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. 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. In this paper, it is found that the variance of RSSI value changes along with distance regularly by analyzing a large number of experimental data. 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). At the same time, the method of least squares(LS) was selected to estimate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce error, and have strong self-adaptability to various environments compared with the LNSM.
Xu et al. REF proposed an RSSI-based distance measurement model using log-normal shadowing model with dynamic variance propagation model and found that the proposed propagation model owns strong self-adaptability to various environments.
Distance Measurement Model Based on RSSI in WSN
Wirel. Sens. Netw.
Wirel. Sens. Netw.
Computer Science