With the prevalence of mobile Wi-Fi devices and infrastructures, there are growing interests in mobile surveillance and device tracking for providing better location-aware services in metropolitan areas. With a good location estimation integrated into a wireless infrastructure, system administrators can closely monitor the network traffic as well as the behavior of the mobile users. The Received Signal Strength(RSS), easily available information from Access Point(AP) Sensors, has become the most popular research approach. However, in reality received signal strength is affected by factors such as occlusion, signal deflections and reflections. There had been proposed estimation systems that use the Fingerprinting approach to provide good and accurate location recommendation. But such systems have been drawn back by their time-intensive training and retraining process. The solution to Signal Strength-based estimation, therefore, is to devise a system that minimizes the training Andre adaptation process while attaining good accuracy in location estimation. This paper proposes a location estimation system whose estimation method is based on the Center of Gravity(CG)method. This method also serves as an algorithm selector such that the system can switch to another estimation algorithm if need be. The aim of this system is to reduce the high cost of training and re-calibration but attain an accuracy comparable to the Fingerprinting location estimation approach.