A Pragmatic Evaluation of Piece-Wise Linear Lossy Compression Schemes

We consider piece-wise linear lossy compression schemes for wireless sensor network applications, involving streaming sensor data, as small-size wireless packet payloads. Comparisons of existing schemes can mislead if no due care is paid to the exact cost, that we call representation cost , which counts how many bits are required to represent the sequence of the produced line segments. We rectify the situation in the presented work by presenting a fair means to evaluate the representation cost of competing schemes. Schemes that produce a small number of line segments can result in poor compression ratio when one considers the representation cost involved. Additionally, we outline an extremely competitive scheme which, at the cost of more computation, satisfies a given size for the compressed data output, while minimizing L ∞ error. It improves the compression ratio, reducing the representation cost by up to 140% over previous schemes while also practically achieving a lower maximum reconstruction error than the reconstruction error bound requested. We address several technicalities for fairly mapping, and evaluating, existing schemes that, when originally proposed, were indifferent to data representation concerns. We evaluate the compression schemes using a number of datasets, composed of different sensor types, compressed under a range of reconstruction error bounds and payload size restrictions, including some data that are challenging for any piece-wise linear compression scheme.