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Using Polynomial Regression for Data Representation in Wireless Sensor Networks by Torsha Banerjee; Kaushik R. Chowdhury; Dharma P. Agrawal is a Computer Science article available to read on EtoBox.

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## Abstract Unlike conventional sensor networks, wireless sensors are limited in power, have much smaller memory buffers, and possess relatively slower processing speeds. These characteristics necessitate minimum transfer and storage of information in order to prolong the network lifetime. In this paper, we exploit the spatio‐temporal nature of sensor data to approximate the current values of the sensors based on readings obtained from neighbouring sensors and itself. We propose a tree based polynomial regression algorithm (TREG), that addresses the problem of data compression in wireless sensor networks. Instead of aggregated data, only the coefficients computed by the regression function, TREG are passed to achieve the following goals: (i) the sink can get attribute values in the regions devoid of sensor nodes, and (ii) readings over any portion of the region can be obtained at one time by querying the root of the tree. As the size of the data packet from each tree node to its parent remains constant, the proposed scheme scales very well with growing network density or increased coverage area. Since physical attributes exhibit a gradual change over time, we propose an iterative s

Who reads Using Polynomial Regression for Data Representation in Wireless Sensor Networks?

Author

Torsha Banerjee; Kaushik R. Chowdhury; Dharma P. Agrawal

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; John Wiley & Sons Ltd.; Wiley (ISSN 1074-5351)

Published

2006

Language

EN

Field

Computer Science (Physical Sciences)