Data Aggregation and Principal Component Analysis in WSNs

Data aggregation plays an important role in wireless sensor networks (WSNs) as far as it reduces power consumption and boosts the scalability of the network, especially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works in the literature use clustering approaches, principal component analysis (PCA) and/or compressed sensing (CS) strategies. Our contribution is aligned with PCA and explores whether a projection basis that is not the eigenvectors basis may be valid to sustain a normalized mean squared error (NMSE) threshold in signal reconstruction and reduce the energy consumption. We derivate first the NSME achieved with the new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose a new subspace-based data aggregation method. The proposed solution reduces transmissions among the sink and one or more data aggregation nodes (DANs) in the network. In our simulations, we consider without loss of generality a single cluster network and results show that the new technique succeeds in satisfying the NMSE requirement and gets close in terms of energy consumption to the best possible solution employing subspace representations. Additionally, the proposed method alleviates the computational load with respect to an eigenvector-based strategy (by a factor of six in our simulations).