1−3−4 2−3−4 1−2 1−3 1−4 2−3 2−4 3−4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Set of measurement points

Frequency of occurence

Figure 4: How frequently sensors located in different mea-

surement points recorded temperature values which could

be considered equal.

(55 %) and 3-4 (45 %). When the overlap existed, I

intervals shared about 40 % of their width (see Table

2). Interestingly, we noticed an overlap in pairs 1-2

and 1-3 rather than in pairs 1-2 and 2-3, as would be

suggested by the vertical as well as horizontal proxim-

ity of sensors (see Fig. 2). This fact may be explained

by the exceptional character of location 1. Based on

Fig. 3, if one arranged measurement points according

to the increasing temperature, the sequence would be

as follows: point 2, 1, 3 and 4. Except for point 1, the

positions of others in the row could be easily associ-

ated with the height of measuring instrument above

the lowest ﬂoor level. The obtained temperature data

reﬂected the existence of thermal stratiﬁcation in the

room. Sensor 1 recorded greater values than sensor

2 and smaller than sensor 3 because of an additional

heat source at the lecturer’s seat - a computer system

for overhead projection.

Table 2: L index for temperature. Average for the cases

when there existed a common part of I intervals.

Set of measuring points I intervals overlap [%]

1-2-3-4 0.00 ± 0.00

1-2-3 3.26 ± 5.65

1-2-4 5.36 ± 5.26

1-3-4 20.05 ± 10.68

2-3-4 6.56 ± 7.07

1-2 40.15 ± 5.38

1-3 47.35 ± 6.08

1-4 34.86 ± 6.61

2-3 18.14 ± 12.90

2-4 17.21 ± 14.13

3-4 39.99 ± 10.92

In case of three-sensors combinations the same

value of temperature was recorded less frequently

than 10 % of the overall monitoring period (see Fig.

4). The identity of records form four measurement

points was extremely rare, less than 1 % of time.

Our analysis showed that, in an amphitheatrical

lecture hall, spatial temperature variation was de-

tectable at a horizontal distance of less than two me-

ters based on measurements performed with the ac-

curacy of 0.2

◦

C. Therefore, it is reasonable to ap-

ply multi-point temperature monitoring indoors using

standard instruments. Data redundancy is relatively

low. Of course, the ultimate distribution of measure-

ment points would be mainly driven by the goal of

maintaining human comfort in an occupied zone.

Temperature sensors are small, cheap and battery

powered. Their calibration is rarely required. Sen-

sor devices may operate unattended for weeks, even

months. Therefore, numerous measurement points

are affordable in case of temperature monitoring in-

doors. It is important. As we have shown, in view

of the available measurement accuracy, there may be

required temperature sensors displacement on quite a

dense grid in order to properly characterize indoor air.

4.2 Relative Humidity

In Fig. 5 we show the results of relative humidity

monitoring in the lecture hall during an exemplary

day. More precisely, we plotted the limits of intervals

I (see eq. 4) which are expected to host true values

of RH on the subsequent minutes of the monitoring

period in different measurement points.

In Fig. 6 we show how frequently RH sensors

placed in different measurement points recorded val-

ues which could be considered equal. In Table 3 we

present the likelihood that one sensor would be suf-

ﬁcient to provide a true value of humidity, which is

representative for all places where particular sensors

were located.

The Problem of Measurement Accuracy in Sensor Networks for IAQ Monitoring

153

0 1 2 3 4 5 6 7 8 9

Time [h]

I intervals for RH [%]

measurement point 1

measurement point 2

measurement point 3

measurement point 4

Figure 5: I intervals around RH values recorded by four

sensors located in different measurement points. Results

come from an exemplary day of the monitoring study.

1−2−3−41−2−3 1−2−4 1−3−4 2−3−4 1−2 1−3 1−4 2−3 2−4 3−4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Set of measurement points

Frequency of occurence

Figure 6: How frequently sensors located in different mea-

surement points recorded relative humidity values which

could be considered equal.

Table 3: L index for relative humidity. Average for the cases

when there existed a common part of I intervals.

