3GPE: An energy efficient probabilistic fingerprint-assisted localization in indoor Wi-Fi areas

As the most competitive and cost-efficient localization technology, the map-aided fingerprinting-assisted positioning has drawn a large body of attentions during the past decade. There are normally two phases, the off-line and on-line phases, and the construction of radio map in the off-line phase will significantly influence the location accuracy in the on-line phase. However, the radio map which describes the mapping relationship between the physical positions of the reference points (RPs) and the recorded radio signal strength (RSS) or signal to noise ratio (SNR) always involves the cumbersome collection work. Further, thousands of Wi-Fi access points (APs) remaining idle will also bring serious concerns of the power consumption in the future. Therefore, in response to these compelling problems, we present a preliminary analysis towards the probabilistic location methods, develop the green global-greedy position estimation (3GPE) and introduce entropy deduction as a new metric for performance evaluation. Finally, our analytical expressions and simulation results with respect to the simple circle model and ideal indoor Wi-Fi regular environment demonstrate these issues and challenges.