Self-Organizing Maps
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
The design of an acquisitional query processor for sensor networks
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Medians and beyond: new aggregation techniques for sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Power-conserving computation of order-statistics over sensor networks
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
POS: A Practical Order Statistics Service forWireless Sensor Networks
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
A note on efficient aggregate queries in sensor networks
Theoretical Computer Science
Health monitoring of civil infrastructures using wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Tight bounds for distributed selection
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Robust approximate aggregation in sensor data management systems
ACM Transactions on Database Systems (TODS)
Processing Top-k Monitoring Queries in Wireless Sensor Networks
SENSORCOMM '09 Proceedings of the 2009 Third International Conference on Sensor Technologies and Applications
Data aggregation in sensor networks: no more a slave to routing
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks
IEEE Transactions on Computers
Histogram and other aggregate queries in wireless sensor networks
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
| Hi-index | 0.00 |
A number of papers concerning algorithms for processing typical aggregate queries, e.g., Max and Top-k, within a wireless sensor network have been published in recent years. However, relatively few have addressed Median queries. In this paper we propose an exact algorithm to process Median queries that is based on a series of refinement queries. Each refinement query is a Histogram query, with the aim of incrementally refining the range where the actual median value resides. Because the cost of a Histogram query depends mostly on the structure of the histogram itself, we aim at optimizing each Histogram query, hence optimizing the overall cost of the Median query. Experiments, using synthetic and real datasets, show that our proposed approach yields up to 50% less traffic than a TAG-based solution and only about 25% more traffic on average than the minimum required.