In this section, a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the MCB algorithm, is proposed.
In our RMCB algorithm, the RSSI value are utilized to estimate the distances between the target node and its one-hop/two-hop anchors.
In our RMCB algorithm, the RSSI value are employed to form the new filter condition.
In our simulation, we compare the MCB, RMCL, RMMCB and our proposed RMCB algorithm under the same experimental setup.
* Some special parameters in our RMCB algorithm: preset error coefficient [delta]=0.1, acceleration constants [c.sub.1]=[c.sub.2]=2.0, the threshold of objective function is 0.1R, the maximum iteration number of sPSO algorithm is 30.
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.
However, the localization error of our proposed RMCB algorithm is always lower than the other three algorithms regardless of how many the anchor nodes number is.
However, our RMCB method is more precise than the RMCL and RMMCB method by about 13% and 14% on average.