A Distributed Data Storage Method Based on Integrated Threshold

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Abstract:

Based on the analysis and study of the data storage strategy in wireless sensor networks, this paper presents a distributed data storage method based on sleep scheduling to resolve the problems of network imbalance and storage hot spots problems.Finally, multi group analysis of simulate experiments results show that compared to other data storage method the distributed data storage method based on composite threshold have obviously advantages on the sides of overall energy consumption,data storage capacity,the number of failure node and data quality,thus have a significant effect on reducing energy consumption and extending network life cycle.

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Periodical:

Advanced Materials Research (Volumes 268-270)

Pages:

595-600

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Online since:

July 2011

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[1] Philippe Bonnet, Johannes Gehrke and Praveen Seshadri. Towards Sensor Database Systems. Mobile data managemet. 2001, 3-14P.

Google Scholar

[2] Ash Mohammad abbas, Oivind Kure. Quality of Service in mobile ad hoc networks: a survey. International Journal of Ad Hoc and Ubiquitous Computing. 2010, 6(2): 75-98P.

DOI: 10.1504/ijahuc.2010.034322

Google Scholar

[3] Prasanna Padmanabhan, Le Gruenwald, Anita Vallur, Mohammed Atiquzzaman. A survey of data replication techniques for mobile ad hoc network databases. The International Journal on Very Large Data Bases. 2008, 17(5): 1143-1164P.

DOI: 10.1007/s00778-007-0055-0

Google Scholar

[4] Isabel Dietrich, Falko Dressler. On the Lifetime of Wireless Sensor Networks. ACM Transactions on Sensor Networks, 2009, 5(1): 1-39P.

DOI: 10.1145/1464420.1464425

Google Scholar

[5] Michele Albano, Stefano Chessa. Publish/subscribe in wireless sensor networks based on data centric storage. CAMS '09 Proceedings of the 1st International Workshop on Context-Aware Middleware and Services. 2009, 37-42P.

DOI: 10.1145/1554233.1554243

Google Scholar

[6] Zhaochun Yu, Bin Xiao, Shuigeng Zhou. Achieving 0ptimal data storage position in wireless sensor networks. Computer Communications. 2010, 33(1): 92-102P.

DOI: 10.1016/j.comcom.2009.08.005

Google Scholar

[7] S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, S. Shenker. GHT: A geographic hash table for data-centric storage. Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications. 2002: 78-87P.

DOI: 10.1145/570738.570750

Google Scholar

[8] Shenker S, Ratnasamy S, Karp B, Govindan R, Estrin D. Data-Centric storage in sensornets. ACM SIGCOMM Computer Communication Review, 2003, 33(1): 137−142P.

DOI: 10.1145/774763.774785

Google Scholar

[9] D Ganesan, D Estrin, J Heidemann. DIMENSONS: why do we need a new data handling architecture for sensor networks. ACM SIGCOMM Computer Communication Review, 2003, 33 (1): 143-148P.

DOI: 10.1145/774763.774786

Google Scholar

[10] P. Xia, P.K. Chrysanthis, A. Labrinidis. Similarity aware query processing in sensor networks, in: International Workshop on Parallel and Distributed Real-Time Systems (WPDRTS 2007), April, (2006).

DOI: 10.1109/ipdps.2006.1639419

Google Scholar

[11] Mathur G, Desnoyers P, Ganesan D.Ultra-low power data storage for sensor networks. Proc of the 5th Interntional Conference on Information Processing in Sensor Networks.New York: ACM Press, 2006: 374-381P.

DOI: 10.1109/ipsn.2006.243850

Google Scholar

[12] Xing Kai, Cheng Xiu-zhen, Liu Fang.Location-centric storage for safety warning based on roadway sensor networks.Journal of Parallel and Distributed Computing, 2007, 67(3): 336-345P.

DOI: 10.1016/j.jpdc.2006.10.003

Google Scholar