Variable-rate data sampling for low-power microsystems using modified adams methods

Abstract

Abstract-A method for variable-rate data sampling is proposed for the purpose of low-power data acquisition in a small footprint microsystem. The procedure enables energy saving by utilizing dynamic power management techniques and is based on the Adams-Bashforth and Adams-Moulton multistep predictorcorrector methods for ordinary differential equations. NewtonGregory backward difference interpolation formulae and past value substitution are used to facilitate sample rate changes. It is necessary to store only 2 + 1 equispaced past values of and the corresponding values of , where = ( ), and is the number of steps in the Adams methods. For the purposes of demonstrating the technique, fourth-order methods are used, but it is possible to use higher orders to improve accuracy if required.Index Terms-Adams methods, dynamic power management, interpolation, low-power, variable-rate sampling.

Adaptive data sensing rate in ad-hoc sensor networks for autonomous transport application

“…A so called modified Adams method is introduced for data prediction in [3]. The forth-order Adams-Bashforth and Adams-Moulton methods are combined as the data predictor and corrector.…”

“…In [3] Irvine et al controlled the data sampling intervals for the low-power microsystems according to the data approximation accuracy. The authors developed a novel data prediction approach named modified Adams methods (MAM) by combining the forth order Adams-Bashforth and Adams-Moulton methods together.…”

“…However, if either the temperature or relative humidity readings exceed one of the parameter boundaries, the sensors have to sense the environment frequently with the minimum interval min t Δ , which is set to 2 minutes in this work. The time, which is calculated by (3), is the duration to reach one of the temperature boundaries with the predicted variation speed nT x Δ . To ensure a reliable environment sensing, a scaling factor 2 ≥ s is taken into account.…”

“…For this purpose, two other prediction methods are chosen to compare with neural network. One is the least squares approximation (LS) and the other is the modified Adams method (MAM), which is used for a variable-rate data sampling rate approach [3]. …”

Equidistant Piecewise function Approximation for neurocomputing based environmental monitoring in wireless sensor networks

“…As the WSN inside a container consists of battery-driven SN, energy consumption is a crucial parameter regarding environmental monitoring maintenance. In order to increase the battery lifetime, prediction algorithms are used that enable function course prediction of environmental data [5]. In general, there are several different mathematical approaches for the prediction task that vary mainly in runtime, accuracy and sampling flexibility [2].…”