BLOTTER: Block-based lossless compression for highway structural health monitoring data

The high sampling frequency of highway structural health monitoring brings a heavy burden on data storage. However, existing data compression approaches either lose some key information in monitoring data or obtain unsatisfactory effectiveness. Motivated by this, we consider the characteristics of structural health monitoring data into data compression and propose a block-based lossless compression method. To the best of our knowledge, this is the first work to incorporate the properties of structural health monitoring data into lossless compression approaches. The compression performance is affected by the data blocks with different timestamps that are selected to compress together. To determine the data blocks with which timestamps are compressed into a file within the size limit, we define a data block selection problem and develop a greedy algorithm with (1+[Formula presented])-approximation ratio. Our method not only preserves the key information in monitoring data, but also guarantees the satisfactory effectiveness. Experimental results on real highway structural health monitoring data demonstrate that our proposed approach saves 95.69% storage space of structural health monitoring data and decreases by 65.53% compression rate compared with the commonly-used lossless compression methods.