Guo, et al., “Analysis of Distance Measurement Based on RSSI,” Chinese Journal of Sensors and Actuators, Vol. Nath, “Ad Hoc Positioning System (APS) Using AoA ,” Proceedings of the IEEE INFOCOM, New York, 2003, pp. Park, “A Mitigation of Line-of-Sight by TDOA Error Modeling in Wireless Communication System,” International Conference on Control, Automation and Systems, Seoul, October 2008, pp. Guo, “Non-Line-of-Sight Detection Based on TOA and Signal Strength,” Personal, Indoor and Mobile Radio Communications, Cannes, 2008, pp. Elvino, “Asymptotic Performance of Collaborative Spectrum Sensing under Correlated Log- Normal Shadowing,” Communications Letters, Vol. Joshi, “Signal Separation Using Linear Canonical and Fractional Fourier Transforms,” ScienceDirect, Vol. Sneddon, “On Certain Integrals of Lipschitz-Hankel Type Involving Products of Bessel Functions,” Philosophical Transactions of Royal Society, London, Vol. “A Testbed for Localizing Wireless LAN Devices Using Received Signal Strength,” Communication Networks and Services Research Con- ference, Halifax, 2008, pp. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.Ī. At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks.
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