S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology
ISSN: 0975-766X CODEN: IJPTFI Research Article
Available Online through www.ijptonline.com OPTIMAL CLUSTER HEAD SELECTION USING ANFIS FOR AN EFFICIENT DATA TRANSMISSION IN WIRELESS SENSOR NETWORK
S. Emalda Roslin1*, N.M. Nandhitha2, Rekha Chakravarthi3, N. Selvarasu4 Associate Professor1,3, Faculty of Electrical & Electronics, Sathyabama University, Chennai, Tamil Nadu, 600 119. Professor2, Faculty of Electrical & Electronics, Sathyabama University, Chennai, Tamil Nadu, 600 119. 4 Assistant Professor , Department of ECE, Misrimal Navajee Munoth Jain Engineering College, Thorapakkam, Chennai, Tamil Nadu, 600 119. Email:
[email protected] Received on 06-08-2016 Accepted on 27-08-2016 Abstract: In event driven WSN applications like telemedicine, e-health monitoring etc. , high speed reliable data transmission is crucial. However, limited battery resource of the sensor node affects the network connectivity and thereby affects the reliable data transmission. Clustering is the most commonly used technique where data is reliably transmitted through the selected cluster heads. In this paper, ANFIS based system is developed for cluster head selection. RSSI, Residual Energy and Distance are the node parameters for selecting the cluster heads. Also the choice of the membership function and the number of levels is optimized for accurate selection of cluster head. Four levels of Gaussian membership function performs better than triangular, trapezoidal, Gaussian bell shaped membership functions. Keywords: Wireless Sensor Network, Clustering, ANFIS, Received Signal Strength Indicator, Residual Energy, Distance. 1. Introduction: WSN finds enormous applications in various fields like military surveillance, environmental monitoring and disaster monitoring. However in recent years it is widely used in telemedicine, to assist decision making at the physician’s end. Here, a group of sensor nodes are closely deployed for transferring the bio signals of the patients to the physician. Major challenge in sensor nodes lies in effectively utilizing the energy. It is because the lifetime of the battery is less due to depletion of energy. Hence it is necessary to develop energy efficient routing techniques for effective transmission of data. Initially, direct long distance communication was performed between source nodes and the base station. This leads IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 16494-16502
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology to vast and fast depletion of nodes energy. So to overcome this, multi hop communication was introduced. Compared to direct communication, the multi hop communication in a network achieves high energy efficiency. Further increase in energy efficiency is achieved by clustering the network. However the performance of multi hop communication is dependent on the efficient selection of the Cluster Head (CH). Conventionally decision tree based techniques were used for CH selection. Later the paradigm shifted towards using intelligent computing techniques for CH selection. Fuzzy based CH selection was proposed in certain literatures. However the performance of such systems is strongly dependent on the choice of the membership functions. Few researchers also developed neural network based CH selection system. However the performance of these systems is strongly dependent on the training dataset provided to the network. Performance of fuzzy based system can be improved if the membership functions and the number of MFs are optimized. Adaptive Neuro Fuzzy Inference System (ANFIS) uses Back Propagation Network (BPN) for optimizing the membership functions. In this paper, ANFIS based CH selection is proposed. The input parameters to the networks are Residual Energy, Distance and RSSI. Performance of the proposed network is measured in terms of Mean Square Error. Also the choice of the membership function and the number of MFs is also optimized. This paper is organized as follows: Section 2 discusses on the literature review. The methodology of the proposed work is explained in section 3. The results and discussion is dealt in section 4. And the paper is concluded with the future scope in section 5. 2. Related Works: Qiang et al (2015), proposed an energy efficient cluster head selection mechanism for data processing in WSN. The authors designed an effective model for the selection of optimized cluster heads resulted in reduced energy consumption and prolonged network lifetime. Residual energy, distance and number of rounds were considered for the selection of cluster heads. Rana et al (2015), proposed fuzzy logic based multiple cluster head selection for energy efficient routing in WSN. Cluster heads and cluster head leaders were selected using fuzzy logic, considering shortest energy path routing. The proposed protocol extended the network lifetime by reducing the energy consumption. Rajagopal et al (2015), implemented Bacterial Foraging Optimization (BFO) in Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol for cluster head selection in WSN. Euclidean distance between the nodes with the cluster head candidate and the nodes residual energy were the parameters considered. The authors showed better performance in terms of end to end delay, packet drop ratio and network lifetime. Sharma et al (2015), proposed artificial neural network based cluster head
