Retrieving the relative kernel dataset from big sensory data for continuous queries in IoT systems

摘要: Internet of Things (IoT) is rapidly developed and widely deployed in recent years, which makes the sensory data generated by IoT systems explode. The huge amount some has already exceeded storage, transmission, computation capacities systems. However, valuable highly related to a query an system relatively small. Q forms relative kernel dataset Q. Therefore, retrieving instead raw will reduce heavy burdens terms transmission then energy consumption system. In this paper, we investigate problem from big for continuous queries Two algorithms, algorithm piecewise linear fitting-based algorithm, are proposed retrieve queries. Beside, algorithms estimating answers based on their datasets also proposed. Extensive simulation results provided verify effectiveness efficiency algorithms.