PC3: Principal Component-based Context Compression: Improving energy efficiency in wireless sensor networks

We focus on energy efficiency, which guarantees the operation of a Wireless Sensor Network for long. We propose a context compression model that works in an orthogonal fashion. We first reduce the dimensions of multivariate contextual information. This is achieved through the Principal Component Analysis (PCA), which determines the statistical dependencies between the different contextual components. We then suppress the transmission of the determined principal components through an extrapolation scheme that exploits the properties of each individual component. Our findings are quite promising for the broader domain of WSN-based application engineering and context awareness.

► We deal with energy efficiency in wireless sensor networks.
► We propose a Principal Component-based Context Compression (PC3) model.
► We adopt principal component analysis and Lagrange extrapolation for context compression.
► The proposed PC3 model optimally balances between energy savings and data accuracy.