Automated System for Data Acquisition and [602619]

Automated System for Data Acquisition and
Monitoring
Sorin Borza1,* and Iunia Cristina Borza2
1University Lucian Blaga of Sibiu, Romania
2Microsoft Iberica, Paseo del Club Deportivo, 1Centro Empresarial La Finca – Edificio 1
28223 Pozuelo de Alarcón (Madrid), Spain
Abstract. In this paper, an automatic system of taking over the data of the
environment and their automatic transformation in GIS Data, for some
intelligent map’s generation , is presented. Also, the system allows, by using
the multi -criteria method AHP, an objective analysis of the factors that will
be taken into account during the system. These factors will be taken over
automatically by using the LabVIEW Software and with t he help of the
acquisition board that will be connected to the computer. By using the same
LabVIEW software, the factors will be memorised in an ACESS type
database. The database will be connected to the Geomedia Professional
Software, with the help of whi ch an intelligent map will be generated
automatically. The system allows, not only the automatic collection of data,
but also their memorization and the generation of GIS elements and an
objective analysis of the collecting points of data could provide a c oncrete
answer regarding the most and least polluted points. As a novelty element,
the paper allows the analysis of the polluted factors using the multi -criteria
method s in such automatic taking -over storage and data generation .
1 Introduction
The data will be taken over, with the help of the sensors or with the help of the specific
apparatus which will be connected to the computer. In the case in which the data will be taken
over by the help of the sensors, the IEPE (Integrated Electronic Piezoelectric) sensors were
used, the acquisition board NI 9234, NI Mseries. In the case in which the taken over data by
the specialized apparatus, the Drager Pac III apparatus has been used for the measurement of
the monoxide carbon and the sound level meter EXTECH 407 780 for the sound
measurement. The Drager Pac III apparatus is equipped with a sensor for the concentration
measurement of the monoxide carbon. The values showed on the display apparatus are
represented in parts on a million (ppm), these values must be mul tiplied with 1,16, and the
resulted value represented in mg/m3. The virtual apparatus will be made using the compute,
by the help of the LabVIEW software. The multi -criteria analysis will be realized by a virtual
apparatus. Another part (branch) of the sys tem will allow the data memorization in the
ACCESS database for their following use in the class generation process (features classes)
used for the elaboration of the intelligent maps . The automatic taking over and the analysis

* Corresponding author: [anonimizat]

of the data using the AHP me thod (Analytic Hierarchy Process) and of generating the
element’s classes necessary to elaborate the intelligent maps consists of hardware
components: sensors of the acquisition board, apparatus used for measuring the polluting
factors and also, of softwar e elements, like: LabVIEW, Geomedia Professional, Access
Database , presented in figure1.

Fig. 1 The system for Aquisition and Monitoring Data
The MCDM methods are frequently used to solve real -world problems with multiple
attributes. Recently, various methods have been developed to support decision making in
various fields. Multi -criteria decision making (MCDM) is a well -known branch that deals
with problems making decisions based on a number of criteria.

2 The System Programming in LABVIEW
In object oriented programming with LabVIEW [5], a class consists merely of a user defined
data type together with methods that can be applied to values of that data type. Once could
say that object oriented programming in Labview allows the developer to c reate object
oriented wires.
Object orientation in Labview means the following.
1. Simple Inheritance. Neither multiple inheritance nor interfaces as in Java.
2. Strict encapsulation. Data of a class are always private. Public or protected data do
not exist.
As in other object oriented languages, a derived class may overload an abstract method
of its base class. However, the override method must have exactly the same input and output
parameters as the respective method of the parent class.
In object oriented p rogramming for Labview [6] are three fundamental consequences.
1. Objects contain only data and no active code. Agents do not exist.
2. Labview does not have classical variables. For the same reason, in Labview has no
equivalence to the concept of a constructor and a destructor . There are neither
constructors nor destructors.
3. Objects can only be accessed "by value" and never "by reference".
The fundamental differences between object programming in Labview and
conventional object oriented languages prevent a straightforward implementation of design

patterns that are based on the idea of objects as entities. However, many of those design
patter ns are useful for designing control systems .

