2011 International Conference on Computer Communication and Management
Proc .of CSIT vol.5 (2011) © (2011) IACSIT Press, Singapore
Multi Criteria Decision Making for Optimal Sensor Selection
1
Abdolhossein Alipoor 1, a), Farhad Hadinejad 2, b)
CE Department, Islamic Azad University, Babol Branch, Iran
2
IE Department, Shomal University, Amol, Iran
a)
hosseinalipoor@srbiau.ac.ir
b)
Farhad_Hdng@yahoo.com
Abstract. Today object recognition by using sensor fusion systems is one of the main issues for target detection
and environmental monitoring. Environmental condition and target characteristic are two parameters which can
change sensor value in target recognition and there are many factors which can impact on the sensor numbers
utilizing for object recognition. Object recognition rate, object recognition time, operation cost and system’s
reinforcement rate are four important criteria help for optimal sensor selection. Sensor selection system designers
according to Environmental condition and target characteristic, weighting these four factors in every situation.
Therefore optimal sensor selection according to criteria preference decides which sensors group must be utilized to
obtain the best consequence. This paper attempts to design an optimization service by using Multi Criteria Decision
Making (MCDM) techniques like AHP and PROMETHEE and their related soft ware’s EXPERT CHOICE and
DECISION LAB for criteria weighting. For Achieving the object recognition rate and object recognition time of
every sensory group, genetic algorithm and neural network are used. This service specifies highest recognition rate
for each sensory group. The contribution of this paper is the use of neural network as an estimator to evaluate the
fitness value of each optimization algorithms and after that select optimal sensory group by utilizing MCDM
method based on four significant factors.
Keywords: Intelligent Sensor Selection, Multi Criteria Decision Making, Genetic Algorithm, Neural
Network.
1. Introduction
Sensor selection is a fundamental issue in sensor networks. Many techniques have been developed for
general optimized solution searching for sensor selection. These techniques range from utilizing Multi
objective optimization for object recognition [1] to applying constraints on the objective function to
streamline the optimization process [2] to applying advanced artificial analysis techniques such as genetic
algorithms and simulated annealing algorithms [3]. Unique optimization techniques have been proposed such
as particle swarm optimization [4] and cutting and surrogate constraint analysis [5]. Genetic algorithm uses a
set of chromosomes to present possible solutions for solving problems. Each chromosome contains
substrings called genes, expressing variants of the problem space. In this paper, genetic algorithm is used to
find the best sensors group for object recognition in each situation. Figure1 illustrates a chromosome that is a
set of sensors defined as one possible solution. Each chromosome has 10 genes and shows the recognition
rate of each sensor. In this figure, Sensors 1, 3, 5, 6, 8 and 9 are used.
S1
1
S2
0
S3
1
S4
0
S5
1
S6
1
S7
0
S8
1
S9
1
S10
0
Fig.1. Chromosome structure.
Figure 2(B) illustrates the whole process of sensor selection using genetic algorithm. The contribution of
this paper is the use of neural network as an estimator to evaluate the fitness value of each genetic algorithm
chromosomes. In all previous methods at first made a fitness function and then use optimization approach to
find the optimal solution so this method is the first utilizing neural network within an optimization algorithm for
sensor selection in environmental monitoring or target recognition [3 and 6]. After object recognition rate and
time obtained by genetic algorithm and neural network, use AHP method to weighting the optimal sensor
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selection criteria in each situation. This criteria weighting performed by EXPERT CHOICE software. After that
by utilizing PROMETHEE method delineate which sensory group must be used. This decision making achieved
by DECISION LAB software.
2. Evaluating every sensory group object recognition rate
Due to different efficiency of sensors and also many effects of environmental conditions on sensors'
performance, optimized sensor selection is so important and the most important part of optimization problems is
to evaluate fitness of any solution which showing to what extend optimized the selected solution. Since there is
no assessment function for acquiring the best configuration of sensors on the basis of problem space variation,
we utilize a Multilayer perceptron neural network (MLPNN) for fitness value estimation. The reason of using
this method refers to our previous research [8], which depicted that MLP method is one of the best methods for
evaluating fitness function in optimization algorithm for sensor selection. MLPNN has 10 inputs, includes
sensors recognition rates, which are the second six genes of each chromosome and has 3 layers. The first layer
has 50 neurons; the second one has 20 neurons and the last one has1neuron. The first and second layer take
advantage of Tansig activation function and the third layer use pure line activation function. Error back
propagation training algorithm with 0.1 learning rate and The LEVENBERG-MARQUART function is used for
the training part. Besides, the network training stop criteria is specified as the 1e-05 mean square error.
