Indoor localization has become one of the most investigated techniques over the last decade due to the wide scale usage of smart phones and devices with wireless communication capabilities. This proliferates usage has made localization and user tracking much convenient for implementing IoT applications in smart buildings. Also due to the limitations of Global Navigation Satellite System in indoor environments, indoor positioning systems have gained much demand especially with various governments coming up with smart city and smart building initiatives. Various technologies like Wi-Fi, Bluetooth and Zigbee have been used to predict a user or device location indoor but the optimal location accuracy still remains a major problem. So, a review of some of the localization techniques being used and the challenges that could be faced while implementing a localization model is presented. Then the proposal comes up with a manual site survey approach which can be used to efficiently gather RSSI (Received Signal Strength Indicator) fingerprints from Wi-Fi Access Points so that the prediction models get trained well with the data collected and also presents some insights on the collected data.