2 The MCDM METHODS
The Multicriterial evaluation is used in cases where there are several alternatives, variations,
locations or processes that have to be assessed by their total environmental load or quality.
The common result of multicriterial evaluation methods is dimensionless number that
indicates the degree of environmental load of alternatives that are valued. In addition to
indicators that represent the environmental impact it is possible to include indi cators that
have economic, social, and technological character.
2.1 The TOPSIS method
The TOPSIS method ( Technique for Order Preference by Similarity to Ideal Solution ) is
based on the concept that the chosen alternative should have the shortest distance from the
ideal solution and the longest from the anti -ideal solution.
For the multicriterial analysis, through which it was possible to determine the most polluted
point both with regard to carbon monoxide pollution and from the point of view of noise
pollution, there has been used the so -called TOPSIS method.
Mathematically, the application of the TOPSIS method involves following steps:
Step 1: Establishing the decision matrix
The first step of the TOPSIS method involves the construction of a decision matrix
m X n, (DM).
C1 C2 … Cn
L1
L2
…
Lm X11
X21
…
Xm1 X12
X22
…
Xm2 …
…
…
… X1n
X2n
…
Xmn
where i is the criterion index (i = 1 . . . m); m is the number of potential solutions and j is the
alternative index (j = 1 . . . n). The elements C 1, C 2, …C n refer to the criteria, while L 1, L2,
…L m refer to the alternative solutions. The elements of the matrix are related to the values of
criteria i with respect to the alternative j.
Step 2: Calculating a normalised decision matrix
The normalised values r i,j denote the normalised decision matrix (NDM) which
represents the relative performance of the generated design alternatives:

𝑁𝐷𝑀 =𝑟𝑖𝑗=𝑥𝑖𝑗
√∑ 𝑥𝑖𝑗2 𝑚
𝑖=1 (1)
Step 3 : Determining the weighted decision matrix
Not all of the selection criteria may be of equal importance and hence weighting was
introduced prior to quanti fying the relative importance of the different selection criteria. The
weighting decision matrix is easily constructed by multiplying each element of each column
of the normalised decision matrix by the predefined weights:
V= vij = wj rij (2)
Step 4: Identifying the positive and negative ideal solution
The positive ideal solution (PIS), (A+) and the negative ideal solution (NIS), (A-) are
defined according to the weighted decision matrix via the equations given below.
DM =

𝑃𝐼𝑆 =𝐴+= {𝑣1+,𝑣2+,…𝑣𝑛+},𝑤ℎ𝑒𝑟𝑒:𝑉𝑗+={(max (𝑣𝑖𝑗) 𝑖𝑓 𝑗∈𝐽),(min (𝑣𝑖𝑗) 𝑖𝑓 𝑗∈𝐽′)} (3)

𝑁𝐼𝑆 =𝐴−= {𝑣1−,𝑣2−,…𝑣𝑛−},𝑤ℎ𝑒𝑟𝑒:𝑉𝑗−={(min (𝑣𝑖𝑗) 𝑖𝑓 𝑗∈𝐽),(max (𝑣𝑖𝑗) 𝑖𝑓 𝑗∈𝐽′)} (4)

where J is associated with the beneficial attributes and J’ is associated with the non -beneficial
attributes.
Step 5: Calculating the separation distance of each competitive alternative from the
ideal and no n- ideal solution:
𝑆+=√∑ (𝑉𝑗+−𝑉𝑖𝑗)2 𝑛
𝑗=1 𝑖=1,…,𝑚 (5)
𝑆−=√∑ (𝑉𝑗−−𝑉𝑖𝑗)2 𝑛
𝑗=1 𝑖=1,…,𝑚 (6)

