MULTI-STAGE OPTIMIZATION OF IEEE 802.11 ACCESS POINTS PLACEMENT USING PARTICLE SWARM OPTIMIZATION BASED ON PATH LOSS

Abstract

Indoor placement is critical factor in enhancing the performance of wireless networks especially in smart healthcare environments requiring accurate localization and consistent connectivity. This study proposed a multi-stage optimization framework using the Particle Swarm Optimization (PSO) algorithm to optimize the placement of IEEE 802.11 Access Points (APs) based on empirical Path Loss (PL) modelling. Initially Received Signal Strength Indictor (RSSI) data is collected across four zones in a hospital environment. A zone-specific long distance PL model is derived by optimizing the PL exponent and the standard deviation using PSO under both Line of Sight (LoS) and non Line of Sight (NLoS) conditions. Subsequently a second PSO stage determines the optimal AP location to maximize coverage and minimize interference by maximizing Signal to Interference Ratio (SIR) using the optimum PL parameters in the previous stage. The accuracy of measurement data was evaluated by calculating the mean-square error (MSE) between real PL and theoretical PL, as well as the real PL and optimized PL model. The results demonstrate that the measurement data is accurate and the optimized PL model can reduce MSE and build a reliable dataset. The minimum MSE is achieved in zone 1 which is equal to 0.28 dB. On the other hand, the optimum AP locations provide a high coverage area with less interference