Energy Efficient Data Gathering in Wireless Sensor Networks Using Rough Fuzzy C-Means and ACO

Abstract

Data gathering from inhospitable terrains such as volcanic area, dense forest, sea bed are a major application area of wireless sensor network (WSN). The replacements of sensor node batteries are not feasible and as a result all the protocols in WSN should be energy efficient to elongate network lifetime. In hierarchical routing protocol (HRP) nodes are assigned different tasks of varying energy intensity as per their role which are interchanged across rounds. It leads to load balancing and energy preservation. We propose in this paper an energy efficient load balanced data gathering method based on rough fuzzy c-means (RFCM) and ant colony optimization (ACO) and coin it as RFCM-ACO. The deployed are partitioned into clusters by RFCM followed by ACO-based lower and upper chain formation. The chain leader (CL) for lower chain and super leader (SL) for upper chain are elected using a fuzzy inference system (FIS). Simulation results indicate that RFCM-ACO outperforms LEACH, PEGASIS and Hybrid_FCM in terms of network lifetime and load balance.