English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In most wireless sensor network (WSN) applications, the sensor nodes (SNs) are battery powered and the amount of energy consumed by the nodes in the network determines the network lifespan. For future Internet of Things (IoT) applications, reducing energy consumption of SNs has become mandatory. In this paper, an ultra-low-power nRF24L01 wireless protocol is considered for a bicycle WSN. The power consumption of the mobile node on the cycle track was modified by combining adjustable data rate, sleep/wake, and transmission power control (TPC) based on two algorithms. The first algorithm was a TPC-based distance estimation, which adopted a novel hybrid particle swarm optimization-artificial neural network (PSO-ANN) using the received signal strength indicator (RSSI), while the second algorithm was a novel TPC-based accelerometer using inclination angle of the bicycle on the cycle track. Based on the second algorithm, the power consumption of the mobile and master nodes can be improved compared with the first algorithm and constant transmitted power level. In addition, an analytical model is derived to correlate the power consumption and data rate of the mobile node. The results indicate that the power savings based on the two algorithms outperformed the conventional operation (i.e., without power reduction algorithm) by 78%

Energy Efficiency of Ultra-Low-Power Bicycle Wireless Sensor Networks Based on a Combination of Power Reduction Techniques