Evolutionary algorithmic deployment of radio beacons for indoor positioning

In mobile computing, the location awareness of a mobile device and its user enables numerous personalized and social services such as recommendation of products and sharing current locations on social networks. Extending positioning services to indoor environments augments the value of the mobile communication market vastly. Due to serious signal attenuation, navigation satellites are incapable, and a common approach is to use or deploy small-scale radio frequency transmitters. When deploying these radio beacons, it is crucial to use a small number of them to provide high-quality positioning services. Such a deployment task is a challenging optimization problem, and the system administrators would benefit from having a spectrum of solutions with varying balance between cost and quality. In this study, we propose an Evolutionary Algorithm (EA) to tackle the problem. Using a cost-quality adjustment parameter, our EA framework is able to provide a set of solution options to meet varying requirements balancing cost and quality. This property is a result of the parallel population-based search of EAs, and can be very useful in real-world engineering applications.