Localization in wireless sensor networks: A dimension based pruning approach in 3D environments

Abstract

Accurate localization is a key agenda for location-based services in wireless sensor networks. In this paper, two dimensionality based 3D node localization algorithms using the particle swarm optimization framework is proposed. The anchor nodes are randomly distributed in 3D search space and the proposed positioning methodology estimates the location of target node amidst of noise factors. It is also reliable in highly complex node deployments, i.e., anisotropic environments. Instead of deploying particles in terms of co-ordinate positions as followed in existing particle swarm optimization (PSO) based techniques, dimensionality based particle swarm optimization (DPSO) and hybrid dimensionality based particle swarm optimization (HDPSO) considers each dimension of the participating co-ordinate for particle deployment to attain the optimized values in individual dimension. The node deployment environment faces more on-field complexities such as radio signal irregularities, interference and path loss to calculate received signal strength (RSS). This results in estimation of inappropriate distance estimates. To overcome the disjunctive between received signal strength and distance estimates, the proposed algorithms include pre-processing factors for manipulating RSS and follows a sphere-based deployment region for deployment of particles. Both algorithms utilize less computational power and minimize the error rate in location estimation. The mature and fast convergence behavior of both DPSO and HDPSO directly reduces the computational run time. The results of two proposed techniques are compared with the existing algorithms such as PSO and HPSO in terms of time, average localization error and scalability. The models outperform well on random test cases and behave well in heterogeneous environments with increased network lifetime.