Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic


eISSN: 1178-5608



VOLUME 9 , ISSUE 3 (September 2016) > List of articles


LUO Qing-Yun * / ZHU Ling-Zhi * / CHAGN Yun-Jie * / ZHAO Jin-Guo / LIAO Wei-Sheng / HE Rui

Keywords : sensor networks, information processing cloud, data storage, data query

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 3, Pages 1,614-1,636, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 03-May-2016 / Accepted: 30-July-2016 / Published Online: 01-September-2016



Sensor network is a data-centric network, which provides data collection, storage and query services. Data storage and query is one of the hot spot in the research of sensor networks. In order to solve the problem of low efficiency of storage and query,high energy consumption in sensor networks, we put forward a scheme that storing distributed data of wireless sensor network based on information processing cloud. Information processing cloud is made up of a group of sensor nodes around the network center, which have the ability to absorb and process data from other nodes of sensor network which do not belong to the information processing cloud. The group of sensor nodes around the network center respond data query requests from anywhere of the network, and sensor nodes can be adjusted dynamically according to real situation, the cloud of nodes and non-cloud of nodes can be dynamically transformed as well. When non-cloud of sensor nodes store data or send query request, they only need to do centripetal movement, centrifugal movement or circumfusing movement. The analysis shows that the proposed scheme can simplify route algorithm of data storage and data query, and it also has less computation cost and storage cost than the existing schemes.

Content not available PDF Share



[1] Le H C, Guyennet H, Zerhouni N. “Mobile effect reduction in data-centric storage for wireless sensor networks”, 3rd IET International Conference on Intelligent Environments (IE 07). IET Digital Library, 2007, pp: 304-311.
[2] Shen HY, Li T, Schweiger T. “An Efficient Similarity Searching Scheme Based on Locality Sensitive Hashing”, In: Proceedings of Int’l Conf. Database Theory (ICDT 2008). Piscataway: IEEE Press, 2008. pp: 123−128.
[3] Lin Y, Liang B, Li B. “Data Persistence in Large-Scale Sensor Networks with Decentralized Fountain Codes”, Proceedings - IEEE INFOCOM, 2007, pp: 1658-1666.
[4] Akyildiz I F, Su W, Sankarasubramaniam Y, et al. “A survey on sensor networks”, Communications Magazine IEEE, vol.40, No.8, 2002, pp.102-114.
[5] Yu G J. “Adaptive storage policy switching for wireless sensor networks”, Wireless Personal Communications, vol.48, No.3, 2009, pp: 327-346.
[6] Li J, GAO H. “Survey on Sensor Network Research”, Journal of Computer Research & Development, vol.1, No.45, 2008, pp.1-15.
[7] Zhang Q, Xie Z P, Ling B, et al. “A Maximum Lifetime Data Gathering Algorithm for Wireless Sensor Networks”, Journal of Software, vol.11, No.16, 2005, pp.1946-1957.
[8] Gong H, Liu M, Wang X, et al. EADEEG: “An Energy-Aware Data Gathering Protocol for Wireless Sensor Networks”, Journal of Software, vol.5, No.18, 2007.pp:1092-1109.
[9] Liang J B, Wang J X, Tao-Shen L I, et al. “Maximum Lifetime Algorithm for Precise Data Gathering Based on Tree in Wireless Sensor Networks”, Journal of Software, vol.9, No.21, 2010. pp: 2289-2303.
[10] X. Li, Y. J. Kim, R. Goninan and W. Hong. “Multi-dimensional Range Queries in Sensor Networks”, International Conference on Embedded Networked Sensor Systems. 2004, pp: 63-75.
[11] L. Xie, L. J. Chen, D. X. Chen. “Ring-Based multi-resolution data storage for sensor networks”, Journal of Software, vol.12, no.20, 2009, pp: 3163-3178.
[12] J. L. Xu, X. Y. Tang, and W. C. Lee, “A New Storage Scheme for Approximate Location Queries in Object-Tracking Sensor Networks”, IEEE Transactions on Parallel & Distributed Systems, vol.2, no.19, 2008, pp: 262-275.
[13] C. Y. Ai, R. Y. Du, M. H. Zhang and Y. S. Li, “In-Network Historical Data Storage and Query Processing Based on Distributed Indexing Techniques in Wireless Sensor Networks”, Wireless Algorithms, Systems, and Applications, International Conference, WASA 2009, Boston, Ma, USA, August 16-18, 2009, pp:264-273.
[14] L. Q. Pan, J. Z. Li, J. Z. Luo. “Approximate Skyline Query Processing Algorithm in wireless sensor networks”. Journal of Software, vol.5, no.21, 2010, pp: 1020-1030.
[15] D. Braginsky and D. Estrin. “Rumor routing algorithm for sensor networks”, First ACM International workshop on wireless sensor networks & applications, 2002, pp: 22-31.
[16] X. Liu, Q. F. Huang and Y. Zhang, “Balancing Push and Pull for Efficient Information Discovery in Large-Scale Sensor Networks”, IEEE Transactions on Mobile Computing, vol.3, no.6, 2007, pp: 241-251.
[17] Z. C. Yu, B. Xiao and S. G. “Zhou Achieving optimal data storage position in wireless sensor networks”, Computer Communications, vol.1, No.33, 2010, pp: 92-102.
[18] Xu J, Qian H, Ying W, et al. “A deployment algorithm for mobile wireless sensor networks based on the electrostatic field theory”, The International Journal on Smart Sensing and Intelligent Systems, Vol.8, No.1, 2015, pp. 516-537.
[19] Bai, Q., & Jin, C, “Image fusion and recognition based on compressed sensing theory”, International Journal on Smart Sensing & Intelligent Systems, Vol.8, No.1, 2015, pp. 159-180.
[20] Qiao J, Liu S, Qi X, et al, “Transmission power control in wireless sensor networks under the minimum connected average node degree constraint”, The International Journal on Smart Sensing and Intelligent Systems, Vol.8, No. 1, 2015, pp.801-821.