-Vehicle to Vehicle communication (V2V) has taken place in research interest for many purposes such as road safety and traffic management. An accurate estimation for vehicular node position is important for such type of communication. A vehicular node can be equipped with Global Positioning Systems (GPS) to estimate its position. In practice, many vehicular nodes may lose GPS signals in rural regions due to dense foliage, or in urban regions due to compact high buildings. In this paper, the received signal strength indication (RSSI) is exploited to assist vehicular nodes to estimate their locations using inter-vehicle communication. High dynamic network topology in V2V is expected due to high node mobility. As a result, the localization error due to signal strength measurements clearly increases compared to low dynamic network topology. The proposed scheme is self-correcting solution which studies the network topology scenarios that increase localization errors and introduces optimal techniques to minimize such errors. Performance evaluation and simulation results show that this work improves localization accuracy and increases the number of vehicular nodes that estimate their locations compared to existing localization schemes.Index Terms -Vehicular ad-hoc networks, Vehicle to Vehicle communication, Localization, Radio ranging, Path loss, Shadowing.
Sensor localization based on Received Signal Strength Indication (RSSI) in Wireless Sensor Networks (WSNs) takes a place in research interest because received signal strength can be measured without additional hardware. However, RSSI based localization requires static configuration for anchor nodes to achieve high localization accuracy in reasonable time. Furthermore, when sufficient number of mobile nodes are chosen as anchors, RSSI based localization accuracy is radically influenced by measurement errors of signal strength in physical environments. As a result, RSSI based localization error may increase due to anchor mobility such as a case of close anchors and a case of approximately linear anchors. In this paper, a new localization scheme is proposed to solve such problems. In addition, the proposed scheme is enhanced to work in low anchor density to minimize additional equipment cost for anchor nodes. Performance comparison and simulation results show that the proposed scheme achieves better localization accuracy in fading environments (i.e. urban, suburban or rural) compared to existing RSSI solutions.
-Underground vehicular ad-hoc networks are indorsing wireless networks because they can realize many goals such as improving the driving safety and monitoring the emergency alerts in underground environments (i.e., road tunnels). It is necessary for vehicular nodes to recognize their positions to achieve these goals. However, Global Positioning System (GPS) devices cannot operate in underground environments; furthermore, the signal propagation faces many effects such as attenuation, multipath and shadow fading. Traditional distance measurement techniques are inadequate to estimate vehicular node locations in underground environments because the expected measurement errors lead to poor positioning. In this paper, the network connectivity is exploited to estimate vehicular node positions instead of radio ranging methods. This work investigates one of the most important techniques that are based on the network connectivity (i.e., Monte Carlo) and proposes new heuristics that achieve an appropriate position estimation accuracy for vehicular nodes. As the underlying method is predictable, it enables these nodes to know their positions all the time inside underground environments. In addition, an efficient deployment strategy is proposed in this work to well organize reference nodes (i.e., fixed nodes that their positions are preconfigured) inside a road tunnel. The proposed scheme performance is verified by NS2 simulator and compared with the current Monte Carlo localization schemes where the simulation results indicate the superiority of the proposed scheme.
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