Prolonging the lifetime of wireless sensor networks ... - IEEE Xplore

2014 International Conference on Signal Processing and Integrated Networks (SPIN)

Prolonging the Lifetime of Wireless Sensor Networks using Prediction based Data Reduction Scheme Dhirendra Pratap Singh

Vikrant Bhateja, Member, IEEE

Surender Kumar Soni

Department of Electronics and Communication Engineering, SRMGPC, Lucknow (U.P.), India

Department of Electronics and Communication Engineering, SRMGPC, Lucknow (U.P.), India

Department of Electronics and Communication Engineering, NIT Hamirpur, (H.P.), India

[email protected]

[email protected]

[email protected]

is sent to the CH; where CH aggregates the data coming from non-CHs and sends aggregated data to the BS [17].

Abstract— Wireless Sensor Networks (WSNs) are becoming popular for continuous monitoring of physical phenomena in diverse applications. These networks use sensors having batteries with limited power storage. Hence, it is desired to reduce energy consumption for prolonging the network lifetime in order to provide uninterrupted services. An efficient routing scheme is therefore devised in combination with energy saving techniques to enhance the lifetime of WSNs. This paper proposes a methodology which applies prediction based data reduction scheme for designing an energy efficient routing protocol. From simulation results, it can be inferred that the proposed protocol is exhibiting better performance in comparison to LEACH protocol; thus increasing the overall lifetime of WSNs.

Fig. 1. Cluster based routing scheme.

There were numerous improvements made in the existing LEACH protocol [20]-[21]. In the same direction, present work utilizes the role of node residual energy and distance of sensors from sink to develop an efficient CH election and routing scheme. The same has been achieved with the proposed cluster based routing scheme. The remaining part of the paper has been organized as follows: Section II presents proposed methodology of cluster-based routing along with two devised approaches for CH selection. Section III gives the simulation results along with their analysis and finally, the conclusions are drawn in section IV.

Keywords- Low Energy Adaptive Cluster-head Election Approach (LEACH); Prediction based Data Reduction Scheme; Wireless Sensor Networks (WSNs).

I.

INTRODUCTION

Wireless Sensor Networks (WSNs) have been projected to play primary role in defining new criteria of pollutant characterization and concentrations; where they find application in environmental and species monitoring [1]-[3]. WSNs are used to carry out measurement acquisitions that involve combined acquisition of electroencephalograms, electrocardiograms and ergospirometry signals. These help in identification of associated features related to epilepsy detection, breadth disorders and various cardiovascular diseases [4]-[15]. Energy consumption is an important constraint associated with WSNs owing to their usage in remote areas [16]-[17]. Hierarchical cluster tree topology is generally preferred for its scalability and simplicity. But, in multi-hop mode with hierarchical routing there is a good amount of energy reduction of WSNs. In this scheme, the entire network is divided into clustered layers as illustrated in Fig. 1[18]-19]. Hierarchical routing can be performed using a basic protocol LEACH; in this the sensor nodes deployed over the field of interest are also responsible for the medium access among the nodes in the clusters. The clusterhead (CH) node consumes more energy in comparison to the other cluster members as it has to perform some other specific tasks. To make energy consumption uniform among the nodes in the network, the role of the CH is rotated among all the cluster members. Data sensed by the cluster members

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II.

PROPOSED METHODOLOGY

In this paper two new clustering approaches have been proposed which are proved to be more energy efficient than LEACH. A. Background Energy consumption in the WSNs can be calculated according to the set of equation according to which the depleted energy of the sensor nodes to transmit a single bit over a distance ‘d’ from sensor nodes to BS is given by the expressions as in Eq. (1)-(2) [17].

