Energy-Efficient Compressed Sensing in Cognitive Radio Network for Telemedicine Services

Algorithm 1

K-means clustering.

Input:

 1. N= number of CRBAN nodes

 2. k_opt= number of desired clusters

 3. E_threshold = Threshold Energy

 4. d_threshold = Threshold distance = √(d_fs/d_mp)

Output:

 1. Optimum number of initial centroids

 2. Set of k optimum clusters

Steps:

 1. Compute the distance from the center to each node.

 2. Arrange the nodes according to the distance computed in step 1.

 3. Partition the sorted nodes into k_opt sets.

 4. In each set the middle point is the initial centroid.

 5. This node at the centroid is the initial CH.

 6. Repeat

 7. Based on Euclidean distance the remaining nodes join their nearest CH.

 8. Centroid Formula to Calculate the Centroid of Each Cluster

 9. Allocate an ID number to each cluster node, with a smaller ID number to nodes closer to the cluster's initial CH.

 10. For all selected CHs,

    Check if (E_residual ≥ E_threshold)

    Then the node remains as CH; otherwise the next lower ID node is selected as a new CH.

    End if

    End for

 11. The network broadcasts information about newly elected CHs

 12. Until the CHs are no longer changed.

 13. Coordinators send data packets to their CHs.

 14. Calculate the distance between each CH and BS (d_BS),

    If d_BS<d_threshold then CH communicates directly with the BS.

    Otherwise CH selects the nearest neighbor CH which satisfies the condition d_BS<d_threshold to communicate to the BS

    End if