The rule-based localization methods proposed in this paper are based on two important observations. First, although the absolute RSS values change with time, the relative RSS (RRSS) values between several Access Points (APs) are more stable than the absolute RSSs. Thus, we can use RRSSs as rules for inferring a client's location. Second, when a unique location cannot be obtained based on RRSS rules, the localization process can backtrack to the previous observed client location. By analyzing the accessible paths on the floor plan, locations that are not reacheable from the previous location can be disqualified. Based on these two key observations, we propose several localization methods, implement them in a life environment and conduct extensive experiments to measure the localization accuracy of the proposed methods. We found that our methods achieve much higher accuracy than the state-of-the-art localization methods, namely, RADAR, LOCADIO and WHAM!.