On In-network Synopsis Join Processing for Sensor Networks

Authors:
Hai Yu;Ee-Peng Lim;Jun Zhang
Affiliations:
Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore
Venue:
MDM '06 Proceedings of the 7th International Conference on Mobile Data Management
Year:
2006

Citing 0
Cited 10

Adaptive processing of historical spatial range queries in peer-to-peer sensor networks

Distributed and Parallel Databases
A substrate for in-network sensor data integration

Proceedings of the 5th workshop on Data management for sensor networks
Retrieval of Spatial Join Pattern Instances from Sensor Networks

Geoinformatica
PEJA: progressive energy-efficient join processing for sensor networks

Journal of Computer Science and Technology
Processing continuous join queries in sensor networks: a filtering approach

Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Dynamic join optimization in multi-hop wireless sensor networks

Proceedings of the VLDB Endowment
SNEE: a query processor for wireless sensor networks

Distributed and Parallel Databases
Cost based in-network join strategy in tree routing sensor networks

Information Sciences: an International Journal
Load shedding for multi-way stream joins based on arrival order patterns

Journal of Intelligent Information Systems
TWINS: Efficient time-windowed in-network joins for sensor networks

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

The emergence of sensor networks enables applications that deploy sensors to collaboratively monitor environment and process data collected. In some scenarios, we are interested in using join queries to correlate data stored in different regions of a sensor network, where the data volume is large, making it prohibitive to transmit all data to a central server for joining. In this paper, we present an in-network synopsis join strategy for evaluating join queries in sensor networks with communication efficiency. In this strategy, we prune data that do not contribute to the join results in the early stage of the join processing, therefore reducing unnecessary communication overhead. In our simulation-based experiments, we study the performance of synopsis join for different join selectivities and investigate the impact synopsis accuracy and message loss. The results show that synopsis join outperforms the centralized join scheme in terms of communication cost, especially for low join selectivities, thus prolonging the lifetime of the sensor network.