Algorithm 2 Clustering based on minimum separation distance enforcement between CHs.
procedure WSN clustering
inputs:
$N=\{n_1,n_2,\dots ,n_k\}$	% N: set of live nodes %
$range$ ← d	% d: minimum forced distance %
p	% p: Percentage of live nodes to become CHs %
$dc$ ← $Ceiling(p\times count(N\left)\right)$	% $dc$: number of the desired CHs %
outputs:
$CH=\{C{H}_1,C{H}_2,\dots ,C{H}_{dc}\}$	% set of CHs %
$C=\{C_1,C_2,\dots ,C_{dc}\}$	% set of clusters %
steps:
1) For each node $n_i\in N$, if the distance from $n_i$ to the closest already selected $CH$ is less than d, then calculate each of the fuzzy input variables: Energy, BS_Distance, Density, Compaction AVG_Energy.
2) Calculate the fuzzy output variable chance for each $n_i\in N$ based on the linguistic variables using the proposed fuzzy inference system
3) Select $n_i$ with the highest chance value among the nodes located away from any pre-selected $CH$ by the distance d
4) Repeat Steps 1–4 until reaching $dc$
5) Form clusters, $C=\{C_1,C_2,\dots ,C_{dc}\}$, by joining each $n_i\in N$ to the closest $C{H}_r\in CH$
end procedure