E Tx ( l , d ) = l × E elec + l × ɽ mp × d ,for d > d 0

(1)

E Tx ( l , d ) = l × E elec + l × ɽ fs × d , for d ≤ d 0

(2)

4

2

§ ɽ fs · ¸¸ © ɽ mp ¹

where: d 0 = ¨ ¨

420

1 2

.


cluster from BS. BS selects the node having the maximum Fij for all j clusters shown in Eq. (6). CH j =max[Fij ] (6)

Energy consumed in receiving packet of l bits can therefore be expressed as in Eq. (3). E Rx ( l ) = E Rx − elec ( l ) = l × E elec (3)

where: CHj is the cluster head of jth cluster. After completing first round operation, cluster head election will again take place but this time, the last CHs will take the energy information of all the sensor nodes of their corresponding clusters. Election process will be same as discussed earlier. After electing next CHs, previous CHs will discard the energy information provided by the nodes. Election of CHs will take place in every round. Scheduling Phase provides every sensor node a dedicated time slot in which it can send data to the cluster head. After being elected as the CH, node will have the information of all cluster members provided by BS. This information is related to identity (ID) of the particular sensor node and its distance from BS. The CH node sets up a TDMA schedule and assigns each node a time slot when it can transmit. This schedule message is then broadcasted to all the nodes within the cluster. Intra-cluster scheduling is done using TDMA while inter-cluster scheduling is done using CDMA. Now, during Steady State Phase, after receiving the TDMA schedule created by their CH nodes, the non-CH nodes start transmitting data depending on the TDMA schedule. To synchronize and start the steady state phase at the same time, the BS can issue the corresponding synchronization pulses to all the nodes. The steady state phase is further divided into frames. During the assigned frame, the sensor node can transmit the data to the CH node. The duration of each frame is constant and depends on the number of nodes in cluster. CHs then start transmitting their data to the sink node within their CDMA schedule. In A1, the proposed clustering scheme has been implemented without using prediction based data reduction scheme. This approach reduces the energy consumption by reducing overheads in cluster formation and deciding CHs in each cluster. In A2, the GM (1, 1) prediction model has been implemented to minimize the number of data transmissions thereby reducing the energy consumption by suppressing overheads during cluster formation and deciding CHs (in each cluster). There is a further reduction in energy consumption with A2 by incorporating reduced data transmissions among each sensor node and their corresponding CH. As, energy is more consumed during transmission of data; A2 reduces more number of data transmissions in comparison to LEACH and A1.

Energy consumed in computation of Grey model for packet of l bits can be expressed as in Eq. (4), E Grey-computation ( l ) = l × E Grey

(4)

Eelec represents power consumed by launching circuit or receiving circuit, ɽmp and ɽfs represent energy consumed by the circuit to launch a single bit of information to 1 square meter in multi-path channel and free space respectively. EGrey is the energy consumption in computation of Grey model for 1 bit while l is the size of data packets in bits. The prediction based data reduction approach relies on the prediction based cooperation between sensor and the sink nodes. Both these nodes will use the same prediction model as well as data for prediction. Sensor node need not to send the data to the sink every time when there is a change in data. Sink node can predict the data in each sensor for that particular round. Thus, a significant amount of energy can be saved by reducing number of transmissions of data if sensor node and sink node efficiently predict the data. Grey prediction model is used for predicting the future values of a time series data using past values. GM (1, 1) i.e., a Grey model with first order one variable; this is computationally efficient and less complex to be widely used in many applications. The mathematical equations of GM (1, 1) model which have been used in the proposed approach A2 can be taken from the previous researches of GM (1, 1) model [22]-[29]. B. Proposed Protocol For the proposed hierarchical cluster based routing, a set of assumptions are made according to which N sensor nodes are distributed randomly in an (M × M) square field. Communication between CHs and BS as well as between sensors and CHs should be reliable; BS knows the location of all the sensor nodes and every node knows the location of other sensor nodes as well as BS. BS is outside the sensor field and has unlimited energy and memory resources and each node has an identity (ID). The proposed hierarchical cluster based routing performs the entire operation in three different phases: Cluster Head Election Phase, Scheduling Phase and Steady State Phase. In Cluster Head Election Phase, first BS divides the sensor field into the desired number of grids. Each grid is called cluster and BS sends a message to all sensor nodes about their corresponding clusters. For the first time, BS will calculate the Residual energy- Distance factor denoted as F expressed as in Eq. (5).

ª

Fij = «E ij +

«¬

º » Dij »¼ 1

III.

