The introduction of the Bluetooth 4.0 and Bluetooth Low Energy (BLE) standard greatly facilitates the development of Internet of Things (IoT) applications. Most of these applications require a positioning mechanism to detect the position of both people and objects. While BLE is a key enabling technology, it is relatively new as compared to Wi-Fi and RFID. Hence there is a need to conduct more studies on BLE-based positioning methods. In general, positioning methods based on signal propagation and fingerprint are commonly used in wireless networking. These methods have their own limitations in terms of practical use and ease of implementation. In this paper, we present an innovative BLE-based positioning methodology called BluePrint which makes use of a detection mechanism called NUFO (Near, Uncertain, Far and Out). It combines a simple fingerprint-like method with a rule-based algorithm to estimate positions. Experimental results show that Blueprint with NUFO detection can achieve good performance as compared to other methods. Furthermore, its implementation is simple and practical.