Experimental exploration of RSSI model for the vehicle intelligent position system
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Purpose: Vehicle intelligent position systems based on Received Signal Strength Indicator
(RSSI) in Wireless Sensor Networks (WSNs) are efficiently utilized. The vehicle’s position
accuracy is of great importance for transportation behaviors, such as dynamic vehicle routing
problems and multiple pedestrian routing choice behaviors and so on. Therefore, a precise
position and available optimization is necessary for total parameters of conventional RSSI
model.
Design/methodology/approach: In this paper, we investigate the experimental performance
of translating the power measurements to the corresponding distance between each pair of
nodes. The priori knowledge about the environment interference could impact the accuracy of
vehicles’ position and the reliability of parameters greatly. Based on the real-world outdoor
experiments, we compare different regression analysis of the RSSI model, in order to establish
a calibration scheme on RSSI model.
Findings: Empirical experimentation shows that the average errors of RSSI model are able to
decrease throughout the rules of environmental factor n and shadowing factor η respectively.
Moreover, the calculation complexity is reduced, as an innovative approach. Since variation
tendency of environmental factor n, shadowing factor η with distance and signal strength could
be simulated respectively, RSSI model fulfills the precision of the vehicle intelligent position
system.
(RSSI) in Wireless Sensor Networks (WSNs) are efficiently utilized. The vehicle’s position
accuracy is of great importance for transportation behaviors, such as dynamic vehicle routing
problems and multiple pedestrian routing choice behaviors and so on. Therefore, a precise
position and available optimization is necessary for total parameters of conventional RSSI
model.
Design/methodology/approach: In this paper, we investigate the experimental performance
of translating the power measurements to the corresponding distance between each pair of
nodes. The priori knowledge about the environment interference could impact the accuracy of
vehicles’ position and the reliability of parameters greatly. Based on the real-world outdoor
experiments, we compare different regression analysis of the RSSI model, in order to establish
a calibration scheme on RSSI model.
Findings: Empirical experimentation shows that the average errors of RSSI model are able to
decrease throughout the rules of environmental factor n and shadowing factor η respectively.
Moreover, the calculation complexity is reduced, as an innovative approach. Since variation
tendency of environmental factor n, shadowing factor η with distance and signal strength could
be simulated respectively, RSSI model fulfills the precision of the vehicle intelligent position
system.
Article Details
Com citar
Cao, Zhichao et al. “Experimental exploration of RSSI model for the vehicle intelligent position system”. Journal of Industrial Engineering and Management, vol.VOL 8, no. 1, https://raco.cat/index.php/JIEM/article/view/293177.