WLAN scanning strategies for RSSI-based positioning

We present a novel scanning algorithm to enable mobile units to obtain measurements from surrounding access points for indoor positioning using the wireless local area network (WLAN) infrastructure. Our scanning algorithm adapts to minimize its impact on network throughput and battery power consumption while meeting a target positioning accuracy. This is achieved by using a learning procedure that dynamically adapts the parameters of the WLAN scanning procedure. Further, we show static algorithms that do not consider high-traffic scenarios will not only fail to meet positioning accuracy targets, but they also severely degrade network throughput.