Compressing Kinetic Data from Sensor Networks

We introduce a framework for storing and processing kinetic data observed by sensor networks. These sensor networks generate vast quantities of data, which motivates a significant need for data compression. We are given a set of sensors, each of which continuously monitors some region of space. We are interested in the kinetic data generated by a finite set of objects moving through space, as observed by these sensors. Our model relies purely on sensor observations; it allows points to move freely and requires no advance notification of motion plans. Sensor outputs are represented as random processes, where nearby sensors may be statistically dependent. We model the local nature of sensor networks by assuming that two sensor outputs are statistically dependent only if the two sensors are among the k nearest neighbors of each other. We present an algorithm for the lossless compression of the data produced by the network. We show that, under the statistical dependence and locality ass...

• Type: Conference
• Year: 2009
• Where: ALGOSENSORS
• Authors: Sorelle A. Friedler, David M. Mount
• Keywords: ALGOSENSORS 2009, Kinetic Data, Sensor Networks, Sensors, Wireless Sensors