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.
Highlights
Wireless Sensor Networks (WSNs) (Davey, Jacobus, Namineni & Siebert, 2010; Kamali, Laibinis, Petre & Sere, 2014) have received considerable attention in the past few years
A wireless sensor network for intelligent transportation system is proposed (Bohli, Hessler, Ugus & Westhoff, 2008), road condition and the traffic state is acquired with ITS (Losilla, García-Sánchez, García-Sánchez & García-Haro, 2012), the problem of adaptive traffic light control using real-time traffic information collected by a WSN is investigated (Zhou, Cao, Zeng & Wu, 2010), as well as unmanned vehicle (Wan, Suo, Yan & Liu, 2011), see Figure 1
A precisely optimized calibration for the Received Signal Strength Indicator (RSSI) model is a process of adjusting parameters, namely environmental factor n; shadowing factor η
Summary
Wireless Sensor Networks (WSNs) (Davey, Jacobus, Namineni & Siebert, 2010; Kamali, Laibinis, Petre & Sere, 2014) have received considerable attention in the past few years. Throughout the experimental exploration as well as real-world tracking application, a large number of information collection data could be analyzed to optimize practicing transportation operation. The calibration is standardized, which is that experimental statistical data are comparison with real values The fingerprint, as an empirically comparative method, is to estimate the experimentally obtained distance between two sensor nodes with real value. A variant of the fingerprint method (Sun, Lang, Wang & Liu, 2014) interpolates the measured data to give a better fitting on a RSSI value. Smailagic established quadratic objective function model with signal strength and distance from TMI approach (triangulation-based remapped interpolated approach) (Smailagic & Kogan, 2002). The paper is organized as follows: Section 1 introduces the literature about RSSI Model for the Vehicle Intelligent Position System.
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