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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

Abstract

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

Open access

Sohini Roy

Abstract

Wireless Sensor Network (WSN) has emerged as an important supplement to the modern wireless communication systems due to its wide range of applications. The recent researches are facing the various challenges of the sensor network more gracefully. However, energy efficiency has still remained a matter of concern for the researches. Meeting the countless security needs, timely data delivery and taking a quick action, efficient route selection and multi-path routing etc. can only be achieved at the cost of energy. Hierarchical routing is more useful in this regard. The proposed algorithm Energy Aware Cluster Based Routing Scheme (EACBRS) aims at conserving energy with the help of hierarchical routing by calculating the optimum number of cluster heads for the network, selecting energy-efficient route to the sink and by offering congestion control. Simulation results prove that EACBRS performs better than existing hierarchical routing algorithms like Distributed Energy-Efficient Clustering (DEEC) algorithm for heterogeneous wireless sensor networks and Energy Efficient Heterogeneous Clustered scheme for Wireless Sensor Network (EEHC).

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A. Alijani, K. Ivaz and S. Mahjoub

References [1] J. N. Al-Karaki and A. E. Kamal.” Routing Techniques in Wireless Sensor Networks: A Survey.” IEEE Wireless Communications, 2004, pp. 6-28.I [2] J. Chang and L. Tassiulas, ”Energy Conserving Routing in Wireless Adhoc Networks”, in Proc. IEEE INFOCOM, 2000. [3] Y. Chen, C. Chauh and Q. Zhao, ”Sensor Placement for Maximing Lifetime per Unit Cost in Wireless Sensor Networks”,in:MILCOM, 2005, pp. 1097- 1102 [4] D. Goldenberg, J. Lin, A. Morse, B. Rosen and Y. Rang, ”Towards

Open access

Umashankar Prasad and D.S. Adane

Abstract

Wireless Sensor Network consist of thousands sensor node which have limited Power, Computation, Sensing and Communication capabilities. Among all operation of Sensor Node, Wireless Communication consumes most of the energy. So it is necessary to decrease the number of packets transmitted through the network. Many Sensor Node could detect similar event, which increases the overall bandwidth utilization to transmit redundant data. Here Nodes computation is cheaper than communication in terms of energy. So the technique of Data Aggregation is applied to summarize data which decreases the amount of data transmitted in the network, which in turn increases the lifetime of the network. Many Data aggregation protocols are based on a structured approach which is suitable for data collection application. But maintenance of the structure is an extra overhead and this approach is not suitable for dynamic scenario. So we propose an ad-hoc data aggregation protocol for dynamic scenario mainly event based application.