The RMCL method  introduce the RSSI value only in the process of building the sample box, the filter condition is exactly the same as the MCL algorithm.
However, the localization error of our proposed RMCB algorithm is always lower than the MCB, RMCL and RMMCB algorithm by about 24%, 14% and 14% respectively on average.
The simulation results show that the localization errors of our RMCB algorithm is lower than the MCB, RMCL and RMMCB algorithm by about 22%, 11% and 14% respectively on average.
The Monte Carlo box localization algorithm based on RSSI (MCBBR)  uses a reference genetic algorithm (linear crossing and rectangular crossing) to enhance the localization accuracy of the RMCL scheme and RSSI observation to optimize the sample area.
There are various schemes that fully depend on anchor nodes such as MCL, dual and mixture MCL, MCB, and PMCB, and there are others that combine anchor and normal nodes location information like [MSL.sup.*], WMCL, RMCL, COMCL, IMCL, and Orbit.
The assumption in [MSL.sup.*] is adopted in WMCL and RMCL; the normal node broadcasts its sample to the first hop.