RESULTS AND DISCUSSIONS

A. Evaluation Parameters In the setup of simulation, the proposed network model consists of 100 sensor nodes deployed over a field of (M × M) square unit area. In this work, network has been divided into four equal parts; division into optimal number of clusters is an important issue to cover the entire field and all the sensor nodes. Table I illustrates the parameters with their values and units which have been used in simulations.

(5)

where: Fij is the Residual energy- Distance factor and Dij is the distance of Sij sensor node which is the ith node of jth

421


Time series prediction based data reduction scheme using GM (1, 1) model is implemented in designing an energy efficient cluster based routing protocol. The comparisons among LEACH, A1 and A2 (using GM (1, 1) model with prediction error threshold, ɽ = 3% and ɽ = 5%) is made and results have been analyzed in terms of various parameters using the Fig. 2 to Fig. 4. TABLE I. S. No.

FND for GM(1,1) with 3% prediction error threshold 800

Number of rounds

600 500 400 300

USED IN SIMULATION OF PROPOSED PROTOCOL

200 100

Parameters Used in Simulation Parameters

Value

1

Number of nodes deployed

100

2

Node distribution

(50,50) to (250,250)

3

Location of base station

(0,0)

4

Initial energy of each node (in Joules)

0.1

5

Data packet length (in bits)

800

6

Energy Packet Size (in bits)

400

7

Control Packet Size (in bits)

200

8

Eelec

50

9

ɽmpc (in nJ/bit/m4)

0.0013

200 100

b

0 50

2

ɽfs (in nJ/bit/m )

100

11

EDA and EGrey (in nJ/bit)

5

a

Window size

150 200 Network Side (in meters)

FND for GM(1,1) with 5% prediction error threshold 900 LEACH A1 A2

Number of rounds

700

Free space energy coefficient

c.

Multipath energy coefficient

600 500 400 300

0 50

100


250

Fig. 3. Comparison of LEACH, A1 and A2 in terms of FND at 5% threshold prediction error.

Sample data values used for prediction b.

250

Fig. 2. Comparison of LEACH, A1 and A2 in terms of FND at 3% threshold prediction error.

3 a.

100

800

10

12

LEACH A1 A2

700

It can be further seen that A2 with ɽ = 5% is giving the superior results. A tabulation of these FND values is made in Table II.

The various parameters for evaluation and comparison of results are FND, HND and Energy consumption [30]. FND is measured in terms of number of rounds to which all the sensor nodes of sensor network can serve the desired task whereas HND is measured in terms of number of rounds after which only 50% nodes remain for serving the task. Energy consumption per round shows the depleted energy per round.

TABLE II. N/W Side(m) 50 100 150 200 250

B. Simulated Results Figure 2 shows the graphical comparison among the LEACH and proposed approaches (A1 & A2) for different values of network side. From these figures, it can be interpreted that the proposed approaches yields better results for FND in comparison to LEACH.

COMPARISON OF LEACH, A1 AND A2 IN TERMS OF FND PARAMETER. LEACH 441 411 326 153 67

A1 565 518 495 376 230

A2 ȯ=3% 725 710 605 475 265

ȯ=5% 860 830 700 469 325

From the results given in table II, and Fig. 2 to 3, it has been observed that A1 & A2 have shown improvement over LEACH (in terms of FND) as the first node of network dies in LEACH after completing 441 rounds in case of 50 m network side whereas for A1, the first node dies after completing 565 rounds. In approach A2 (with ȯ=3% and ȯ=5%) completes 725 and 860 rounds respectively for FND. Even for the higher network sides, it can be seen that both the approaches (A1 & A2) are showing better performance in comparison to LEACH. Amongst all approaches, A2 with ȯ=5%, completes more number of rounds for FND. Figure 4 and 5 shows the comparison among the LEACH, A1 and A2 based on computed values of HND for different values of network side. From the figures, again it can be seen that A2 with ɽ = 5% is giving the superior results (These values are

