Exploiting Multi-Spatial Correlations of Motion Data in a Body Sensor Network

Chun-Hao Wu, Yu-Chee Tseng
2012 IEEE Communications Letters  
Human body motions usually exhibit a high degree of coherence and correlation in patterns. This allows exploiting spatial correlations of motion data being captured by a body sensor network. Since human bodies are relatively small, earlier work has shown how to compress motion data by allowing a node to overhear at most κ = 1 node's transmission and exploit the correlation with its own data for data compression. In this work, we consider multi-spatial correlations by extending κ = 1 to κ > 1
more » ... constructing a partial-ordering directed acyclic graph (DAG) to represent the compression dependencies among sensor nodes. While a minimum-cost tree for κ = 1 can be found in polynomial time, we show that finding a minimum-cost DAG is NP-hard even for κ = 2. We then propose an efficient heuristic and verify its performance by real sensing data. Index Terms-Body sensor network, data compression, inertial sensor, pervasive computing, wireless sensor network.
doi:10.1109/lcomm.2012.031212.120073 fatcat:gi6qzul5djgxre36y76uq2vsum