An adaptive method for data reduction in the Internet of Things

Enormous amounts of dynamic observation and measurement data are collected from sensors in Wireless Sensor Networks (WSNs) for the Internet of Things (IoT) applications such as environmental monitoring. However, continuous transmission of the sensed data requires high energy consumption. Data transmission between sensor nodes and cluster heads (sink nodes) consumes much higher energy than data sensing in WSNs. One way of reducing such energy consumption is to minimise the number of data transmissions. In this paper, we propose an Adaptive Method for Data Reduction (AM-DR). Our method is based on a convex combination of two decoupled Least-Mean-Square (LMS) windowed filters with differing sizes for estimating the next measured values both at the source and the sink node such that sensor nodes have to transmit only their immediate sensed values that deviate significantly (with a pre-defined threshold) from the predicted values. The conducted experiments on a real-world data show that our approach has been able to achieve up to 95% communication reduction while retaining a high accuracy (i.e. predicted values have a deviation of ±0.5 from real data values).