Optimization of energy consumption in wireless sensor networks using density-based clustering algorithm

Todays, using wireless sensor networks including a large number of sensor nodes located in the target environment to monitor physical and environmental activities has widely expanded due to their benefits and applications in numerous contexts. The most important part of a sensor node is its power supply coming from the battery, and since these batteries cannot be recharged in complex environments, optimum consumption of energy has turned into a harsh challenge facing applications which require a long lifetime of the network. The present research set out to use the sensor node clustering method to reduce energy consumption. Using density-based clustering is one of the most effective methods in this field, which can help to obtain this goal. The proposed method is inspired by the OPTICS density-based clustering environment, which is an efficient algorithm and can create clusters with different dimensions. In this method, the clusters are first formed, and then cluster-head nodes are selected. In the next stage, that nodes which do not belong to any cluster, join to the nearest cluster to their cluster-head node. Finally, the related data are collected by the sensors and sending them to the cluster-head nodes. Simulation results revealed that the proposed method based on OPTICS density-based clustering algorithm has better performance, compared to similar algorithms such as LEACH, SEP, EEHC, and DBSCAN and could increase network lifetime and the number of packets sent by reducing energy consumption.