Optimizing 3D Sensor Network Topologies using Skeleton Extraction Algorithms

Abstract

Providing of additional resources to certain sensor nodes within the network has proved to improve network performance. To identify such nodes is a problem that exists in 3D topologies. To address this problem, skeleton extraction algorithm has been developed in an earlier work and the same is incorporated in this paper to optimize network performance in terms of network drop rate and sensor node energy. The skeleton extraction algorithm considers realistic complex 3D space models for sensor node coverage rendering it practical and applicable to the real world deployments. A novel energy utilization function is discussed to identify the skeletal links and skeleton nodes. The skeleton extracted is represented as a graph and the topological features of the 3D deployment spaces are preserved using clustering techniques. The experimental study presented considers complex 3D deployment scenarios. The simulation results prove that if additional resources are provided to the skeleton nodes identified, the network performance is enhanced and energy conservation achieved is about 20%.