Performance Analysis of V2V and V2I Communications Using Empirical Path Loss Models Indicators and Embedded IoT Devices
Abstract
Vehicle management technologies deals with the management of critical vehicle
information, including location, idle time, speed, and mileage. Such information can
always be transferred through a direct vehicle-to-vehicle communication among cars.
However, the limitation of this type of design is that it is based on the assumption
that vehicles are always served by cellular bases, which is not always the case. For
the effective implementation of the Internet-of-Things (IoT) technology in this
sector, it is critical to design vehicles with systems that enable them to transmit
essential information in the absence of base stations. IoT technologies can then be
used to develop mesh communication between devices to replace the need for
cellular service. This project proposes models that can be used to design self-reporting systems for vehicles to enhance self-management. The study also compares the proposed models with theoretical models, which show deviations of between 6%
and 23%. The overall efficiency of vehicle-to-vehicle (V2V), vehicle-to/from-infrastructure (V2I) or vehicle-to/from-environments can only be attained if there is
a reliable exchange of information between the communicating vehicles. Reliable
exchange of information also enhances the overall efficiency with which self-driving
cars and autonomous vehicle technologies can be implemented. Such systems require
not only a variety of IoT systems, but also a series of sensors and nodes for effective
transfer of information, the processing of information, and quick decision-making.
However, the heterogeneous environments and overall ecosystems pose reliability
changes on the information transmitted to be processed by the ecosystem in order to
guarantee the safety and functional operation of the ecosystem. This study examines
the reliability of the communication model that can support the operation of self-driving cars ecosystem. It also shows semi-empirical energy per bit to noise spectral
density, empirical radio propagation models and parameters for driving and
transportation environment. These values and models, which are obtained from a
combination of the experimental approach and analytical approach of additive white
Gaussian noise channel are used to ensure a reliable communication of wireless
sensor nodes deployed in the environments for V2V, V2I, and V2X services.
Additionally, the values and models are validated in theoretical and semi-analytical
simulation scenarios. The results indicate that both techniques are nearly
identical. The semi-empirical approach, the proposed models, and values can be used for efficient planning and future deployments of autonomous vehicles and self-driving cars.