A Novel Reverse Random Hyperplane Projection Scheme and Its Effect on Mining Sensor Streams

In this work we introduce a novel, reversible data summarization technique, namely the Reverse Random Hyperplane Projection (RRHP) scheme. RRHP is particularly useful in Wireless Sensor Network (WSN) settings because it enables individual sensors to compress their local data streams before transmitting them across the WSN. In that, RRHP saves communication and, thus, the residual energy of battery-powered sensors. Then, when the compressed sensor data streams reach a base station, the reversibility property of RRHP can be used to regain approximations of the original sensor streams to perform all kinds of data mining tasks. We provide formal theoretic guarantees on how RRHP directly trades the amount of compression for the approximation of original sensor streams’ desired properties. We experimentally prove that RRHP is useful for performing various kinds of data mining tasks, over sensor data streams, by dramatically reducing the amount of communicated data, simultaneously achieving high accuracy.