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also shown tabulated in Table III). Proposed approaches (A1 & A2) have also shown improvement over LEACH in terms of network lifetime. Here, A1 is improving network lifetime by a factor of 1.65 while A2 does same by a factor of 2.1 and 2.51 (for ɽ = 3% and 5% respectively). Hence, this comparison shows that the A2 with ɽ = 5% is giving the best results between the two proposed approaches (A1 & A2) in terms of network lifetime making it a robust algorithm for routing in WSNs. In the later part, figure 6 and 7 illustrates the comparison among LEACH, A1 and A2 with same values of prediction errors in terms of energy consumption per round. The results with the proposed approaches show energy depletion at a slower rate in comparison to LEACH. In addition, as the network side increases, energy is consumed with very high rate in LEACH whereas this consumption is least with A2 (for ɽ = 5%). These WSNs with enhanced lifetime render noteworthy support in features extraction finding application in computer aided detection of breast cancer [31]-[47].

HND for GM(1,1) with 5% prediction error threshold 1400 LEACH A1 A2

1200

Number of rounds

1000 800 600 400 200 0 50

100


250

Fig. 6. Comparison of LEACH, A1 and A2 in terms of HND at 5% threshold prediction error. TABLE III. COMPARISON OF LEACH, A1 & A2 IN TERMS OF HND PARAMETER.

0.7

N/W Side(m) 50 100 150 200 250

LEACH A1 A2

0.6 0.5

A1

584 526 412 258 158

847 749 602 450 368

A2 ȯ=3% 1087 881 734 575 524

ȯ=5% 1391 1091 875 684 520

Energy consumption for GM(1,1) with 5% prediction error threshold 0.7

0.3 0.2 0.1 50

100


250

Fig. 4. Comparison of LEACH, A1 and A2 in terms of Energy consumption at 3% threshold prediction error.

HND for GM(1,1) with 3% prediction error threshold 1200 LEACH A1 A2

1000

Number of rounds

LEACH

0.4

Energy consumption per round (in mJ)

Energy consumption per round (in mJ)

Energy consumption for GM(1,1) with 3% prediction error threshold 0.8

LEACH A1 A2

0.5 0.4 0.3 0.2 0.1 0 50

800

100


250

Fig. 7. Comparison of LEACH, A1 and A2 in terms of Energy consumption at 5% threshold prediction error.

600

IV.

400

100


CONCLUSION

It can be concluded from the obtained results that with the proposed approach, a significant amount of energy can be saved by reducing the overheads in CH election process. Further, Time series prediction based data reduction scheme has minimized the energy consumption by reducing the number of data transmissions in intra-cluster and intercluster region. The proposed methodology has significantly improved the network lifetime as depicted by the computed values of FND and HND; thereby demonstrating improvements over LEACH.

200

0 50

0.6

250

Fig. 5. Comparison of LEACH, A1 and A2 in terms of HND at 3% threshold prediction error.

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REFERENCES [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

[15]

