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  Implementation of Data Mining in Wireless Sensor Networks: An Integrated Review  
  Authors : Mohd Muntjir
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Current years have witnessed the emergence of wireless sensor networks (WSNs) as a new informationgathering paradigm, in which a large number of sensors scatter over a examination field and extract data of interests by reading real-world phenomena from the physical environment. Energy consumption becomes a primary concern in a WSN, as it is crucial for the network to functionally operate for an probable period of time. The WSN’s extraordinary characteristics direct us to innovative research challenges in some data mining process. Data mining is one of the most important methods by which useful patterns in data with minimal user interference are known and available information of users and analysts to make decisions relayed on their vital organizations to adopt. Data mining, as the continuance of multiple intertwined disciplines, consisting statistics, machine learning, pattern recognition, database systems, information retrieval, World- Wide Web, visualization, and lots of application domains, has made great progress in the past decade. To ensure that the advances of data mining research and technology will competently benefit the progress of science and engineering, it is important to scrutinize the challenges on data mining posed in data-intensive science and engineering and explore how to further develop the technology to assist new discoveries and advances in science and engineering. In WSNs, hierarchical network structures have the advantage of supplying scalable and energy efficient solutions.


Published In : IJCSN Journal Volume 5, Issue 4

Date of Publication : August 2016

Pages : 692-698

Figures :03

Tables : 01


Mohd Muntjir : is a Research Scholar in OPJS University .He has received his B.Sc in Mathematics from Choudhary Charan Singh University Meerut U.P. in 2002 and M.C.A. degree in Computer Science from Hemawati Nandan Bahuguna Garhwal University Srinagar Garhwal Uttarakhand in 2007. His research interests are Wireless Sensor Network, Data Mining, DBMS, Cloud Computing, ECommerce and Multimedia Technology.








WSN, Partitioned WSN, Scheduling in WSN, Location-Based Scheduling, Data Mining

Wireless sensor network technology has the prospective to enable main breakthroughs in the natural sciences by giving scientists the potential to collect high-fidelity data over large geographic regions and extended periods of time. In WSNs, since the sensor nodes are energy constrained and have limited lifetime, energy consumption of sensor nodes becomes as a major issue. Two main approaches: 1) clustering-based: sensor nodes form clusters and elect the cluster heads in such a way to improve energy efficiency, and 2) prediction based: energy-aware prediction is used to find the slight trade-off among communication and prediction cost. Via performance evaluation, it is shown that it achieves energy efficiency even though the object arrived from any random location and moves randomly. K-Means Data Relay (K-MDR) clustering algorithm for WSN reduces the communication overhead and increases the entire network life time by reducing the number of transmission between every sensor node to sink. The K-Means Data Relay algorithm decreases the computational time and improves the performance of the network when compared to K-Means algorithm. Hybrid moving based scheduling strategy for data collection process is improved lifetime of the network. For large coverage area and more number of nodes failure in the network, multiple mobile robots used for separate partition in order to collect data.


[1] V. Karthik,”Region Based Scheduling With Multiple Mobile Robots for Data Collection Strategies in Wireless Sensor Networks”, Vol. 2, Issue 7, July 2013 [2] M. Vijayalakshmi , V. Vanitha,”CLUSTER BASED ADAPTIVE PREDICTION SCHEME FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS”, Vol 04; Special Issue; June 2013 [3] Sherin Mathew, S. Nandhini,” A Novel Approach for Sensory Data Collection in Wireless Sensor Networks with Mobile Sinks”, Volume 2, Issue 2, March 2013 [4] Laxmi Choudhary,” CHALLENGES FOR DATA MINING”, Volume 2, Issue 2 (February 2012) [5] S. Nithyakalyani and S. Suresh Kumar,”Data Relay Clustering Algorithm for Wireless Sensor Networks: A Data Mining Approach”,2012 [6] Rouhollah Maghsoudi,, Somayye Hoseini , Yaghub Heidari,” Surveying Robot Routing Algorithms with Data Mining Approach”, Vol .2 No.2 (2011) 284-294 [7] Onkur Tekdar and Volkan Isler University of Minnesota Jong Hyun Lim and Andreas Terzis Johns Hopkins University,”Using Mobile Robots To Harvest Data From Sensor Fields”,2009 [8] R.Sivaranjini, E.Dinesh,”An Adaptive scheme for energy consumption and data collection in Wireless Sensor Networks”, Volume 2 Issue 3 ? March. 2013 ? PP.50-55 [9] Miao Zhao, Member, IEEE, and Yuanyuan Yang, Fellow, IEEE,” Bounded Relay Hop Mobile Data Gathering in Wireless Sensor Networks”, Vol. 61, No. 2, Febuary 2012 [10] Doug Alexander,Data Mining,” http://www.laits.utexas.edu/~anorman/BUS.FOR/cour se.mat/Alex/” [11] Khushboo Sharma, Manisha Rajpoot, Lokesh Kumar Sharma, Department of Computer Science and Engineering, Rungta College of Engineering and Technology, Kohka Road, Kurud, Bhilai, India. [12] Tzung-Cheng Chen, Tzung-Shi Chen, Member, IEEE, and Ping-Wen Wu. [13] Bo Yuan, Member, IEEE, Maria Orlowska, and Shazia Sadiq, School of Information Technology and Electrical Engineering, The University of Queensland, QLD 4072, Australia, E-mail: {boyuan, maria, shazia}@itee.uq.edu.au. [14] Marcelo B. Soares, Mario F. M. Campos, Dimas A. Dutra,V´_ctor C. da S. Campos and Guilherme A. S. Pereira.