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N. B. Khoolenjani and O. Chatrabgoun

. and Whisenand, C. W. (1973). Best linear estimator of the parameter of the Rayleigh distribution-Part I: Small sample theory for censored order statistics. IEEE Transactions on Reliability, 22, 27-34. Gebhardt, J., Gil M.A. and Kruse R., (1998). Fuzzy set-theoretic methods in statistics, in: R. Slowinski(Ed.), Fuzzy Sets in Decision Analysis, Operations Research and Statistics, Kluwer Academic Publishers, Boston, pp.311-347. Huang, H., Zuo, M. and Sun, Z., (2006). Bayesian reliability analysis for fuzzy lifetime data. Fuzzy Sets and Systems, 157

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Adil Rashid, Tariq Rashid Jan, Akhtar Hussain Bhat and Z. Ahmad

References [1] Adamidis, K., and Loukas, S. (1998). A lifetime distribution with decreasing failure rate. Journal of Statistics & Probability Letters, 39, 35-42. [2] Adil, R., Zahoor, A., and Jan, T.R. (2016). A new count data model with application in genetics and ecology. Electronic Journal of Applied Statistical Analysis, 9(1), 213-226 [3] Adil, R., Zahoor, A., and Jan, T.R. (2017). Complementary compound Lindley power series distribution with application. Journal of Reliability and Statistical Studies, 10

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S. Ananda Kumar, P. Ilango and Grover Harsh Dinesh


Many studies have been proposed on clustering protocols for various applications in Wireless Sensor Network (WSN). The main objective of the clustering algorithm is to minimize the energy consumption, deployment of nodes, latency, and fault tolerance in network. In short high reliability, robustness and scalability can be achieved. Clustering techniques are mainly used to extend the lifetime of wireless sensor network. The first and foremost clustering algorithm for wireless sensor network was Low Energy Adaptive Clustering Hierarchy (LEACH). As per LEACH, some Cluster Head (CH) may have more nodes, some other may have less nodes, which affects the network performance. The proposed method MaximuM-LEACH provides a solution by load balancing the number of nodes equally by fixing the average value N, so the life time of the network is increased.

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S. Sankar and P. Srinivasan

. – Wireless Networks, Vol. 22 , 2016, No 3, pp. 945-957. 10. Mokhtar, S., I. Wan, H. Wan, M. N. Norita. Modeling Reservoir Water Release Decision Using Adaptive Neuro Fuzzy Inference System. – Journal of Information & Communication Technology, Vol. 15 , 2016, No 2. 11. Nayak, P., D. Anurag. A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime. – IEEE Sensors Journal, Vol. 16 , 2016, No 1, pp. 137-144. 12. Othman, M., N. F. A. Siti. Deseasonalised Forecasting Model of Rainfall Distribution Using Fuzzy Time Series. – Journal of

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Danping Jia, Ximeng Gao and Chunhua Li

Measurement Based on Fluorescence Lifetime. - Journal of Shenyang University of Technology, Vol. 28, 2006, No 5, 542-545. 10. Danping , Jia , Zhuo Yuan , Wei Lin . Research of DC Current Transformer Based on Optical Fiber Thermometry. - J. Nanoelectronics and Optoelectronics, Vol. 7, 2012, No 2, 11. Q i a n g Wa n g , J i a k u n . Prony Method Implementation Based on MATLAB. - Chinese Science and Technology Information Technology, 2007, No 4, 128-129. 12. Yifeng, Ding, Haozhong Cheng, Ganyun Lv, Yong Zhan, Yibin Sun, Rong Lu. Spectrum

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Cheng Bing Hua, Zhao Wei and Chang Zi Nan

References 1. Pantazis, N. A., S. A. Nikolidakis, D. D. Vergados. Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey. - Communications Surveys & Tutorials, IEEE, Vol. 15, 2013, No 2, 551-591. 2. Aziz, A. A., Y. A. Sekercioglu, P. Fitzpatrick et al. A Survey on Distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor Networks. - Communications Surveys & Tutorials, IEEE, Vol. 15, 2013, No 1, 121-144. 3. Du, H., W. Wu, Q. Ye et al. CDS-Based Virtual

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Yang Wang

: Aerospace Conference Proceedings, 2002. IEEE, Vol. 3, 2002, 1125-1130. 18. Manjeshwar, A., D. P. Agrawa l. TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks. IPDPS, 2001. 19. Boulis, A., M. B. Srivastava. Node-Level Energy Management for Sensor Networks in the Presence of Multiple Applications. - Wireless Networks, Vol. 10, 2004, No 6, 737-746. 20. Abusaime h, H., S. H. Yang. Dynamic Cluster Head for Lifetime Efficiency in WSN. - International Journal of Automation and Computing, Vol. 6, 2009, No 1

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Aleksejs Jurenoks and Leonids Novickis

REFERENCES [1] S. Basagni, A. Carosi, C. Petrioli, “Controlled Vs. Uncontrolled Mobility in Wireless Sensor Networks: Some Performance Insights,” Vehicular Technology Conference , 2007. VTC-2007 Fall. 2007 IEEE 66th. 2007. pp. 269–273. [2] D.M. Blough, P. Santi, “Investigating upper bounds on network lifetime extension for cell-based energy conservation techniques in stationary ad hoc networks,” Proc. of the 8th annual int. conf. on Mobile computing and networking. MobiCom ’02 . New York, NY, USA: ACM, 2002

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R. Balamurali and K. Kathiravan

Technology, Nanjing University, China and Department of Computer Science, City University of Hong Kong, China, 1-4244-0507-6/06/$20.00 ©2006 IEEE, pp. 558-561. 4. Abdulla, A. E. A. A., H. Nishiyama, N. Kato. Extending the Lifetime of Wireless Sensor Networks: A Hybrid Routing Algorithm. – Computer Communications Journal, Vol. 35 , May 2012, No 9, pp. 1056-1063. 5. Perillo, M., Z. Cheng, W. Heinzelman. An Analysis of Strategies for Mitigating the Sensor Network Hot Spot Problem. – In: Proc. of Second International Conference on Mobile and Ubiquitous Systems

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Cunjiang Yu

Application of Ant Colony Optimization in Wireless Sensor Network Routing. – Advanced Materials Research, Vol. 5 , May 2013, pp. 838-841. 12. Zhang, J. Y., D. Y. Chen. Clustering Routing Algorithm Ant Colony Optimization-Based for Wireless Sensor Network. – Applied Mechanics and Materials, Vol. 58 , April 2015, pp. 591-597. 13. Zhong, J. H. Ant Colony Optimization Algorithm for Lifetime Maximization in Wireless Sensor Network with Mobile Sink. – In: Proc. of 14th International Conference on Genetic and Evolutionary Computation, 2012, GECCO’12, pp. 1199