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This paper presents density variation base clustering algorithm. The proposed scheme first defines cluster formation based on density variation using DBSCAN-DLP algorithm. This algorithm discover a cluster of same relative density nodes and automatically calculate density variation threshold parameter “α” for each cluster locally instead of using unique global density variation threshold for all clusters in the network. As a result the nodes that have relative density variation with respect to its neighboring nodes formed stable cluster and number of messages exchange during cluster formation is reduced. Secondly the cluster head (CH) selection in done on the basis of different parameters including minimum energy consumption ratio, less density variation and success factor. This lessened the usage of the energy during round time of system communication. The simulation results shows that the proposed algorithm performs better in terms of Percentage of member node has similar movement with a cluster head, cluster duration stability ratio, network lifetime, Throughput and some other parameters.