Optimizing WiFi AP placement for both localization and coverage

Abstract

Nowadays, WiFi infrastructures and WiFi-enabled mobile devices have been ubiquitous in our daily lives, and are promising to provide both network services and indoor positioning and navigation services due to its simplicity and low costs. But, it is evident that AP placement is critical to both localization and network coverage, so that it is helpful to find the optimal AP placement scheme in terms of both localization and coverage. This paper tackles this problem by leveraging the widely used Cramer-Rao lower bound (CRLB) and heuristic genetic algorithm to develop an efficient AP optimization method. To be specific, the CRLB is used as the metric for localization and a multiple degree criterion is defined as the metric for coverage, which is incorporated into the fitness function in the genetic algorithm. Furthermore, instead of using the idea log distance path loss (LDPL) model, the more practical Motley-keenan model is adopted to reflect the influences of obstacles which are widespread in indoor environments. Finally, extensive simulations are conducted, and comparisons between the proposed method and the other three popular methods confirm the efficiency and effectiveness of the proposed method.