Critical data points retrieving method for big sensory data in wireless sensor networks

Abstract

With the development and widespread application of wireless sensor networks (WSNs), the amount of sensory data grows sharply and the volumes of some sensory data sets are larger than terabytes, petabytes, or exabytes, which have already exceeded the processing abilities of current WSNs. However, such big sensory data are not necessary for most applications of WSNs, and only a small subset containing critical data points may be enough for analysis, where the critical data points including the extremum and inflection data points of the monitored physical world during given period. Therefore, it is an efficient way to reduce the amount of the big sensory data set by only retrieving the critical data points during sensory data acquisition process. Since most of the traditional sensory data acquisition algorithms were only designed for discrete data and did not support to retrieve critical points from a continuously varying physical world, this paper will study such a problem. In order to solve it, we firstly provided the formal definition of the δ-approximate critical points. Then, a data acquisition algorithm based on numerical analysis and Lagrange interpolation is proposed to acquire the critical points. The extensive theoretical analysis and simulation results are provided, which show that the proposed algorithm can achieve high accuracy for retrieving the δ-approximate critical points from the monitored physical world.

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

“…As a consequence, the proposed algorithms can reduce the sensory data by sampling and then reduce the energy consumption of a wireless sensor network. Another work in [15] adaptively adjusts the sampling frequency to retrieve the critical data points of each sensor, which can significantly reduce the energy consumption. The authors in [16] apply adaptive sampling in snow monitoring applications of wireless sensor networks.…”

“…There already exists some data reduction algorithms which reduce the amount of sensory data transmitted and computed in an IoT system and then reduce the energy consumption of the IoT system. Firstly, the simplest data reduction technique is based on sampling [12][13][14][15][16]. However, the sampling technique is only applicable to some simple statistic queries.…”