Step 6: Measuring the relative closeness of each solution to the ideal solution. For
each competitive alternative there has to be computed the relative closeness of the potential
solution with respect to the ideal solution.
𝐶𝑖=𝑆𝑖−
𝑆𝑖++𝑆𝑖− ,0≤𝐶𝑖≤1⁄ (7)
Step 7: Ranking the preference order
According to the value of C i, the higher the value of the relative closeness, the higher
the ranking order and hence the better the performan ce of the alternative. Ranking of the
preference in descending order thus allows the comparing of relatively better performances.
2.1 The AHP method
Analytic Hierarchy Process (AHP) is used for decision making when a decision (choice of
some of the available alte rnatives, or their ranking) is based on several attr ibutes that
represent criteria [1] . Solving complex decision problems using AHP method is based on
their decomposition in a hierarchical structure whose elements are goal (objective), criteria
(sub-criter ia) and alternatives. An important component of the AHP method is a
mathematical model by which priorities of elements are calculated (weighted), for elements
that are on the same level hierarchical structure. AHP was successfully used in environmental
impact assessment for determining of weights for impact categories in paper [3]. In paper [4]
AHP was used for verification of results gained by quantification of environmental aspects
and impacts. Summary of AHP method consists of converting subjective assessments to the
relative importa nce of the criteria scores and weights. The method, developed by Saaty [ 2],
proved to be the most common form of multi -criteria analysis. AHP input data are answers
to questions such as "How important is criterion A relative to criterion B?". This results are
compared in pairs, resulting are in scores and weights. For each pair of criteria required
comparing the importance of the two, associating a score as follows (Table 1):

Table 1 Saaty table
Definition Intensity of importance
Equally important 1
Moderately more important 3
Strongly more important 5
Very strongly more important 7
Extremely more important 9
Intermediate values 2,4,6,8

Numbered intermediate values can be used to define nuances among the five basic
formulation. Of course, if it is considered that B is very strongly more important than A,
when the opposite is true, so A is assigned the value of 1/7 compared to B. Therefore, since
it is assumed that judgments are consistent with respect to all pairs and all the criteria are
"equally important" to themselves, the total number of evaluations will be:
1
2×𝑛×(𝑛−1)

Fig. 2 General hierarchical model in AHP
Application of AHP method can be explained in four steps:
1. Setting a hierarchical model of decision problems in order with goal on the top
criteria and subcriteria at lower levels, and alternatives at the bottom of the model
(Fig. 1).
2. At each level of hierarchical structure each elements of the structure are compared
in pairs, whereby the decision makers express their preferences with the help of
appropriate scale which has 5 degrees and 4 sub -degrees of verbally described
intensities and the corresponding numerical values for them in the range from 1 to
9 (Table 1).
3. Local priorities (weights) of criteria, sub -criteria and alternatives at same
hierarchical structure level are calculated through appropriate mathematical model
and afterwards they are synthesized in total priorities of alternatives.
4. Implementation of the sensitivity analysis for final decisions
The matrix A has special fe atures (all of it’s rows are proportional to the first row, and
they are all positive and aij = 1/aji is true) and because of that only one of it’s eigenvalue
differs from 0 and is equal to n.
If the matrix A contains inconsistent estimates (in practical e xamples almost always), weight
vector w can be obtained by solving the equation (A−λmax I)w=0 with prerequisite that Σwi
= 1, where λ max is the largest eigenvalue in matrix A. Because of matrix A properties λ max
≥ n, the difference λ max – n is used in measuringes timations consistency. With consistency
index CI=( λmax − n)/(n −1) measure of consistency can be calculated:
CR=CI/RI

Fig. 3 Matrix A

The next step is to determine the set of weights that are most consistent with the estimates of
the relative importance of the criteria. This can be done in several ways. In the method
developed by Saaty [1] , the calculation of the weights is based on a relatively complex
mathematical apparatus, using matrix algebra tools. The results are values associated to
eigenvector of maximum eigenvalue matrix.
The calculations are quite complex, so it is necessary to use a dedicated program.
But in practice, we provide a simple method of calculation, which gives the same result with
two decimal places:
 Calculate the geometric mean of each row of the matrix.
 It calculates the sum of the geometric mean.
 Normalized geometric mean.
2 Results and discussions
The system allows both measurements using dedicated equipment, and with the help of
sensors and acqui sition board. Measurements can be performed with the help of the mobile
laboratory.
Monitoring points were established to evaluate the impact of road traffic on
environment and implicitly on people.
The data obtained through hardware presented will be analyzed in Labview software.This
permitted:
 Easy used Arduino digital input/output , analog input, I2C, and Serial Peripheral;
 Interface from LabVIEW;
 I/O engine sketch to load on Arduin;
 Communication wireless via XBee or Bluetooth;
 Loop rates: USB tethe red (200 Hz) and wireless (25 Hz);
 IDE arduino sketch and LABVIEW toolkit VIs help to specification functionality.
The panel of this virtual instrument is shown in figure 4.

