Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2513228.2513291 | RACS |
Keywords | Field | DocType |
bayesian-based localization,bayesian inference,inhomogeneous transmission media,novel robust probabilistic approach,heterogeneous transmission medium,path-loss exponent,bayesian-based method,heterogeneous medium,square error,existing localization approach,rss measurement,probability density function | Bayesian inference,Exponent,Computer science,Mean squared error,Artificial intelligence,Probabilistic logic,Computer vision,Algorithm,Transmission medium,Statistics,RSS,Probability density function,Bayesian probability | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Esmaeil S. Nadimi | 1 | 9 | 5.90 |
| Victoria Blanes-Vidal | 2 | 6 | 3.46 |
| Per Michael Johansen | 3 | 1 | 0.72 |