A. Lay-Ekuakille, G. Vendramin & A. Trotta, “LED-based sensing system for biomedical gas monitoring: Design and experimentation of a photoacoustic chamber, Sensors and Actuators B, Chemical, Vol. 135, No. 2, pp. 411-419, 2009. A. Lay-Ekuakille, V. Pelillo, C. Dellisanti and F. Tralli, “SAR Aided Method for Rural Soil Evaluation,” SPIE2002 Remote Sensing, Crete (Greece), Sept. 2002. A. Lay-Ekuakille, P. Vergallo, N.I. Giannoccaro, A. Massaro and D. Caratelli, “Prediction and Validation of Outcomes from Air Monitoring Sensors and Networks of Sensors,” V International Conference on Sensing Technology, ICST2011, Palmerston North, New Zealand, 2011. A. Lay-Ekuakille, P. Vergallo, G. Griffo, O. Kanoun, F. Angelillo, and A. Trabacca. "Wireless network acquisition of joint EEG-ECGergospirometric signals for epilepsy detection," Proc. (IEEE) International Workshop on Measurements and Networking Proceedings (M&N-2013), pp. 41-45, 2013. V. Bhateja, R. Verma, R. Mehrotra and S. Urooj, “A Novel Approach for Suppression of Powerline Interference and Impulse Noise in ECG Signals,” Proc. (IEEE) International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT 2013), AMU, Aligarh (U.P.), India, pp. xx, November 2013. V. Bhateja, R. Verma, R. Mehrotra, S. Urooj, A. Lay-Ekuakille, V. D. Verma, “A Composite Wavelets and Morphology Approach for ECG Noise Filtering,” Proc. (Springer) 5th International Conference on Pattern Recognition and Machine Intelligence (PReMI2013), Kolkatta (India), Vol. 8251, pp. 361-366, December 2013. P.Vergallo, A. Lay-Ekuakille, S.Urooj and V. Bhateja, “Spatial Filtering to Detect Brain Sources from EEG Measurements,” Proc. (IEEE) International Symposium on Medical Measurements and Applications, 2014. P.Vergallo, A. Lay-Ekuakille, N. I. Giannoccaro, F. Morabito, A.Trabacca, S.Urooj and V. Bhateja, “Identification of Visual Evoked Potentials in EEG detection by Emprical Mode Decomposition,”Proc. (IEEE) 11th International Multi-Conference on Systems, Signals and Devices- Conference on Sensors, Circuits & Instrumentation Systems, 2014. S. Urooj, M Khan, A Q Ansari, A. Lay-Ekuakille, A K Salhan, “Prediction of Quantitative Intrathoracic Fluid Volume to Diagnose Pulmonary Oedema Using LabVIEW”, Computer Methods in Biomechanics and Biomedical Engineering – pp 1-6, May 2011. A. Lay-Ekuakille, P.Vergallo, G. Griffo, F. Conversano, S. Casciaro, S.Urooj, V. Bhateja and A.Trabacca, “Mutidimensional Analysis of EEG Features using Advanced Spectral Estimates for Diagnosis Accuracy,” Proc. of IEEE International Symposium on Medical Measurements and Applications (MeMeA-2013), Gatineau (Quebec), Canada, pp. 237-240, May 2013. R. Verma, R. Mehrotra and V. Bhateja, “An Integration of Improved Median and Morphological Filtering Techniques for Electrocardiogram Signal Processing," Proc. of (IEEE) 3rd International Advance Computing Conference (IACC 2013), Ghaziabad (U.P.), India, pp. 1223-1228, February 2013. R. Verma, R. Mehrotra and V. Bhateja, “A New Morphological Filtering Algorithm for Pre-Processing of Electrocardiographic Signals," Proc. of (Springer) 4th International Conference on Signal and Image Processing (ICSIP 2012), Coimbatore, India, vol. 1, pp. 193-201, December 2012. R. Verma, R. Mehrotra and V. Bhateja, "An Improved Algorithm for Noise Suppression and Baseline Correction of ECG Signals," Proc. of (Springer) International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA 2012), Bhubaneswar, India, AISC vol. 199, pp. 733-739, December 2012. V. Bhateja, R. Verma, R. Mehrotra and S. Urooj, “A Non-linear Approach to ECG Signal Processing using Morphological Filters,” International Journal of Measurement Technologies and Instrumentation Engineering (IJMTIE), vol. 3, no. 3, pp. 46-59, 2013. A. Lay-Ekuakille, P. Vergallo, G. Griffo, F. Conversano, S. Casciaro, S. Urooj, V. Bhateja and A. Trabacca, “Entropy Index in Quantitative

[16]

[17]

[18]

[19] [20]

[21]

[22]