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology selection in WSN. Radial basis function network model was used in the proposed method. Residual energy is the parameter used for the selection of cluster heads. The performance of their methodology is evaluated based on the energy consumption, number of dead nodes, number of rounds and number of cluster heads. Mohan, Sarojadevi (2015), incorporated LEACH protocol by considering the metrics residual energy and distance. They adopted dynamic clustering and static clustering in two rounds of cluster head selection. They proved the effectiveness of the technique by extending the network lifetime, increasing throughput and packet delivery ratio. Gajjar et al (2014), presented fuzzy logic based cluster head selection. Residual energy, reachability, quality of communication link and distance from the base station were the metrics used. The proposed work achieved reduced energy consumption and increased reliability. 3. Methodology: In order to develop an ANFIS based cluster head selection, it is necessary to train the network through supervised learning. An exemplar is generated with RSSI, RE and distance as the input parameters and CH node as the output parameter. In this way a set of 30 exemplars were generated. ANFIS is trained and tested with two different datasets. In order to optimize the choice of the number of CHs, 8 different membership types were considered namely Trimf, Trapmf, Gbellmf, Gaussmf, Gauss2mf, Dsigmf, Psigmf and Pimf. Each membership type is assigned different number of membership functions (3 MFs, 4 MFs, 5 MFs). The actual output obtained is the list of optimized CHs. The actual output is also compared with the desired output to calculate the Mean Squared Error (MSE). MSE is the metric used to study the performance of the proposed system. 4. Results and Discussions: The desired output is compared with the actual output obtained using various membership functions. The actual output achieved for 3 MFs, 4 MFs and 5 MFs are shown in Tables 1, 2 and 3. The MSE is calculated based on the desired and the actual output for all the membership types and is listed in Table 4. Table-1: Comparison of Actual and Desired output for 3 MFs. Actual Output (3 MF) Desired Output
Trimf
Trap
Gbell
Gauss
gauss2 Dsig
Psig
Mf
mf
mf
mf
mf
Mf
pimf
0
0.94
0.65
0.91
0.97
0.84
0.83
0.66
0.82
1
0.33
-0.1
0.75
0.56
0.99
0.8
0.58
0.73
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0
0.62
S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology 0.46 0.58 0.6 0.49 0.47 0.43 0.42
0
0.25
-0.1
-0.02
0.06
-0.04
-0
-0.1
-0
1
1.22
0.99
0.98
1.06
0.88
0.87
0.88
0.79
0
-0.6
-0.2
-0.45
-0.5
-0.26
-0.2
-0.3
-0
0
-0.9
-0.3
-0.59
-0.8
-0.24
-0.3
-0.2
-0.2
1
0.23
0.01
0.17
0.22
0.04
0.03
0.02
0
0
0.09
-0
-0
0.02
-0.02
-0
-0
-0
0
0.47
0.05
0.27
0.34
0.12
0.1
0.1
0
1
0.13
0.01
0.08
0.11
0.02
0.01
0.02
0
0
-0
-0
-0.04
-0
-0.01
-0
-0
-0
Table-2: Comparison of Actual and Desired output for 4 MFs. Actual Output (4 MF) Desired Output
trimf
Trap
Gbell
Gauss
gauss2
Dsig
Psig
mf
mf
mf
mf
mf
mf
Pimf
0
0.04
0.1
0.22
0.22
0.1
0.1
0.1
0.06
1
0.38
0.06
0.17
0.55
0.04
0.06
0.08
0.01
0
0.03
0
0.09
0.08
0
0
0
0
0
-0.1
0
-0.28
-0.1
-0.2
-0.2
-0.1
0
1
0.68
0.78
0.78
0.89
0.82
0.82
0.83
0.85
0
-0.1
-0.35
-0.29
-0.3
-0.4
-0.4
-0.4
-0.1
0
-0.1
0
0
-0
0
-0
-0
0
1
0.11
0
0.09
0.06
0.02
0.02
0.02
0
0
-0
0.06
0.04
0.01
0.03
0.05
0.06
0.02
0
0.01
0
0.03
0.01
0
0
-0
0
1
0
0
0.01
0.01
0
0
0
0
0
-0.1
0
-0.27
-0.1
-0.2
-0.2
-0.1
-0
Table-3: Comparison of Actual and Desired output for 5 MFs. Actual Output (5 MF) Desired Output
trimf
Trap
Gbell
Gauss
gauss2
Dsig
Psig
Pim
mf
mf
mf
mf
mf
mf
f
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology 0 0.15 0.22 0.01 0.02 0.01 0
0
0.24
1
-0
0
0.1
0.07
0
0
0
0
0
0
0
0.02
0.02
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0.37
0
0.14
0.13
0.04
0.02
0.07
0.01
0
-0
0
-0
-0.02
0
-0
-0
0
0
0
0
0
0
0
0
0
0
1
0
0
0.01
0
0
0
0
0
0
-0
0
-0
-0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
-0
0
-0
-0.01
0
-0
-0
0
Table-4: MSE comparison for various MF types.
