Research Article
A Clustering Approach for Multiband Neighbor Discovery on 60 GHz WLAN
Algorithm 1
Clustering greedy algorithm.
| 1: function CLUSTERING(C number of available channels, K number of nodes) | |
| 2: distance_matrix Matrix(K,K) | |
| 3: score Array(K) | |
| 4: leaders Array(C) | |
| 5: clusters Array(C) | |
| 6: limit ← | |
| 7: for each pair of nodes do | |
| 8: distance_matrix distance | |
| 9: end for | |
| 10: quickSort(distance_matrix) | |
| 11: for each node do | |
| 12: score← sum of indices of the distance_matrix which contains | |
| 13: end for | |
| 14: leader C nodes with minimal score | |
| 15: for each cluster do | |
| 16: while (cluster[c]) < limit do | |
| 17: clusters[c] nearest node to the leader of not yet added to any cluster | |
| 18: endwhile | |
| 19: end for | |
| 20: if any node remains then | |
| 21: Add to the cluster of the nearest cluster leader, which | |
| 22: end if | |
| 23: return clusters | |
| 24: end function |