Experimental Exploration of RSSI Model for the Vehicle Intelligent Position System

Zhichao Cao


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.


RSSI model; environmental factor n; shadowing factor ?; intelligent position; experimental performance

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DOI: https://doi.org/10.3926/jiem.1234

Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2024

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience