TAG: a Tiny AGgregation service for ad-hoc sensor networks
ACM SIGOPS Operating Systems Review - OSDI '02: Proceedings of the 5th symposium on Operating systems design and implementation
TiNA: a scheme for temporal coherency-aware in-network aggregation
Proceedings of the 3rd ACM international workshop on Data engineering for wireless and mobile access
Holistic aggregates in a networked world: distributed tracking of approximate quantiles
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
A Primitive Operator for Similarity Joins in Data Cleaning
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
ProcessingWindow Queries in Wireless Sensor Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient exact set-similarity joins
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Scaling up all pairs similarity search
Proceedings of the 16th international conference on World Wide Web
Efficient similarity joins for near duplicate detection
Proceedings of the 17th international conference on World Wide Web
Adaptive data aggregation scheme in clustered wireless sensor networks
Computer Communications
Dynamic Forwarding over Tree-on-DAG for Scalable Data Aggregation in Sensor Networks
IEEE Transactions on Mobile Computing
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Data aggregation for wireless sensor networks using self-organizing map
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
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In-network data aggregation is considered an effective technique for conserving energy communication in wireless sensor networks. It consists in eliminating the inherent redundancy in raw data collected from the sensor nodes. Prior works on data aggregation protocols have focused on the measurement data redundancy. In this paper, our goal in addition of reducing measures redundancy is to identify near duplicate nodes that generate similar data sets. We consider a tree based bi-level periodic data aggregation approach implemented on the source node and on the aggregator levels. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We propose a new frequency filtering approach and several optimizations using sets similarity functions to solve this problem. To evaluate the performance of the proposed filtering method, experiments on real sensor data have been conducted. The obtained results show that our approach offers significant data reduction by eliminating in network redundancy and outperforms existing filtering techniques.