Stacking Ensemble Learning-Based Wireless Sensor Network Deployment Parameter Estimation

AKBAŞ A., Buyrukoglu S.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, vol.48, no.8, pp.9739-9748, 2023 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 48 Issue: 8
  • Publication Date: 2023
  • Doi Number: 10.1007/s13369-022-07365-5
  • Journal Name: ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH
  • Page Numbers: pp.9739-9748
  • Keywords: Wireless sensor networks, Machine learning, Parameter prediction, Stacked ensemble, Gradient boosting, LIFETIME
  • Abdullah Gül University Affiliated: Yes

Abstract

In wireless sensor network projects, it is generally desired to cover the area to be monitored at a given cost and to achieve the maximum useful network lifetime. In the deployment of the wireless sensors, it is necessary to know in advance how many sensor nodes will be required, how much the distance between the nodes should be, etc., or what the transmit power level should be, etc. depending on the channel parameters of the area. This necessitates accurate calculation of variables such as maximum network lifetime, communication channel parameters, number of nodes to be used, and distance between nodes. As numbers reach to the order of hundreds, calculation tends to a NP hard problem to solve. At this point, we employed both single-based and stacked ensemble-based machine learning models to speed up the parameter estimations with highly accurate outcomes. Adaboost was superior over other models (Elastic Net, SVR) in single-based models. Stacked ensemble models achieved best results for the WSN parameter prediction compared to single-based models.