Continuous Probabilistic Count Queries in Wireless Sensor Networks

Count queries in wireless sensor networks (WSNs) report the number of sensor nodes whose measured values satisfy a given predicate. However, measurements in WSNs are typically imprecise due, for instance, to limited accuracy of the sensor hardware. In this context, we present four algorithms for computing continuous probabilistic count queries on a WSN, i.e., given a query Q we compute a probability distribution over the number of sensors satisfying Q's predicate. These algorithms aim at maximizing the lifetime of the sensors by minimizing the communication overhead and data processing cost. Our performance evaluation shows that by using a distributed and incremental approach we are able to reduce the number of message transfers within the WSN by up to a factor of 5 when compared to a straightforward centralized algorithm.

[1]  Hans-Peter Kriegel,et al.  Probabilistic Similarity Join on Uncertain Data , 2006, DASFAA.

[2]  Jennifer Widom,et al.  Working Models for Uncertain Data , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[3]  Dan Olteanu,et al.  $${10^{(10^{6})}}$$ worlds and beyond: efficient representation and processing of incomplete information , 2006, 2007 IEEE 23rd International Conference on Data Engineering.

[4]  Sunil Prabhakar,et al.  Evaluating probabilistic queries over imprecise data , 2003, SIGMOD '03.

[5]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[6]  Dan Suciu,et al.  Efficient query evaluation on probabilistic databases , 2004, The VLDB Journal.

[7]  Jeffrey Scott Vitter,et al.  Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data , 2004, VLDB.

[8]  Hans-Peter Kriegel,et al.  Scalable Probabilistic Similarity Ranking in Uncertain Databases , 2010, IEEE Transactions on Knowledge and Data Engineering.

[9]  Kenneth Lange,et al.  Numerical analysis for statisticians , 1999 .

[10]  Jian Pei,et al.  Ranking queries on uncertain data: a probabilistic threshold approach , 2008, SIGMOD Conference.

[11]  Saul A. Kripke,et al.  Semantical Analysis of Modal Logic I Normal Modal Propositional Calculi , 1963 .

[12]  Feifei Li,et al.  Efficient Processing of Top-k Queries in Uncertain Databases with x-Relations , 2008, IEEE Transactions on Knowledge and Data Engineering.

[13]  Mario A. Nascimento,et al.  Exact Top-K Queries in Wireless Sensor Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[14]  Feifei Li,et al.  Semantics of Ranking Queries for Probabilistic Data and Expected Ranks , 2009, 2009 IEEE 25th International Conference on Data Engineering.

[15]  Robert B. Ross,et al.  Aggregate operators in probabilistic databases , 2005, JACM.

[16]  Shuang Wang,et al.  Frequent Items Computation over Uncertain Wireless Sensor Network , 2009, 2009 Ninth International Conference on Hybrid Intelligent Systems.

[17]  Saurabh Ganeriwal,et al.  Optimizing sensor networks in the energy-density-latency design space , 2002 .

[18]  J. Antonio García-Macías,et al.  An experimental analysis of Zigbee networks , 2008, 2008 33rd IEEE Conference on Local Computer Networks (LCN).

[19]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[20]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[21]  Mohamed A. Soliman,et al.  Top-k Query Processing in Uncertain Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[22]  Deborah Estrin,et al.  Habitat monitoring with sensor networks , 2004, CACM.