MF Type
MSE
MSE
MSE
(3 MF)
(4 MF)
(5 MF)
Trimf
0.3861
0.1927
0.2879
Trapmf
0.3336
0.255
0.3333
Gbellmf
0.2814
0.2355
0.2973
Gaussmf
0.3272
0.1889
0.3055
gauss2mf
0.2502
0.2606
0.3275
Dsigmf
0.2511
0.2619
0.3295
Psigmf
0.2398
0.2519
0.3222
Pimf
0.2513
0.2525
0.3314
When the number of membership functions is 3, the MF type psigmf gives a minimum mean square error value. Gaussmf gives a minimum MSE when 4 membership functions are used. When the number of membership functions is 5, the MF IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 16494-16502
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology type trimf gives a minimum MSE. Average MSE is less for 4 MFs compared to 3 MFs and 5 MFs. Figure 2 shows the comparison graph for the actual and desired output when same exemplars is used for testing and training process. Comparison between the actual and desired output using the MF type psigmf for 3 MF is shown in Figure 3, and the corresponding MFs for the input attributes is shown in Figure 4. For MF type Gaussmf with 4 MFs, the comparison graph between desired and the actual output is shown in Figure 5 and the MFs for the input attributes is shown in Figure 6. The comparison graph for the MF type trimf with 5 MFs is shown in Figure 7 and the MFs for the input attributes is shown in Figure 8.
Figure 1: Flow Diagram of the Proposed Work. Comparison of Actual and Desired Output 1.2 Training Data ANFIS Output
1
Target
0.8
0.6
0.4
0.2
0
-0.2
0
2
4
.
6 8 Input Samples
10
12
14
Figure 2: Comparison of actual and desired output for training and testing same exemplars. Comparison of Actual and Desired Output 1 Test Data ANFIS Output
0.8
Target
0.6
0.4
0.2
0
-0.2
-0.4
0
2
4
6 Input Samples
8
10
12
Figure.3: Comparison of actual and desired output using the MF type psigmf for 3 mf. IJPT| Sep-2016 | Vol. 8 | Issue No.3 | 16494-16502
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology Membership functions for Input Variables using psigmf 1 0.5
Degree of Membership
0
0
10
20
30 Distance (m)
40
50
60
0 -30
-25
-20
-15 RSSI (dB)
-10
-5
0
1 0.5
1 0.5 0 0.1
0.2
0.3
0.4 0.5 0.6 0.7 Residual Energy (Joules)
0.8
0.9
1
Figure.4: Input attributes with 3 MFs using psigmf. Comparison of Actual and Desired Output 1 Test Data ANFIS Output
0.8
Target
0.6
0.4
0.2
0
-0.2
-0.4
0
2
4
6 Input Samples
8
10
12
Figure.5: Comparison of actual and desired output using the MF type Gaussmf with 4 MFs. Membership functions for Input Variables using gaussmf 1 0.5 0 Degree 0of Membership 10
20
30 Distance (m)
40
50
60
-20
-15 RSSI (dB)
-10
-5
0
1 0.5 0 -30
-25
1 0.5 0 0.1
0.2
0.3
0.4 0.5 0.6 0.7 Residual Energy (Joules)
0.8
0.9
1
Figure. 6: Input attributes with 4 MFs using Gaussmf. Comparison of Actual and Desired Output 1. Test Data ANFIS Output
1
0.
