iosr-JCE   Volume-19 ~ Issue-6 ~ Nov-Dec 2017

Abstract: It has been observed that coverage is a basic issue in remote sensor organize (WSN).This issue can be elucidated by survey the less number of the handle vivacious at the specific time. The sensors from the dynamic groups defines all objectives and at danger to transmit the information to the base station and remaining center points are in rest mode. This paper discusses a couple of critical computations - Modified K-Means, PSO figuring and AOMDV utilized as a bit of remote sensor systems. The Modified K-Means is utilized to scope the range, AOMDV used for multipath coordinating and PSO is utilized to locate the perfect way.

[1] Aneesh Kumar V.N., AyanaAjith, "Hole and Border Detection Methods and Coverage Enhancement in WSN: A Survey", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 5, Issue 6, June 2015 ISSN: 2277 128X,
[2] Feng Li, Jun Luo, "LAACAD: Load balancing k-Area Coverage through Autonomous Deployment in Wireless Sensor Networks", Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference, 21 June 2012, DOI: 10.1109/ICDCS.2012.34,
[3] A. Capone, M. Cesana, D. De Donno, I. Filippini, "Optimal Placement of Multiple Interconnected Gateways in Heterogeneous Wireless Sensor Networks", International Conference on Research in Networking 2009, DOI: 10.1007/978-3-642-01399-7_35,
[4] KoenLangendoen ,NielsReijers, "Distributed localization in wireless sensor networks: a quantitative comparison", Elsevier Journal Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Wireless sensor networks, Volume 43 Issue 4, 15 November 2003, doi>10.1016/S1389-1286(03)00356-6,

Abstract: In present scenario where network traffic is so high due to which speed and efficiency of the network also reduce. Network traffic also leads to delay in time as many nodes energies got reduced in span of time. So in this research paper we proposed a improved leach protocol which is improvement over the energy efficient routing protocol low energy adaptive clustering hierarchy (LEACH) for wireless sensor networks. We use TDMA concept here for communication between cluster head and nodes. In improved leach here the transmission takes place from hop to hop rather than direct transmission from cluster head to base station that takes place in leach protocol. So when we compare these protocol we find that multi hop leach protocol is better than energy efficient routing protocol low energy adaptive clustering hierarchy (LEACH) for wireless network

Keywords: Leach, Cluster Head, Base Station, Nodes

[1] Poonam Tyagi, Ravinder Prakash Gupta, Rakesh Kumar Gill,"Comparative Analysis of Cluster Based Routing Protocols used in Heterogeneous Wireless Sensor Network", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, vol. 1, no. 5, pp. 2231-2307, Nov. 2014
[2] Xu, G. Yang, Z.-Yu. Chen, L. Chen, and Z. Yang, "Performance analysis of data aggregation algorithms in wireless sensor networks," in Proceedings of the International Conference on Electrical and Control Engineering (ICECE '11), September 2013.
[3] Akyildiz and M. C. Vuran, Wireless Sensor Networks,University Publishers, Tsinghua, China, 2011
[4] Nam and H. Min, "An Energy-Efficient Clustering Using a Round-Robin Method in a Wireless Sensor Network".5th ACIS International Conference on Software Engineering Research, Management & Applications, 2013, pp. 54-60.
[5] N.M.A. Latiff, C.C. Tsimenidis, and B.S. Sharif, "Performance Comparison of Optimization Algorithm for Clustering in Wireless Sensor Networks," IEEE International Conference on Mobile Adhoc and Sensor Systems, 2010, pp. 1-4.

Abstract: Clustering is a technique to analyze the data in efficient manner and generate required information. To cluster the dataset, the technique of k-medoid is applied which is a partitioning based method. It uses k as a parameter, divide n objects into k clusters. In K-medoid clustering data points are used as the centers of the clusters. Here, we will work on prediction analysis in which final clustered data will be analyzed and evaluated according to the requirement. In order to analyze the clusters the relationship among the attributes of the dataset must be known which is a difficult task in case of complex dataset is. In this work, a technique will be proposed using back propagation to derive relationship among the attributes of the complex dataset for better analysis of clustered data. This technique will lead to the improvement in accuracy, precision, F-measure and recall of the clusters.

Keywords: Clustering, K-medoid clustering, back Propagation, Diabetes, Prediction Analysis

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