Experimental Exploration of RSSI Model for the Vehicle Intelligent Position System
Abstract: 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. In this papar, we investigate
the experimental performance of translating the power measurements to
corresponding distance between each pair of nodes. The priori knowledge about
the environment interference could impact the accuracy of vehicles’s position
and the reliability of paremeters greatly. Based on the real-world outdoor
experiments, we compares different regression analysis of the RSSI model, in
order to establish a calibration scheme on RSSI model. We showed that the
average error of RSSI model is able to decrease throughout the rules of
environmental factor n and shadowing factor ? respectively. Moreover, the
calculation complexity is reduced. Since variation tendency of environmental
factor n, shadowing factor ? with distance and signal strength could be
simulated respectively, RSSI model fulfills the precision of the vehicle
intelligent position system.
Keywords: RSSI model;
environmental factor n; shadowing factor ?; intelligent position; experimental
performance
Author: Zhichao Cao
Journal Code: jptindustrigg150019
