FCVM(i): Integrated FCNP-VWA-MCDM(i) Methods for On-Demand Charging Scheduling in WRSNs

Authors

  • Ju Song Rim Communication Faculty, Kim Chaek University of Technology, Democratic People’s Republic of Korea
  • Man Gun Ri Communication Faculty, Kim Chaek University of Technology, Democratic People’s Republic of Korea https://orcid.org/0000-0002-0763-5004
  • Se Hun Pak Communication Faculty, Kim Chaek University of Technology, Democratic People’s Republic of Korea
  • U Song Kim Communication Faculty, Kim Chaek University of Technology, Democratic People’s Republic of Korea

DOI:

https://doi.org/10.47852/bonviewJDSIS42023250

Keywords:

wireless rechargeable sensor network (WRSN), fuzzy cognitive network process (FCNP), variable weight analysis (VWA), TOPSIS, on-demand CS, partial charging time

Abstract

On-demand charging schemes have been recently proposed to make efficient charging schedules of mobile chargers by introducing MCDM methods in wireless rechargeable sensor networks (WRSNs). However, most of the existing schemes use analytic hierarchy process (AHP) or fuzzy AHP (FAHP) of paired ratio scale (PRS) to exaggerate the actual paired difference between multi-criteria, thereby very likely producing misapplications such as inappropriately ranking the charging locations or inaccurately drawing the partial charging time in charging scheduling (CS) of WRSNs. In addition, in case of using FCNP of paired interval scale (PIS) for weight assignment of multi-criteria, weight compensation has not been considered. In particular, it is still unknown which is the best method for integrating FCNP with several MCDM approaches. This paper proposed novel CS methods by integrated FCNP-VWA-MCDM(i) called FCVM(i) which solves all of these problems. The proposed methods first assign the weights to multiple criteria discriminating charging request nodes (cRNs) using FCNP and make compensation of them to be relatively exact weights with VWA. Then, on the basis of these weights, MCDM(i) is used to elect the best proper next charging position. In this way, drawing up the recharging schedule, at the selected charging locations, we decide the reasonable partial charging time using the assigned weights with FCNP-VWA. Extended experiment results prove that the FCVM(1) using TOPSIS gives the best performance among FCVM(i) methods.

 

Received: 26 April 2024 | Revised: 25 July 2024 | Accepted: 18 September 2024

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

 

Data Availability Statement

The data used to support the finding of this study are available from the corresponding author upon request.

 

Author Contribution Statement

Ju Song Rim: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - original draft, Writing - review & editing, Visualization, Project administration. Man Gun Ri: Conceptualization, Validation, Resources, Writing - review & editing, Supervision, Funding acquisition. Se Hun Pak: Validation, Investigation. U Song Kim: Data curation.


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Published

2024-09-23

How to Cite

Rim, J. S., Ri, M. G., Pak, S. H. ., & Kim, U. S. . (2024). FCVM(i): Integrated FCNP-VWA-MCDM(i) Methods for On-Demand Charging Scheduling in WRSNs. Journal of Data Science and Intelligent Systems. https://doi.org/10.47852/bonviewJDSIS42023250

Issue

Section

Research Articles