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

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.

Development of digitalised maintenance – a concept

“…For instance, in some industrial cases where remote data measurements are required, some factors such as harsh environments or the existence of large-body obstacles could cause difficulties when attempting to implement a reliable data acquisition system (Ma et al , 2016; Zhu et al , 2017; Khan et al , 2018). Additionally, the limited battery life of the wireless sensors will pose a challenge when used in some applications, particularly in inaccessible areas (Algabroun, 2019).…”

“…The challenges were found fragmented in twelve articles (Kagermann et al , 2013; Deloitte, 2015; Ma et al , 2016; Halenár et al , 2016; Zhu et al , 2017; Bokrantz et al . 2017, 2019a, b; Khan et al , 2018; Wabner, 2018; Simon et al , 2018; Algabroun, 2019). These challenges could be categorised under the following five major categories: technological advancements; data utilisation; human resources competence; regulations and standards; and capital investments.…”

Discrete Data Rate Adaptation for Wireless Body Area Networks

“…This is typical in the case of multimedia streams, e.g., a video stream can be generated with various spatial resolutions, frame rates, and quantization parameters. Commercial biosensors can be also programmed for different sampling frequencies or accuracy levels [7][8][9]. For example, an ECG (electrocardiogram) or EMG (electromyography) sensor node may produce data with a base rate and the rate of each subsequent variation is doubled (e.g., 125, 250, 500, 1000, …, 8000 SPSs).…”