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Experimental Exploration of RSSI Model for the Vehicle Intelligent Position System

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2015
DOI: 
http://dx.doi.org/10.3926/jiem.1234

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
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. Research limitations/implications: In this research, it is not evident to find the variation trend between the environmental factor n, shadowing factor η and the signal strength in view of our proposed approach. Originality/value: In our study, a methodology to calibrate the parameters of RSSI model is proposed with smaller errors. At the same time, three primary conventional model is evaluated based on the fitted regression.
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