Localization in Sensor Networks with Fading Channels based on Nonmetric Distrance Models

Wireless Sensor Network (WSN) applications nowadays are an emerging avenue in which sensor localization is an essential and crucial issue. Many algorithms have been proposed to estimate the coordinate of sensors in WSNs, however, the attained accuracy in real-world applications is still far from the theoretical lower bound, Crame-Rao Lower Bound (CRLB), due to the effects of fading channels. In this paper, we propose a very simple and light weight statistical model for rang-based localization schemes, especially for the most typical localization algorithms based on received signal strength (RSS) and time-of-arrival (TOA). Our proposed method infers only the order or the nomination of given distances from measurement data to avoid significant bias caused by fading channels or shadowing. In such way, it radically reduces the effects of the degradation and performs better than existing algorithms do. With simulation of fading channels and irregular noises for both the RSS-based measurement and the TOA-based measurement, we analyze and testify both the benefits and the drawbacks of the proposed models and the localization scheme.