Self-organizing maps
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICNP '02 Proceedings of the 10th IEEE International Conference on Network Protocols
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Optimal Fractal Coding is NP-Hard
DCC '97 Proceedings of the Conference on Data Compression
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Energy-Aware Routing in Cluster-Based Sensor Networks
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Compressing historical information in sensor networks
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
IEEE Transactions on Computers
Computer Networks: The International Journal of Computer and Telecommunications Networking
A Stimulus-Centric Algebraic Approach to Sensors and Observations
GSN '09 Proceedings of the 3rd International Conference on GeoSensor Networks
A survey of communication/networking in Smart Grids
Future Generation Computer Systems
Trench-Zohar inversion for SAR sensor network 3-D imaging based on compressive sensing
International Journal of Sensor Networks
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In the emerging area of wireless sensor networks, one of the most typical challenges is to retrieve historical information from the sensor nodes. Due to the resource limitations of sensor nodes (processing, memory, bandwidth and energy), the collected information of sensor nodes has to be compressed quickly and precisely for transmission. In this paper, we propose a new technique the Adaptive Learning Vector Quantisation (ALVQ) algorithm to compress this historical information. The Adaptive LVQ (ALVQ) algorithm constructs a codebook to capture the prominent features of the data and with these features all the other data can be piece-wise encoded for compression. In addition, we extend our ALVQ algorithm to compress multidimensional information by transforming the multidimensional data into one-dimensional data array. Finally, we consider the problem of transmitting data in a sensor network while maximising the precision. We show how we apply our algorithm so that a set of sensors can dynamically share a wireless communication channel.