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

424

EEG Measurement for Diagnosis Accuracy,” IEEE Transactions on Instrumentation & Measurement, vol. x, no. x, pp. xx, November 2013. D. P. Singh, V. Bhateja, S. Soni and A. K. Shukla, “A Novel Cluster Head Selection and Routing Scheme for Wireless Sensor Networks,” Proc. International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2014), Trivandrum (Kerala), India, vol. xx, pp. xx , March 2014. D. P. Singh, V. Bhateja and S. Soni, “Energy Optimization in WSNs employing Rolling Grey Model,” Proc. (IEEE) International Conference on Signal Processing and Integrated Netwroks (SPIN2014), Noida (UP), India, pp. xx, Feb 2014. W. B. Heinzelman, A.P. Chandrakasan & H. Balakrishnan, “ Application-specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, Vol. 1, No. 4, pp. 660-670, 2002. K. Akkaya & M. Younis, “A survey on routing protocols for wireless sensor networks,” Ad hoc networks, Vol. 3, No. 3, pp. 325-349, 2005. O. Younis and S. Fahmy, “Heed: A hybrid, Energy-efficient, Distributed Clustering Approach for Ad-hoc Networks, IEEE Transactions on Mobile Computing, Vol. 3, No. 4, pp. 366-369, OctDec, 2004. Lu Tao, Qing-Xin Zhu and Luqiao. Zhang, “An improvement for LEACH algorithm in wireless sensor network,” in 2010 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 1811-1814, 2010. G. Anastasi, M. Conti, M. Di Francesco & A. Passarella, “Energy conservation in wireless sensor networks: A survey,” Ad Hoc Networks, Vol. 7, No. 3, pp. 537-568, 2009. V. Katiyar, N. Chand & S. Soni, “Grey System Theory-Based Energy Map Construction for Wireless Sensor Networks,” in Advances in Computing and Communications, Springer Berlin Heidelberg, pp. 122-131, 2011. S.K.Soni & D.P.Singh, “Energy Map Construction using Adaptive Alpha Grey Prediction Model in WSNs,” International Journal of Computer and Information Engineering, Vol. 6, No. 3, pp. 107-111, 2012. S.K. Soni, N. Chand & D.P. Singh, “Reducing the data transmission in WSNs using time series prediction model,” in Proc. of 2012 IEEE International Conference on Signal Processing, Computing and Control (ISPCC), pp. 1-5, March, 2012. D.P. Singh, Y. Agrawal & S.K. Soni, “Energy optimization in Zigbee using prediction based shortest path routing algorithm,” in Proc. of 2012 3rd National Conference on Emerging Trends and Applications in Computer Science (NCETACS), pp. 257-261, March, 2012. E. Kayacan, B. Ulutas & O. Kaynak, “Grey system theory-based models in time series prediction,” Expert Systems with Applications, Vol. 37, No. 2, pp. 1784-1789, (2010. U. Kumar & V.K. Jain, “Time series models Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India,” Energy, Vol. 35, No. 4, pp. 1709-1716, 2010. K.L. Wen & T.C. Chang, “The research and development of completed GM (1, 1) model toolbox using Matlab,” International Journal of Computational Cognition, Vol. 3, No. 3, 41, 2005. A. Ray & D. De, “Energy efficient cluster head selection in wireless sensor network,” in Proc. of 2012 1st International Conference on Recent Advances in Information Technology (RAIT), pp. 306-311, March, 2012. V. Bhateja, G. Singh and A. Srivastava, “A Novel Weighted Diffusion Filtering Approach for Speckle Suppression in Ultrasound Images,” Proc. of (Springer) International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA 2013), Bhubaneswar, India, vol. 247, pp. 459-466, November 2013. V. Bhateja, A. Srivastava, G. Singh and J. Singh, “A Modified Speckle Suppression Algorithm for Breast Ultrasound Images Using Directional Filters,” ICT and Critical Infrastructure: Proc.(Springer) of the 48th Annual Convention of Computer Society of India (CSI 2013), Vishakhapatnam, India, vol. II, pp. 219-226, December 2013.


