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

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