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Smart meters have currently attracted attention because of their highefficiency and throughput performance. They transmit a massive volume ofcontinuously collected waveform readings (e.g. monitoring). Although manycompression models are proposed, the unexpected size of these compressedstreams required endless storage and management space which poses a uniquechallenge. Therefore, this paper explores the question of can the compressedsmart meter readings be re-compressed? We first investigate the applicabilityof re-applying general compression algorithms directly on compressed streams.The results were poor due to the lack of redundancy. We further propose a noveltechnique to enhance the theoretical entropy and exploit that to re-compress.This is successfully achieved by using unsupervised learning as a similaritymeasurement to cluster the compressed streams into subgroups. The streams inevery subgroup have been interleaved, followed by the first derivative tominimize the values and increase the redundancy. After that, two rotation stepshave been applied to rearrange the readings in a more consecutive format beforeapplying a developed dynamic run length. Finally, entropy coding is performed.Both mathematical and empirical experiments proved the significant improvementof the compressed streams entropy (i.e. almost reduced by half) and theresultant compression ratio (i.e. up to 50%).

Can the Multi-Incoming Smart Meter Compressed Streams be Re-Compressed?