Dynamic sampling rate algorithm (DSRA) implemented in self-adaptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling

Hatem Algabroun

doi:10.1007/s00542-019-04631-9

Abstract

With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.

Highlights

In the maintenance industry, condition monitoring can be defined as a technique used to monitor physical variables to draw conclusions about the condition of an asset being monitored
As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation
The Rainbow software architecture appears to have a mechanism that answers those questions, that is, after collecting data, Model manager component detects the change in the signal, Constraint evaluator component determines the relevance of the change, and Adaptation engine component selects the suitable adaptation strategies that are executed by Adaptation executor component

Summary

Introduction

Condition monitoring can be defined as a technique used to monitor physical variables (e.g., temperature, vibration, and pressure) to draw conclusions about the condition of an asset being monitored. Condition monitoring techniques are used as a data source in condition-based maintenance to provide an early warning to plan and trigger cost-effective maintenance actions (Owen et al 2009; Al-Najjar 2012). With the recent digitalization trends in industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of installing them in inaccessible locations for wired sensors, such as in embedded systems and rotating machines. This avoids the burden of unreliable electrical connections and wiring expenses (Owen et al 2009). Great success has already been achieved in this area, energy limitation remains a major obstacle for further

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