Big Sensor Data Acquisition and Archiving with Compression

Machine-generated data such as sensor data now comprise major portion of available information, which raises two important problems: efficient acquisition of sensor data and the storage of massive sensor data collection. These data sources generate so much data quickly that data compression is essential to reduce storage requirement or transmission capacity of devices. This work first discusses a low complexity sensing framework which enables to reduce computation and communication overheads of devices without much compromising the accuracy of sensor readings. Then a new class of compression algorithm based on statistical similarity is presented that can be effectively used in many applications where an order of data sequence could be relaxed. Next, a quality-adjustable sensor data archiving is discussed, which compresses an entire collection of sensor data efficiently without compromising key features. Considering data aging aspect, this archiving scheme is capable of decreasing data fidelity gradually to secure more storage space.