Multiple Parameter based Clustering

Multiple Parameter based Clustering ("MPC") is a technique embedded with the traditional k-means which takes different parameters (Node energy level, Euclidian distance from the base station, RSSI, Latency of data to reach base station) into consideration to form clusters in wireless sensor networks (WSNs).
The goal of MPC is to have control over the random node distribution by considering various parameter combinations. Thus we can avoid clusters with poor distribution of node or highly dense clusters and choose low energy consuming centroids to make a low energy consumed network.
Properties of this algorithm include:
1) We can have control over the random node distribution by considering various parameter combinations. Thus we can avoid clusters with poor distribution of node or highly dense clusters. The base station can centrally de- sign the network with Good clusters by our given criteria.
2) A good cluster can be defined in a new way with the following property:
# Node distribution is approximate uniform
# Inter-cluster distance is high
# The intra-cluster distance is low and thus
# The ratio of intra and inter cluster distance called validity is low
3) Minimum and maximum range of nodes in each cluster varies with parameters.
4) By adding valid parameter we can choose low energy consuming centroid and thus make a low energy consumed network.

Main Menu

See Also

Main Menu