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Comparative Study of Classification Techniques with Labeled Data in Wireless Sensor Network
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 69 - Number 11 |
| Year of Publication: 2013 |
| Authors: Bhawana Parbat, R. K. Dhuware |
10.5120/11888-7928
|
Bhawana Parbat, R. K. Dhuware . Comparative Study of Classification Techniques with Labeled Data in Wireless Sensor Network. International Journal of Computer Applications. 69, 11 ( May 2013), 27-31. DOI=10.5120/11888-7928
Abstract
The wireless sensor nodes are getting smaller, but Wireless Sensor Networks (WSNs) are getting larger with the technological developments, currently containing thousands of nodes and possibly millions of nodes in the future. To deal with the large volume of data produced by these special kinds of wireless networks, one approach is use of Data Mining techniques. Classification is an important task in data mining. Classification of sensory data is a major research problem in WSNs and it can be widely used in reducing the data transmission in WSNs effectively and also in process monitoring. In this paper, Labelled Wireless Sensor Network Data is used for mining. This multihop data consist of humidity and temperature measurements. To mine the sensor data three classification techniques J48(Decision Tree), Naive Bayes, and ZeroR are considered in this study. Experimental investigation yields a significant output in terms of the correctly classified instances. At the end it has been found that Naïve Bayes is a suitable method to classify the large amount of data considered is made finally according to the mining result.
References
- T. Clouqueur, V. Phipatanasuphorn, P. Ramanathan, and K. K. Saluja 2002 Sensor deployment strategy for target detection. In Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, Atlanta, Georgia, USA, pp. 42–48.
- G. Simon et al. , 2004. Sensor network-based counter sniper system. In Proceedings of the 2nd International conference on Embedded networked sensor systems, Baltimore, MD, USA, pp. 1– 12.
- P. Zhang, C. M. Sadler, S. A. Lyon, and M. Martonosi 2004 Hardware design experiences in ZebraNet," in Proceedings of the 2nd international conference on Embedded networked sensor systems, Baltimore, MD, USA, pp. 227–238.
- G. Werner-Allen et al. 2006 Deploying a wireless sensor network on an active volcano, IEEE Internet Computing, vol. 10, no. 2 (Apr. 2006), pp. 18- 25.
- Amir Akhavan Kharazian1, Kamal Jamshidi and Mohammad Reza Khayyambashi "Adaptive Clustering In Wireless Sensor Network: Considering Nodes With Lowest energy", International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC) Vol. 3, No. 2, April 2012.
- Jiawei Han and MichelineKamber 2006 Data Mining Concepts and Techniques. San Francisco, CA: Elsevier Inc,.
- Tia Gao, D. Greenspan, M. Welsh, R. Juang, and A. Alm, 2006 Vital Signs Monitoring and Patient Tracking Over a Wireless Network. In Engineering in Medicine and Biology Society,. IEEEEMBS 2005. 27th Annual International Conference of the, 2006, pp. 102-105.
- K. Lorincz et al. 2004 Sensor networks for emergency response: challenges and opportunities. IEEE Pervasive Computing, vol. 3, no. 4 (Dec. 2004), pp. 16- 23.
- M. Demirbas, X. Lu, and P. Singla, 2009 An In-Network Querying Framework for Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, Vol. 20, No. 8 (August 2009).
- C. Zhang, C. Wang, D. Li, X. Zhou, C. Gad 2009 Unspecific Event Detection in Wireless Sensor Networks. International Conference on Communication Software and Networks, Shanghai, China (February 2009) pp. 243-246.
- Khushboo Sharma, Manisha Rajpoot, Lokesh Kumar Sharma, "Nearest Neighbour Classification for Wireless Sensor Network Data", International Journal of Computer Trends and Technology- volume2Issue2- 2011, pp. 41-43.
- Y. Gao, E. Tumwesigye, L. Allan, B. Cahill, K. Menze 2010 Using Data Mining in Optimisation of Building Energy Consumption and Thermal Comfort Management. In Proc. 2nd International Conference on Software Engineering and Data Mining, IEEE,.
- Maria Muntean, HonoriuV?lean, Adrian Tulbure, IoanIlean?, Manuella Kadar, "Data mining algorithms for wireless sensor network's data", Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies V, edited by Paul Schiopu, George Caruntu, Proc. of SPIE Vol. 7821, 78212G © 2010 SPIE • CCC code: 0277-786X/10/$18 • doi: 10. 1117/12. 882215, Proc. of SPIE Vol. 7821 78212G-1.
- P. Garrity, S. Bhattacharyya, C. Shen, D. Dawadi, B. Panja Vibration Monitoring And Analysis Using A Wireless Sensor Network (Wsn) To Classify Vehicle. http://patrickgarrity. com/files/VIBRATION_MONITORING_AND_ANALYSIS_USING_A_WSN_TO_CLASSIFY_VEHICLES_FINAL. pdf
- C. Nadal, R. Legault, and C. Y. Suen, 1990 Complementary algorithms for the recognition of totally uncontrained handwritten numerals. In Proceedings of the 10th International Conference on Pattern Recognition, volume A (June 1990) , pages 434–449.
- Shan Suthaharan, Mohammed Alzahrani, Sutharshan Rajasegarar, Christopher Leckie and Marimuthu Palaniswami 2010 Labelled Data Collection for Anomaly Detection in Wireless Sensor Networks. In Proceedings of the Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2010), Brisbane, Australia, (Dec 2010).
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