Enhancing Vehicular Networking Using Bumblebee Foraging Theory and Signals of Opportunity

Gill, Kuldeep

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Enhancing Vehicular Networking Using Bumblebee Foraging Theory and Signals of Opportunity

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In the future, connected vehicles will drastically reduce the number of road traffic accidents, leading to safer, more reliable transportation. Connected vehicle technology will enable cars to communicate with each other to share safety and infotainment information. However, there are some key challenges which must be addressed before large-scale deployments of this technology. First, the spectrum currently allocated for vehicular communication is in- sufficient to sustain high network traffic loads in congested urban environments. Second, GPS-based localization, which is critical for the operation of connected vehicles, is inadequate in urban environments. To address these challenges and improve vehicular networks, this dissertation presents two key contributions: (1) a novel bumblebee-based vehicular dynamic spectrum access (B-VDSA) algorithm as a promising solution for spectrum scarcity, and (2) signals-of-opportunity (SOP) based localization for GPS-denied environments. The B-VDSA algorithm estimates optimal channels in a distributed and time-efficient manner by utilizing bumblebee intelligence. The channel selection strategy derives fundamental concepts from the bumblebee foraging model. The algorithm is integrated into the MAC layer of the DSRC and C-V2X protocol stacks to demonstrate its feasibility in Vehicle-to-Vehicle (V2V) communication. Numerical simulation results showed substantial gain in the probability of the best channel selection achieved relative to a uniform sampling allocation approach. Similarly, we observed an increase in the probability of successful reception when employing the bumblebee algorithm via a system-level simulator. The SOP-based localization is a novel opportunistic approach of passive RF localization designed for detecting “phantom car” attacks, i.e., vehicles intentionally reporting false in- formation against the surrounding vehicles and communication networks. The feasibility of the proposed SOP-based localization approach was evaluated using a custom-built Python- based computer simulation platform. A hardware field experiment was also conducted for evaluating the performance of the proposed approach incorporating radio frequency (RF) localization, data fusion, and vehicle behavioral dynamics.

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