[33] V. Bhateja, S. Urooj, A. Pandey, M. Misra and A. Lay-Ekuakille, “A Polynomial Filtering Model for Enhancement of Mammogram Lesions,” Proc. of IEEE International Symposium on Medical Measurements and Applications (MeMeA-2013), Gatineau (Quebec), Canada, pp. 97-100, May 2013. [34] V. Bhateja, S. Urooj, A. Pandey, M. Misra and A. Lay-Ekuakille, “Improvement of Masses Detection in Digital Mammograms employing Non-Linear Filtering,” Proc. of (IEEE) International Multi-Conference on Automation, Computing, Control, Communication and Compressed Sensing (iMac4s-2013), PalaiKottayam, Kerala (India), pp. 406-408, no. 119, March 2013. [35] H. Srivastava, A. Mishra and V. Bhateja, “Non-Linear Quality Evaluation Index for Mammograms,” Proc. of (IEEE) 2nd Students Conference on Engineering and Systems (SCES-2013), Allahabad, India, pp. 269-273, April 2013. [36] A. Pandey, A. Yadav and V. Bhateja, “Volterra Filter Design for Edge Enhancement of Mammogram Lesions," Proc. of (IEEE) 3rd International Advance Computing Conference (IACC 2013), Ghaziabad (U.P.), India, pp. 1219-1222, February 2013. [37] A. Pandey, A. Yadav and V. Bhateja, “Contrast Improvement of Mammographic Masses Using Adaptive Volterra Filter," Proc. of (Springer) 4th International Conference on Signal and Image Processing (ICSIP 2012), Coimbatore, India, vol. 2, pp. 583-593, December 2012. [38] V. Bhateja, S. Devi and S. Urooj, "An Evaluation of Edge Detection Algorithms for Mammographic Calcifications," Proc. of (Springer) 4th International Conference on Signal and Image Processing (ICSIP 2012), Coimbatore, India, vol. 2, pp. 487-498, December 2012. [39] A. Pandey, A. Yadav and V. Bhateja, "Design of New Volterra Filter for Mammogram Enhancement," Proc. of (Springer) International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA 2012), Bhubaneswar, India, AISC vol. 199, pp. 143-151, December 2012. [40] V. Bhateja and S. Devi, "A Reconstruction Based Measure for Evaluation of Mammogram Edge-Maps," Proc. of (Springer) International Conference on Frontiers in Intelligent Computing Theory and Applications (FICTA 2012), Bhubaneswar, India, AISC vol. 199, pp. 741-746, December 2012. [41] Siddhartha, R. Gupta and V. Bhateja,"A Log-Ratio based Unsharp Masking (UM) Approach for Enhancement of Digital Mammograms," Proc. of (ACM ICPS) CUBE International Information Technology Conference & Exhibition, Pune, India, pp. 26-31, September, 2012. [42] Siddhartha, R. Gupta and V. Bhateja, “An Improved Unsharp Masking Algorithm for Enhancement of Mammographic Masses,” Proc. of IEEE Students Conference on Engineering and Systems (SCES-2012), Allahabad, India, pp. 234-237, March, 2012. [43] Siddhartha, R. Gupta and V. Bhateja, “A New UnSharp Masking Algorithm for Mammography using Non-Linear Enhancement Function,” Proc. of the (Springer) International Conference on Information Systems Design and Intelligent Applications (INDIA 2012), Vishakhapatnam, India, pp. 779-786, January 2012. [44] V. Bhateja and S. Devi, “A Novel Framework for Edge Detection of Microcalcifications using a Non-Linear Enhancement Operator and Morphological Filter,” Proc. of (IEEE) 3rd International Conference on Electronics & Computer Technology (ICECT-2011), Kanyakumari (India), vol. 5, pp. 419-424, April 2011. [45] V. Bhateja and S. Devi, “An Improved Non-Linear Transformation Function for Enhancement of Mammographic Breast Masses,” Proc. of (IEEE) 3rd International Conference on Electronics & Computer Technology (ICECT-2011), Kanyakumari (India), vol. 5, pp. 341346, April 2011. [46] A. Jain, S. Singh and V. Bhateja, “A Robust Approach for Denoising and Enhancement of Mammographic Breast Masses,” International Journal on Convergence Computing, Inderscience Publishers, vol. 1, No. 1, pp. 38-49, 2013. [47] V. Bhateja, M. Misra, S. Urooj and A. Lay-Ekuakille, “A Robust Polynomial Filtering Framework for Mammographic Image Enhancement from Biomedical Sensors,” IEEE Sensors Journal, vol. 13, no. 11, pp. 4147-4156, November 2013.

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