Distance Measurement Model Based on RSSI in WSN
By Jiuqiang Xu, Wei David Liu, Fenggao Lang, Yuanyuan Zhang, Chenglong Wang. Wireless Sensor Network, 2010
334 citations recorded by the source corpus.
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 regu- larly 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 es- timate 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 er- ror, and have strong self-adaptability to various environments compared with the LNSM.
Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/294a3fbb66cefd4f0f9404588a6f43aaad9bdb05
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In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks (2016, methodology): “Same methodology has been adopted in the literature [51].”