Supervised learning model is used for neural networks training by using MATLAB toolbox. To estimate the
fitness value of each chromosome accurately, at first the neural networks should be trained with a set of real
data. In this problem, the neural network is trained with 500 samples. When neural network performs the
accurate estimation on real data, it presents suitable response per each input. Recognition task in this paper is
done using 10 sensors in limited geographical area. In order to get real data, sensors in 500 different scenarios
with different conditions are run and get target existence recognition accuracy rate of each sensor group. We use
them as real data in neural networks training stage and after that, the neural networks will be able to estimate the
output of sensors in new scenario [7]. After utilizing neural network in genetic algorithm as fitness function
estimator, optimization algorithm run and obtained object recognition rate and time. Fig.2. (B) illustrates the
result of using genetic algorithm approach with 10 sensors for object recognition which obtains % 0.984
recognition rates in 51 seconds.
FIG.2. (A) THE GENETIC ALGORITHM PROCESS.
FIG.2. (B) RESULT OF SENSOR SELECTION WITH GENETIC ALGORITHM.
3. Multi Criteria Decision Making Technique
3.1. Analytic Hierarchy Process (AHP)
The AHP which is a powerful tool in applying MCDM was introduced and developed by Saaty in 1980[9].
In the AHP method, obtaining the weights or priority vector of the alternatives or the criteria is required. For
this purpose Saaty has used and developed the Pairwise Comparison Method (PCM). In the AHP the decision
making process starts with dividing the problem into a hierarchy of issues which should be considered in the
work. These hierarchical orders help to simplify the illustration of the problem and bring it to a condition
which is more easily understood. In each hierarchical level the weights of the elements are calculated. The
decision on the final goal is made considering the weights of criteria and alternatives. In Figure 3, where the
structure of AHP elements is illustrated, it is shown that the goal is decided through a number of different
criteria. These criteria determine the quality of achieving the goal using any of Alternatives (Ai, i=1... k). The
Ai is different options, choices or alternatives that could be used to reach the final aim of the project.
Comparing these alternatives and defining their importance over each other are done using the Pairwise
comparison method [10]. Giving importance ratios for each pair of alternatives, a matrix of Pairwise
comparison ratios is obtained. The criteria might also have different importance compared to each other.
Therefore a Pairwise comparison matrix is considered for the criteria. Elements of this matrix are Pairwise or
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mutual importance ratios between the criteria which are decided on the basis that how well every criterion
serves and how important it is in reaching the final goal. For creating the Pairwise comparison matrix in the
PCM, Saaty has employed a system of numbers to indicate how much one criterion is more important than the
other. These numerical scale values and their corresponding intensities are stated in Table 1.Hence AHP
method is used to weighting our four criteria with Pairwise Comparison Method. Figure 3 (a), (b) show the
Criteria Pairwise Comparison and Criteria weighting based on AHP method with Expert choice software and
depict that object recognition rate has the maximum priority equal 0.553 and after that operation time equal
0.271 and reinforcement rate equal 0.114 and finally dedicate weight equivalent 0.062 for operation cost.
FIG.3. STRUCTURE OF THE AHP.
Intensity of importance
1
3
5
7
9
Verbal judgment of preference
Equally importance
Moderate importance
Strong importance
Extreme importance
Extremely more importance
2,4,6,8
Intermediate values between
adjacent scale values
TABLE 1. SCALES IN PAIRWISE COMPARISONS
FIG.3. (B) CRITERIA WEIGHTING BASED ON AHP METHOD
WITH EXPERT CHOICE SOFTWARE
FIG.3. (A) CRITERIA PAIRWISE COMPARISON BASED ON
AHP WITH EXPERT CHOICE SOFTWARE
3.2. Promethee methodology
PROMETHEE is a MCDM method developed by Brans [11]. It is a ranking method quite simple in
conception and application compared to other methods for multi-criteria analysis [12]. Let A be a set of
alternatives and gj (a) represent the value of criterion gj (j =1, 2,..., J) of alternative a ∈ A. As the first step in
PROMETHEE a preference function Fj (a, b) is defined for each pair of actions for criterion gj .Assuming
that more is preferred to less. Where qi and pi are indifference and preference thresholds for ith criterion
respectively.