Set of measuring points I intervals overlap [%]

1-2-3-4 46.99 ± 7.44

1-2-3 50.27 ± 7.99

1-2-4 49.55 ± 7.87

1-3-4 63.07 ± 8.84

2-3-4 56.48 ± 6.29

1-2 65.34 ± 10.67

1-3 66.51 ± 10.61

1-4 64.92 ± 10.15

2-3 66.50 ± 6.12

2-4 63.35 ± 6.94

3-4 79.42 ± 2.52

From our results, the overlap between I intervals

for RH recorded in different measurement points was

very frequent. As shown in Fig. 6, this relation was

observed between 80 to 100 % of the overall monitor-

ing period. Computations revealed that the frequency

of overlap was nearly the same in groups of two, three

as well as all four measurement points. In view of the

available RH measurement accuracy, nearly all time

of the monitoring study air humidity was the same in

each measurement point. Just on one day we observed

smaller overlap between RH in measurement point 1

and the remaining locations. Although such occur-

rences shall not to be ignored, this situation could be

considered as episodic.

Based on Fig. 5 also the degree of overlap be-

tween I intervals around RH values recorded simul-

taneously in different measurement points was very

high. From the numbers given in Table 3 we see that

in case of entire sensor network, common part of I in-

tervals was as big as 47 %. Of course, for individual

pairs of sensors the degree of overlap was still higher

and equal 60 to 80 %.

Very high frequency and degree of overlap for I

intervals in case of RH pointed at the considerable re-

dundancy while using multi-point layout for collect-

ing data about air humidity. Instruments which mea-

sure RH with the accuracy of 2 % were not able to

detect spatial variation of this parameter in the lecture

hall. Actually, when accepting this level of accuracy,

the original multi-point monitoring network could be

reduced down to one measurement point. In case of

taking care for the lecturers comfort individually, two

RH sensors would be needed, one located in point 1

and the other located at point 2, 3 or 4.

Similar as in case of temperature measuring units,

humidity sensors are small, cheap and durable. From

the point of view of cost, installation and maintenance

multi-point RH monitoring networks indoors are af-

fordable. However, our ﬁndings indicate that they

may not be needed. Their creation shall be carefully

thought over, with reference to the accuracy of the

available measuring devices.

4.3 CO

Concentration

In Fig. 7 we show the results of CO

concentration

monitoring in the lecture hall during an exemplary

day. More precisely, we plotted the limits of I inter-

vals (see eq. 4) which are expected to host true values

of CO

concentration in each measurement point on

the subsequent minutes of the monitoring period.

In Fig. 8 we show how frequently carbon diox-

ide sensors located along the main axis in the lecture

hall and at the lecturers seat rerecorded concentra-

tions which could be considered equal. In Table 4 we

present the probability that one sensor would be suf-

ﬁcient to provide a true value of CO

concentration,

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154

0 1 2 3 4 5 6 7 8 9

500

1000

1500

2000

2500

Time [h]

I intervals for CO

concentration [ppm]

measurement point 1

measurement point 2

measurement point 3

measurement point 4

Figure 7: I intervals around CO

concentration values

recorded by four sensors located in different measurement

points. Results come from an exemplary day of the moni-

toring study.

1−2−3−41−2−3 1−2−4 1−3−4 2−3−4 1−2 1−3 1−4 2−3 2−4 3−4

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Set of measurement points

Frequency of occurence

Figure 8: How frequently sensors located in different mea-

surement points recorded CO

concentration values which

could be considered equal.

which is representative for all places where particular

sensors were distributed.

The temporal variation of CO

concentration

shown in Fig. 6 well reﬂects room occupancy.

Namely, sudden increase of concentration is always

associated with the beginningof the lecture, when stu-

dents enter the room. Sudden decrease appears when

students leave the hall for the break.

Based on Fig. 8, most rarely the same CO

con-

centrations were recorded in measurement points 1

and 2. Such situations occurred on average during

40 % of the measurement period. For other pairs of

sensors the percentage was much higher from 70 %

up to 90 % . The distance between results obtained

in point 1 and 2 loaded on the overlap within groups

of three or four sensor which included 1-2 pair. In

such sensor sets the overlap was infrequent, namely

10 to 20 % of the monitoring period. In case the set

1 2 3 4 5 6 7 8 9 10

seat in the row

row

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Figure 9: Frequency of student seats occupation. Darkest

blue squares indicate location of sensors.

of sensors did not include 1-2 pair the identity of the

recorded values was observed during 60 to 70 % of

the measurement time. As shown in Table 4, the size

of common part of I intervals behaved in the same

way as the frequency of overlap occurrence. Namely,

the expected regularity was biased due to the dissimi-

larity of data collected in points 1 and 2. For a particu-

lar combination of measurement points the of L index

for CO

was higher than for temperature and lower as

compared with RH.

