Algorithm 1. Sub-Classification Tracing.

Input: R = {RSSI1, RSSI2, …, RSSIn}//the collected RSSI samples Output: NC, IC, DC

Define://initial the center of IC, NC, and DC     IV = MAX(R), NV = AVERAGE(R), and DV = MIN(R)     ICT = ϕ, NCT = ϕ, and DCT = ϕ  //temporal collections     TD = ID = ND = DD = 0.0    //temporal values. Classify: For each (RSSIi ∈ R){ //calculate its distance to IV, NV, and DV ID = abs (IV- RSSIi)        ND = abs (NV- RSSIi)        DD = abs (DV- RSSIi)        //select the minimum distance        TD = MIN ({ID, ND, DD})        //add RSSIi to sub-classification        If (TD= =ID)        IC = IC + { RSSIi }        Else if (TD= =ND)     NC = NC+{ RSSIi }     Else     DC = DC + { RSSIi }     } Judge:    //judge the change of sub-classification    If (ICT = = NCT = = DCT = = ϕ){        ICT = IC        NCT = NC        DCT = DC        } GOTO (Reset) //if convergence, exit    If (ICT = = IC and NCT = = NC and DCT = = DC)    GOTO (Exit)    Else {        ICT = IC        NCT = NC        DCT = DC        } Reset: //reset the sub-classification center IV = AVERAGE (IC)     NV = AVERAGE (NC)     DV = AVERAGE (DC)     GOTO (Classify) Exit:  //deal with ϕ    If (NC = = ϕ) {     TD = (ID + DD)/2     NC+ = {TD}     } EXIT(-)