Indoor localization in multi-floor buildings is an important research problem in recent years. The development of location-aware applications can be promoted by the increasing popularity of smartphones and huge demand in different business sectors based on the localization services. In this paper, we are going to use location fingerprinting based algorithms instead of modelling the propagation of the WiFi signal. The fingerprinting-based localization using Received Signal Strength (RSS) measurements coming from indoor networks, such as WiFi and BLE (Bluetooth Low Energy) is one of the most widely spread techniques for indoor localisation in multi-floor buildings. But it needs to store and transmit a huge amount of fingerprinting data which is a great drawback for mobile devices like smart phones, tablets, etc. which have limited memory, power and computational resources. Alternative methods, which have lower complexity and is faster than the fingerprinting is the Weighted Centroid Localization (WCL) and Log-Gaussian algorithms. All the simulations have been conducted by MATLAB software to test the performances of the methods. All the experiments have been carried with the given dataset of RSS and coordinate values that has been stored from smartphones in typical building environment. The errors have been obtained by computing the similarity between current estimated and the stored true fingerprints for every new location. The size of the dataset can be decreased by storing only the values of the coordinates and their corresponding floor labels.