Targe0.
0.
0.
0
-0.2
0
2
4
6 Input Samples
8
10
12
Figure.7 Comparison of actual and desired output using the MF type trimf with 5 MFs
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S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology Membership functions for Input Variables using trimf 1 0.5 0 0 10 Degree of Membership
20
30 Distance (m)
40
50
60
-20
-15 RSSI (dB)
-10
-5
0
1 0.5 0 -30
-25
1 0.5 0 0.1
0.2
0.3
0.4 0.5 0.6 0.7 Residual Energy (Joules)
0.8
0.9
1
Figure. 8: Input attributes with 5 MFs using trimf. 5. Conclusion and future Scope: In this paper, ANFIS based cluster Head selection system is developed for uninterrupted transmission of data between nodes in telemedicine. Choice of the membership functions and the number of membership functions for accurate Cluster Head selection are also optimized. In contrast to the general belief, that the increase in the number of membership functions improves the accuracy of prediction, it is found that increasing the number of membership functions has decreased the accuracy of CH selection. It has also increased the computational complexity. Hence its is concluded that the accuracy is high for Gaussian membership function with four number of MFs. However, combination of membership functions for Cluster Head selection is yet to be explored. References: 1.
Parupkar Singh, Kirtika Goyal, Energy efficient protocol for cluster head selection in multitier wsn, International Journal Of Advanced Research In Computer Science And Software Engineering 2015; 5(7): 179-182.
2.
Emalda Roslin S. and Gomathy C, IBPN: Intelligent Back Propagation Network based cluster head selection for energy efficient topology control in wireless sensor network, European Journal of Scientific Research 2012; 79(4): 541- 550.
3.
Rekha Chakravarthi, Performance evaluation of fuzzy and BPN based congestion controller in WSN, International Journal of Engineering and Technology 2015.
4.
Emalda Roslin, N. M. Nandhitha, Rekha Chakravarthi, Cynthia James, Monisha A., Node state information based adaptive power allocation and energy efficient routing using fuzzy logic for wireless sensor network, International Journal of Applied Engineering Research 2014; 9(21): 8575-8592.
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5.
S. Emalda Roslin* et al. /International Journal of Pharmacy & Technology Yan Qiang, Bo Pei, Wei Wei, and Yue Li, An efficient cluster head selection approach for collaborative data processing in wireless sensor networks, International Journal of Distributed Sensor Networks 2015.
6.
Sohel Rana, Ali Newaz Bahar, Nazrul Islam, Johirul Islam, Fuzzy based energy efficient multiple cluster head selection routing protocol for wireless sensor networks, International Journal of Computer Network and Information Security 2015; 4: 54-61.
7.
Rajagopal, S. Somasundaram, B. Sowmya, T. Suguna, Soft computing based cluster head selection inwireless sensor network using bacterial foraging optimization algorithm”, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering 2015; 9(3): 379-384.
8.
Siddhi Sharma, Deepak Sethi, P. P. Bhattacharya, Artificial neural network based cluster head selection in wireless sensor network, International Journal of Computer Applications 2015; 119(4): 34-41.
9.
B A Mohan, Sarojadevi H, Efficient cluster head selection method with uniform clusters size to prolong the network lifetime of wireless sensor network, International Research Journal of Engineering and Technology 2015; 2(4): 863867.
10. Sachin Gajjar, Mohanchur Sarkar, Kankar Dasgupta, Cluster head selection protocol using fuzzy logic for wireless sensor networks, International Journal of Computer Applications 2014; 97(7): 38-43. 11. \https://en.wikipedia.org/wiki/Mean_squared_error Corresponding Author: S. Emalda Roslin*, Email:
[email protected]
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