Modeling and Performance Evaluation of Bicycle-to-X Communication Networks

• Autor(a) principal: José Bastos Pintor
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://hdl.handle.net/10216/119204
• topic: Engenharia electrotécnica, electrónica e informática; Electrical engineering, Electronic engineering, Information engineering

The growing connectivity of vehicles and Vulnerable Road Users, i.e., pedestrians and cyclists, allows to explore solutions based on wireless communication to support safety, efficiency and infotainment applications.However, there are few communication technologies that enjoy similar penetration ratios on cars, bicycles and pedestrians.WiFi is one of such technologies, as can be found in smart phones and in on-board hotspots.This thesis aims to characterize experimentally the wireless link performance and develop a model to estimate the received signal strength (RSS) between WiFi devices installed on bicycles and cars equipped with built-in WiFi APs.The RSS estimation model extends existing empirical models (e.g., the Log-Distance Path Loss model) by including the shadowing of the bicycle-and-cyclist system and of a vehicle.We first characterize the radiation pattern of antennas installed in several mounting points of a bicycle, in order to reduce the set of mounting points to be explored in future measurements.We then measured the radiation pattern of the bicycle and cyclist system, and the radiation pattern of a car with built-in and dedicated WiFi access points.Finally, we evaluate the performance of the model by comparing RSS estimates and measurements collected in selected interaction scenarios between bicycles and car: (i) bicycle overtaking a parked car, (ii) perpendicular crossing with LOS, and (iii) without LOS. We observed that 50% of the RSS estimates our model underestimates by less than are within 10 dBs of measured values about 50% of the RSSI values for the scenarios in LOS, and overestimates the RSSI values by more than 5 DBs about 75% of the RSSI values for the scenario containing obstructions.