Online Information Compression in Sensor Networks

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 limitation 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 ALVQ (Adoptive Learning Vector Quantization) algorithm to compress this historical information. The 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, with two-level regression of the codebook´s update, ALVQ algorithm saves the data transfer bandwidth and improves the compression precision further. Finally, we consider the problem of transmitting data in a sensor network while maximizing the precision. We show how we apply our algorithm so that a set of sensors can dynamically share a wireless communication channel.