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Modern Query Optimization Technique (Nature Inspired) for Improving Energy Efficient Data Gathering and Processing in Wireless Sensor Networks

by Komalavalli Chakravarthi, Vijay Bhushan Aggarwal
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 132 - Number 2
Year of Publication: 2015
Authors: Komalavalli Chakravarthi, Vijay Bhushan Aggarwal
10.5120/ijca2015907291

Komalavalli Chakravarthi, Vijay Bhushan Aggarwal . Modern Query Optimization Technique (Nature Inspired) for Improving Energy Efficient Data Gathering and Processing in Wireless Sensor Networks. International Journal of Computer Applications. 132, 2 ( December 2015), 24-30. DOI=10.5120/ijca2015907291

@article{ 10.5120/ijca2015907291,
author = { Komalavalli Chakravarthi, Vijay Bhushan Aggarwal },
title = { Modern Query Optimization Technique (Nature Inspired) for Improving Energy Efficient Data Gathering and Processing in Wireless Sensor Networks },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 132 },
number = { 2 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume132/number2/23567-2015907291/ },
doi = { 10.5120/ijca2015907291 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:28:05.447059+05:30
%A Komalavalli Chakravarthi
%A Vijay Bhushan Aggarwal
%T Modern Query Optimization Technique (Nature Inspired) for Improving Energy Efficient Data Gathering and Processing in Wireless Sensor Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 132
%N 2
%P 24-30
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the main obstacles in wireless sensor networks is limited energy and memory constraints. In wireless sensor network, communication among the nodes is the most energy consuming activity and needs to be minimized in terms of data size and number of communications. To overcome these obstacles the earlier research works introduced several techniques and methods for query optimization. Data aggregation, efficient routing, secured data transmission, secured communication, storage maintenance and various heuristics are some of the approaches introduced earlier. The main objective of this work is to accomplish better efficiency in terms of energy and data storage maintenance. To achieve this goal, a Modern Query Optimization [MQO] technique is proposed in this paper. MQO works at three levels namely (i) Monitoring the query and the path (ii) Tracking the data along with the node information and (iii) Choosing optimum nodes in a best path for data gathering based on query’s lifetime. Artificial Immune System [6] algorithm along with the proposed three modifications is used to optimize the queries. The authors have simulated this modified algorithm in MatLab and the results show enhancement in efficiency.

References
  1. Divyakant Agrawal,Amr El Abbadi Lin Qiao, "Supporting Sliding Window Queries for Continuous Data Streams," in 15th International Conference on Scitific and Stastical Database management, 2003.
  2. J. J. P. C. Rodrigues L. M. L. Oliveira, "Wireless Sensor Networks: a Survey on Environmental Monitoring," Journal of Communications (JCM), vol. 6, no. 2, pp. pp. 143-151, April 2011, DOI: 10.4304/jcm.6.2.
  3. Amir Massoud Bidgoli, Mohammad Hossein Yektaie Arash Nikdel, "A new Scheduling Mechanism Inspired of Artificial Immune System Algorithm for Wireless Sensor Networks," International Journal of Smart Home, vol. 5, no. 4, October 2011.
  4. Johannes, Gehrke Yong Yao, "Cougar Approach to In-Network Query Processing in Sensor Networks," Sigmod Record, vol. 31, no. 3, September 2002.
  5. Joel J. P. C. Rodrigues,Mbaye Sene, Jaime Lloret Ousmane Diallo, "Distributed Database Management Techniques for Wireless Sensor Networks," IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2013.
  6. Imane Aly Saroit, Hesham El-Mahdy, Eid Emary Marwa Sharawi, "Routing wireless sensor networks based on Soft computing paradigms: survey," International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI , vol. 2, no. 4, August 2013.
  7. Sharmila Devi Vaidehi V, "Distributed Database Management and Join of Multiple Data Streams in Wireless Sensor Network using Querying Techniques," in IEEE-International Conference on Recent Trends in Information Technology, 2011.
  8. Gondal, I., Kamruzzaman J. Rashid M, "Share-Frequent Sensor Patterns Mining from Wireless Sensor Network Data," IEEE Transactions on Parallel and Distributed Systems, vol. PP, no. 99, 2014.
  9. Giri C. Chowdhury S., "Data collection point based mobile data gathering scheme with relay hop constraint ," in Advances in Computing, Communications and Informatics (ICACCI) , Mysore, 2013, pp. 282 - 287.
  10. Jaime Lloret, Miguel Garcia and Jose F. Toledo S. Sendra, "Power saving and energy optimization techniques for Wireless Sensor Networks," Journal of Communications, vol. 6, no. 6, pp. Pp. 439-459, August 2011. doi:10.4304/jcm.6.6.439-459.
  11. Yong Yao, Alan Demers, Johannes Gehrke, Rajmohan Rajaraman Niki Trigoni, "Multi-query Optimization for Sensor Networks," in Distributed Computing in Sensor Systems.: Springer Berlin Heidelberg, 2005.
  12. L.D. Chagas and Univ. do Estado do Rio Grande do Norte, Lima E.P, Neto, P.F.R. Dept. de Inf., "Real-Time Databases Techniques in Wireless Sensor Networks ," in Networking and Services (ICNS), 2010, pp. 182 - 187.
  13. A. Lopes, D. Meira, R. Vasconcelos, and R. Menezes Brayner, "An adaptive in-network aggregation operator for query processing in wireless sensor networks," The Journal of Systems and Software , vol. 81, no. 3, pp. 328–342, 2008.
  14. Lars Kulik, EgemenTanin, Muhammad Umer, "Optimizing query processing using selectivity-awareness in Wireless Sensor Networks," Computers, Environment and Urban Systems, vol. 33, pp. 79–89, 2009.
  15. Feifei Li, George Kollios, John Byers Jeffrey Considine, "Approximate aggregation techniques for sensor databases," in 20th International Conference on Data Engineering, 2004, pp. 449-460.
Index Terms

Computer Science
Information Sciences

Keywords

Wireless Sensor Network Cluster Head Energy Artificial Immune System Multiquery Optimization

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