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

To monitor environments, wireless sensor networks (WSNs) are used for collecting data in divers’ domains such as smart factories, smart buildings, etc. In such environments, different medium access control (MAC) protocols are available to sensor nodes for wireless communications and are of a paramount importance to enhance the network performance. Proposed MAC layer protocols for WSNs are generally designed to achieve a good performance in packet reception rate. Once chosen, the MAC protocol is used and remains the same throughout the network lifetime even if its performance decreases over time. In this paper, the authors adopt supervised machine learning techniques to predict the performance of CSMA/CA MAC protocol based on the packet reception rate. This approach consists of three steps: experiments for data collection, offline modeling and performance evaluation. The final analysis shows that XGBoost prediction model is the better supervised machine learning technique to enhance network performance at the MAC layer level. In addition, the authors use SHAP method to explain predictions.

Using Machine Learning in WSNs for Performance Prediction MAC Layer