Algorithm 2: The method for clustering

let T as the set of initial cluster centers;

calculate the number of initial cluster centers $k=\Vert T\Vert$

Repeat

assign each remaining common node to the cluster with the nearest medoid;

randomly select a common sensor node ${\tau }_{random}$;

calculate the cost function $S\left(S={\sigma }^{*}-\sigma \right)$ of swapping node ${\tau }_j$ with ${\tau }_{random}$;

if S<0 then

swap ${\tau }_j$ with ${\tau }_{random}$ to form the new set of k clusters;

Until no change

Output: a set of k clusters.