Snapshot queries: towards data-centric sensor networks

In this paper we introduce the idea of snapshot queries for energy efficient data acquisition in sensor networks. Network nodes generate models of their surrounding environment that are used for electing, using a localized algorithm, a small set of representative nodes in the network. These representative nodes constitute a network snapshot and can be used to provide quick approximate answers to user queries while reducing substantially the energy consumption in the network. We present a detailed experimental study of our framework and algorithms, varying multiple parameters like the available memory of the sensor nodes, their transmission range, the network message loss etc. Depending on the configuration, snapshot queries provide a reduction of up to 90% in the number of nodes that need to participate in a user query.

[1]  Forouzan Golshani,et al.  Proceedings of the Eighth International Conference on Data Engineering , 1992 .

[2]  Nick Roussopoulos,et al.  Hierarchical In-Network Data Aggregation with Quality Guarantees , 2004, EDBT.

[3]  Wei Hong,et al.  The design of an acquisitional query processor for sensor networks , 2003, SIGMOD '03.

[4]  Johannes Gehrke,et al.  Gossip-based computation of aggregate information , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..

[5]  Deborah Estrin,et al.  ASCENT: Adaptive Self-Configuring sEnsor Networks Topologies , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[6]  Nick Roussopoulos,et al.  Compressing historical information in sensor networks , 2004, SIGMOD '04.

[7]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[8]  Wei Hong,et al.  Model-Driven Data Acquisition in Sensor Networks , 2004, VLDB.

[9]  Jennifer Widom,et al.  Adaptive filters for continuous queries over distributed data streams , 2003, SIGMOD '03.

[10]  Jennifer Widom,et al.  Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data , 2000, VLDB.

[11]  Sanjeev Khanna,et al.  On computing functions with uncertainty , 2001, PODS '01.

[12]  M - Estimating Aggregates on a Peer-to-Peer Network , 2003 .

[13]  Satish Kumar,et al.  Next century challenges: scalable coordination in sensor networks , 1999, MobiCom.

[14]  Mohamed A. Sharaf,et al.  Balancing energy efficiency and quality of aggregate data in sensor networks , 2004, The VLDB Journal.

[15]  Hari Balakrishnan,et al.  6th ACM/IEEE International Conference on on Mobile Computing and Networking (ACM MOBICOM ’00) The Cricket Location-Support System , 2022 .

[16]  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 .

[17]  Deborah Estrin,et al.  ASCENT: adaptive self-configuring sensor networks topologies , 2004, IEEE Transactions on Mobile Computing.

[18]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[19]  Jeffrey Considine,et al.  Approximate aggregation techniques for sensor databases , 2004, Proceedings. 20th International Conference on Data Engineering.