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 paired ratio scale (PRS) for weight assignment of multiple criteria to exaggerate the actual paired difference between them, and in the case of using FCNP of paired interval scale (PIS) for weight assignment of multiple criteria, weight compensation is not considered.
What is more, it is still unknown which is the best method for integrating FCNP with several MCDM approaches.
Rim Ju Song, a student at the Faculty of Communications, with the help of Ri Man Gun, an institute head of the same faculty, has proposed novel CS methods by integrated FCNP-VWA-MCDM(i) called FCVM(i) for solving all these problems.
The proposed methods first assign the weights to multiple criteria discriminating charging request node (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 select the most proper next charging position. Recharging schedule is drawn up in this way and, at the same time, reasonable partial charging time at the selected charging locations are determined using the assigned weights with FCNP-VWA.
The extended experimental results prove that the FCVM(1) using TOPSIS gives the best performance among FCVM(i) methods.
You can find the details in his paper “FCVM(i): Integrated FCNP-VWA-MCDM(i) Methods for On-Demand Charging Scheduling in WRSNs” in “Journal of Data Science and Intelligent Systems” (SCI).