In Fig. 7 we see that CO

concentrations recorded

in point 2 were high, compared with other locations.

This regularity was observed during entire IAQ mon-

itoring study. High concentrations of CO

in point 2

could be attributed to the accumulation of this species

in low-lying parts of the room. The molecular mass of

this substance is larger compared with air. As shown

in Fig. 9 Students, who are the major source of CO

in this room rarely took seats in the ﬁrst rows, i.e. in

the vicinity of sensor 2. As a rule, last rows were the

Table 4: L index for CO

concentration. Average for the

cases when there existed a common part of I intervals.

Set of measuring points Common part [%]

1-2-3-4 18.29 ± 11.45

1-2-3 25.13 ± 12.40

1-2-4 18.25 ± 10.36

1-3-4 44.74 ± 8.46

2-3-4 33.48 ± 7.13

1-2 27.75 ± 13.97

1-3 53.61 ± 8.82

1-4 52.75 ± 9.21

2-3 49.41 ± 11.43

2-4 42.88 ± 11.73

3-4 62.77 ± 7.60

The Problem of Measurement Accuracy in Sensor Networks for IAQ Monitoring

155

heavily occupied part of the room. For this reason

concentrations recorded in points 3 and 4 were most

frequently high and could be considered identical (90

%, see Fig. 8). Interestingly, results of lecturers seat

monitoring (point 1) frequently overlapped with the

concentrations up in the audience (80 % of time, see

Fig. 8).

From the above presented analysis we see that the

distinctive, important locations for CO

monitoring

were associated with low-lying parts of the room and

heavily occupied sections. There, the species should

be controlled. In case of the examined lecture hall, the

sufﬁcient information about CO

concentration could

be acquired using two measurement points namely,

point 2 (low-lying part of the room) and point 4 or 3

(heavily occupied zone). Setting more points resulted

in redundant information if measurements were per-

formed with the accuracy of 50 ppm + 3 % m.v.

sensors are several times more expensive

compared with temperature and RH sensors. They

require relatively frequent calibration and consume

much more energy. If measurements session lasts

longer than several days CO

measurement devices

shall be connected to power supply in order to assure

the continuity of readouts. These constraints have to

be taken under consideration while setting CO

moni-

toring network. However, based on our analysis small

number of CO

measurement points may not impair

the quality of information about this species. Con-

trarily, in view of the offered measurement accuracies

such sensor nets may be recommended.

5 CONCLUSIONS

Temporal and spatial variability of IAQ causes that it

should be determined by multi-point sensor networks

which operate continuously.

Many factors affect the quality of information

which is acquired in this way. These are, for exam-

ple the number of sensors, their localization and the

characteristics of measurement devices. In practical

applications, the optimization of these factors is very

important.

In our opinion it is necessary that the accuracy of

measurement devices is taken under consideration in

the selection of the number and distribution of sen-

sors.

This parameter may be different in various com-

mercially offered temperature, RH and CO

sensors.

In this work, we have shown that also the relation of

accuracy to spatial and temporal variation may be dif-

ferent among quantities measured in indoor air. For

this reason, the sensor net should be designed indi-

vidually for each parameter.

Based on our study, the measurement accuracy al-

lows to apply small number of sensors in RH and CO

measurements, while in case of temperature, their

number should be grater. However, in our opinion

the determination of the number of sensors and their

distribution shall be based on the screening study and

the analysis, which needs to be performed individu-

ally for a particular object of interest. This opinion

ﬁnds the justiﬁcation in a strong inﬂuence of HVAC

system, occupancy and building characteristics on the

parameters describing IAQ.

ACKNOWLEDGEMENTS

This work was ﬁnancially supported by the National

Science Center, Poland, under the contract number

DEC-2012/07/B/ST8/03031.

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