Fig. 4 The panel of CO concentration part of the system of automatic aquisition , processin and
analyze data
Furthermore, we will present one of the most important parts of the automatic acquisition,
processing and analysis of environment data system, which is the analysis of the AHP multi –
criteria method implemented within the system . The virtual instrument created in this system
is based on objected -oriented programming. The input data is processed by using the
LabVIEW functions. With the help of these functions the data is memorised in a database, in
order to be further used in the realization of the objected classes for the generation of the
intelligent maps of the Geomedia Professional Software.

The system allows measurements for the diverse polluting chemical factors, like: NOx, PM10
dust and ozone.
The multi -criteria analysis that we will further present it is based on real measurements of
the NOx, PM10 dust and ozone made in 4 different points from Sibiu: Union square, Alba –
Iulia street, DN 1 306 km and the Sub Arini park.
The virtual apparatus pro jected for the realization of the multi -criteria analysis works as
follows:
 A subjective appreciation of the importance of each point stated above is made
depending on the number of vehicles, number of people that are found in that concrete
point at a cert ain time. The comparison matrix of pairs is realized, the weight of each
observation point is calculated, the priority vector Lamda, CR and CI are being
calculated. The block diagram are presented in figure 5.
 Depending on the measurements made for each of the polluting factors, the virtual
apparatus will calculate the weight of each polluting factor from the observation points
taken into account, figure 6

Fig. 5 The diagram of VI for measurement points in AH P analysis

Fig. 6 The panel of VI for all polluant factors
 Finally, the matrix of polluting factors is obtained. This will be multiplied with the

observation point’s weights matrix. The virtual apparatus allows the determination of
the most and least pol luted observation point, depending on the subjective
appreciation made upon them and also, depending on the weight that each polluting
factor has in the observation points. The final results are presented in figure 7.

Fig. 7 The matrix of weght for polluant factors and final matrix with results
For the presented example we can observe that the most polluted point is DN 1 306 km,
followed by Alba Iulia street, Unirii Square and, as expected, Sub Arini park.
The presented system allows the automatic m emorization of data measured and processed in
ACCESS database. This is very important for the GIS Maps generation. The ACCESS table
in which the data is saved is in this way projected, as for it to hold the specific object class
attributes, which will be p resented in the map that will be generated using the Geomedia
Professional Software, in figure 8 .

Fig. 8 The map with the observation points and database connection
The automatic system for the taking over, processing and data analysis allows obtaining the
object class attributes used for generating the intelligent maps.
The data are taken over and processed using the objectual technology and the implemented
functions in LabVIEW. Plus, the system offers the possibility of the data analysis through
sensors, microcontrollers, acquisition boards and specific apparatus, which is highly unused

in the current literature. The multi -criteria analysis is made depending on the user’s will. The
elaborated system is very important because of the fact that it exclud es the intervention of
the human factor in the acquisition process, taking over and data memorisation.
In the future the system will be extended by using other analysis methods, like for instance
the TOPSIS method or other optimization technics based on genetic algorithms. Also, it is
very important the automatic generation of the pair comparison matrix, de pending on the
factor’s values
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life cycle assessment, multi -criteria analysis and environmental performance”, Journal of
Cleaner Production 15 (18), 1787 -1796 , (2007)
4. Maliki, G. Owens, and D. Bruce, “ Combining AHP and TOPSIS Approaches to Support Site
Selection for a Lead Pollution Study ”, 2nd International Conference on Environmental and
Agriculture Engineering, IPCBEE vol.37 (2012) © (2012) IACSIT Press, Singapore, 2012 .
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Proceedings of ICALEPCS07, Knoxville, Tennessee, USA, (2014)