A New Fuzzy Query Processing System in Wireless Sensor Networks

Parand Akhlaghi


The task of acquiring information from sensor networks through generating queries is one of the most important issues in wireless sensor networks. The structure of traditional query processing systems requires defining query criteria in the form of crisp predicates with explicit and numerical thresholds, leading them to be processed in a certain manner. The inherent uncertainty and imprecision of sensor data call for a new approach towards them. Since fuzzy theory provides a toolbox to capture the imprecision associated with both data and query, in this paper, a new system for processing fuzzy queries in wireless sensor networks is introduced. In this system, in addition to presenting a new structure for fuzzy queries, a new algorithm is introduced for processing fuzzy queries in sensor networks. Simulation results indicate that accuracy and precision of the results obtained from fuzzy queries are higher than traditional ones, whereas there is no significant difference between the two regarding their energy consumption.


Wireless sensor networks; query processing; in-network processing; flexible processing; fuzzy query; fuzzy proposition; is-predicate; correlation index

Full Text:



S. R. Madden, M. J. Franklin, J. M. Hellerstein and W.Hong (2005) TinyDB: An acquisitional query processing system for sensor networks, ACM Transactions on Database Systems (TODS), ACM. Vol. 30, No. 1, pp. 122-173.

Y. Yao and J. Gehrke (2002) The cougar approach to in-network query processing in sensor networks, ACM Sigmod Record, Vol. 31, No. 3, pp.9-18.

L. A. Zadeh (1965) Fuzzy sets, Information and control, Elsevier, Vol. 8, No.3, pp. 338-353.

S. Madden, M. J. Franklin, J. M. Hellerstein and W. Hong (2003) The design of an acquisitional query processor for sensor networks, In SIGMOD 2003: Proceedings of the 2003 ACM SIGMOD international conference on Management of data. San Diego, California, USA, ACM. pp. 491-502.

A. Deshpande, C. Guestrin, W. Hong and S. Madden (2005) Exploiting correlated attributes in acquisitional query processing, In ICDE 2005: Proceedings of the 21st International Conference on Data Engineering, 2005, Tokyo, Japan, IEEE, pp. 143-154.

A. Deshpande, C.Guestrin, S. R. Madden, J. M. Hellerstein and W. Hong (2004) Model-driven data acquisition in sensor networks, In VLDB '04: Proceedings of the Thirtieth international conference on Very large data bases-Volume 30. Toronto, Canada, VLDB Endowment, pp.588-599.

D. Chu, A. Deshpande, J. M. Hellerstein and W. Hong (2006) Approximate data collection in sensor networks using probabilistic models, In ICDE'06: Proceedings of the 22nd International Conference on Data Engineering, 2006, Atlanta, GA, USA, IEEE, pp.48-59.

A. Jain, E. Y. Chang, and Y.-F. Wang (2007) Bayesian reasoning for sensor group-queries and diagnosis, Advances in Databases: Concepts, Systems and Applications. Lecture Notes in Computer Science. Vol. 4443, Springer. pp. 522-538.

Q. Ren, and Q. Liang (2007) Energy and quality aware query processing in wireless sensor database systems, Information Sciences, Elsevier ,Vol. 177, No.10, pp.2188-2205.

B. Bostan-Korpeoglu, A. Yazici , I. Korpeoglu and R. George (2006) A New Approach for Information Processing in Wireless Sensor Network, In ICDEW'06: Proceedings of 22nd International Conference on Data Engineering Workshops, 2006, Atlanta, GA, USA, IEEE, pp.34-40.

M. Doman, J. Payton and T. Dahlberg, (2010) Leveraging fuzzy query processing to support applications in wireless sensor networks, In SAC10: Proceedings of the 25th ACM Symposium on Applied Computing, 2010. Sierre, Switzerland, ACM, pp. 764-771.

R. Mueller, G. Alonso and D. Kossmann (2007) SwissQM: Next Generation Data Processing in Sensor Networks, In CIDR 2007: Third Biennial Conference on Innovative Data Systems Research, 2007, Asilomar, CA, USA. pp. 1-9.

M. Doman (2009). The design and implementation of fuzzy query processing on sensor networks, (Doctoral dissertation, University of North Carolina at Charlotte).

J. G. Shanahan (2012). Soft computing for knowledge discovery: introducing Cartesian granule features (Vol. 570). Springer Science & Business Media.

Q. Ren and Q. Liang (2007, March). A VSM-Based and Quality-Aware Query Processing Protocol for Wireless Sensor Networks. In Wireless Communications and Networking Conference, 2007. WCNC 2007. IEEE (pp. 2648-2652). IEEE.

G. Klir and B. Yuan (1995). Fuzzy sets and fuzzy logic: Theory and Applications. (Vol. 4). New Jersey: Prentice Hall.

M. De Cock, (1999). Representing the adverb very in fuzzy set theory. In Proceedings of the ESSLLI99 Student Session 11th European Summer School in Logic, Language and Information.

A. Boulis, Castalia. [Online]. Available: http://castalia.npc.nicta.com.au/index.php.

OMNET++ discrete event simulator. [Online]. Available: http://www.omnetpp.org.

T. Van Dam and K. Langendoen, (2003). An adaptive energy-efficient MAC protocol for wireless sensor networks, In ACM SenSys '03: Proceedings of the 1st international conference on Embedded networked sensor systems 2003, Los Angeles, CA, USA. ACM. pp. 171-180.

A. Pandya and M. Mehta, (2012). Performance Evaluation of Multipath Ring Routing Protocol for Wireless Sensor Network, UACEE International Journal of Advances in Computer Networks and its Security, Vol. 2, No. 2, pp. 53-58.

J. Galindo (2006). Fuzzy databases: Modeling, design and implementation, IGI Global.

Z. M. Ma and L. Yan, (2007). Generalization of strategies for fuzzy query translation in classical relational databases. Information and Software Technology, 49(2), 172-180.

L. A. Zadeh (1975). The concept of a linguistic variable and its application to approximate reasoningI. Information sciences, 8(3), 199-249.


  • There are currently no refbacks.





About IJSBAR | Privacy PolicyTerms & Conditions | Contact Us | DisclaimerFAQs 

IJSBAR is published by (GSSRR).