Concentric Layered Architecture for Multi-Level Clustering in Large-Scale Wireless Sensor Networks

Abstract

Multi-level clustering offers energy efficient data gathering and much needed scalability in large-scale wireless sensor networks (WSNs). Although, few multi-level frameworks have been designed for static clustering and manually deployed WSNs, but no work has been done for randomly deployed WSN performing dynamic clustering. Moreover, there is a lack of structured framework for evolutionary optimization based multilevel clustering protocols. Design of multi-level clustering depends on two parameters: 1) optimal position of layers and 2) number of sensor nodes at each layer. Based on these parameters, a concentric layered architecture (CLA) is designed in this paper to perform multi-level clustering in randomly deployed WSN. CLA divide the network into layers based on node density and number of sensor nodes at each layer. Further, CLA is evaluated on an evolutionary optimization technique based clustering approach namely PSO-C. Simulation results show that the proposed CLA significantly improves the network lifetime and energy efficiency.