Fj (a, b) =0
iff gj (a) − gj (b) ≤ qj,
iff gj (a) − gj (b) ≥ pj,
Fj (a, b) =1
0<Fj (a, b) <1 iff qj <gj (a) − gj (b) <pj
Different shapes (six types) for Fj have been suggested. If a is better than b according to jth criterion, Fj
(a, b)>0, otherwise Fj (a, b) =0. Using the weights wj assigned to each criterion (where ∑wj = 1), one can
determine the aggregated preference indicator as follows: Π (a, b) = ∑ wj fj (a, b). If the number of
alternatives is more than two, overall ranking is done by aggregating the measures of pair wise comparisons.
For each alternative a ∈ A, the following two outranking dominance flows can be obtained with respect to all
the other alternatives x ∈ A: Φ+ (a) = 1/n-1 ∑ Π (a, x) leaving flow. The leaving flow is the sum of the values
of the arcs leaving node a and therefore provide a measure of the outranking character of a. The higher the
φ+ (a), the better the alternative a: Φ-(a) = 1/n-1 ∑xЄa Π (x, a) entering flow. The entering flow measures
the outranked character. The smaller φ− (a), is the better alternative a. For each alternative a, it is obvious
that we can also determine the net flow for each criterion separately. Let us define the net flow for criterion
gj as follows: Φj (a) = 1/n-1 ∑xЄa (fj (a, x) - fj (x, a)). Φj (a) quantifies the position of alternative a
according to criterion j with respect to all the other alternatives in the set A. The larger the single criterion net
flow the better alternative a on criterion gj. Equality in φ+ and φ- indicates indifference among the two
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compared alternatives. In the case where the leaving flows indicate a is better than b, while the entering flows
indicate the reverse the two actions are considered incomparable [12] .Actions a and b are incomparable if:
[φ+ (a) > φ+(b) and φ-(a) < φ-(b)] or [φ+(a) < φ+(b) and φ-(a) < φ-(b)] and the complete ranking flow
given by Φ (a) = φ+ (a) – φ-(a). After putting every criterion weight in the weight’s field of DECISION LAB
as illustrate in Figure 4. (a) and according to PROMETHEE I as depict in figure Fig.4. (b) Object recognition
with utilizing 10 sensors has the highest rank which φ+ = 0.67, utilizing 9 sensors has the rank φ+ = 0.60,…,
and finally utilizing 5 sensors has the rank φ+ = 0.33.
4. experimental result
In this paper, multi objective optimization algorithm is used for sensor selection. Neural network is utilized
for the purpose of estimating fitness function of each genetic algorithm chromosomes. By using 500 different
scenarios, the training task of neural networks is done. The Levenberg-Marquart function is used for neural
network training. Consequently, the neural network approach obtained the efficiency with error accuracy of
1.85e-05. New scenario is defined and genetic algorithm is run to found the object recognition rate and time of
each sensors group in every situation. After that, multi criteria decision making methods used to weighting our
four criteria and ranking sensor groups as illustrated in figure 4. (a),(b). With criteria precedence changing,
sensory group selection completely converts. In scenario 1 when Dedicate the weight equal 55% to criterion
object recognition rate figure 4. (c) show that object recognition with 10 sensor has the highest rank but vice
versa in the scenario 2 when Dedicate the weight equal 55% to criterion operation cost figure 4. (d) show that
object recognition with 5 sensor has the highest rank. Therefore by utilizing this system we can analyze the
impact factor of MCDM methods on the optimal sensor selection.
Fig.4. (a)
Action and Criteria Weighting WITH DECISION LAB SOFTWARE
Fig.4. (c) SENSORY GROUP PRIORITIZING WITH DECISION LAB IN
SCENARIO 1
FIG.4. (B) RANKING SENSOR GROUPS WITH PROMETHEE METHOD
Fig.4. (d) SENSORY GROUP PRIORITIZING WITH DECISION LAB IN
SCENARIO2
5. Conclusion and future work
We conclude that utilizing multi criteria decision making is a good approach for intelligent sensor selection.
With the use of genetic algorithm the most suitable configuration of sensor is obtained. The role of neural
networks is to estimate the fitness value of each chromosome in the genetic algorithm. Using different kinds
of optimization method and compare their results to found the highest recognition rate for every specific
sensor numbers can be considered as future work.
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