Performance Evaluation of Real-Time Localization and Positioning Algorithms for WSNs

The proliferation of the Internet of Things (IoT) has had a significant impact in the improvements of localization techniques in wireless sensor networks (WSNs). However, current positing algorithms have shown their limitation in indoor localization and incurred low convergence to accurately predict sensors localization. Thus, there is a need to develop efficient localization algorithms for WSNs to offer better location accuracy and lower energy footprint. This paper explores well known localization algorithms (i.e., Markov optimization model, Gaussian model, Particle Swarm Optimization (PSO), and Salp Swarm Algorithm (SSA)) to assess their capabilities and provides a comparative study, critical discussion and analysis of these algorithms. It describes their algorithms during the self-localization procedure. Empirical results show the performance of these algorithms in terms of energy consumption, location accuracy, and network latency.