Computing Effective Vehicular Network Connectivity Using Gaussian Based Attractor Selection Technique (GAST)

Decentralized Traffic flow management in its core depends on vehicular wireless communication. Now and beyond 5G communication networks will rely heavily on high-capacity and ultra-reliable vehicle communication. However, when vehicles are roaming on the road attempting to interact with each other, the vehicular communication problem gets more complicated. This work investigates through MATLAB simulation the applicability of E.coli stable gene derivatives that responds to network pattern changes in terms of signal quality and stability in order to reach acceptable level of connectivity under changing environment. In essence, this work incorporates biologically based approach instead of the conventional one, which can be achieved through an attractor selection process known for being adaptive to dynamically changing surroundings. The work combines Gaussian interpolation function with the attractor selection functions to achieve better and more reliable connectivity with controllable coverage and signal spread with soft transition between network connections. To validate and support using Gaussian interpolation, simulation results obtained regarding both the original attractor selection model and the modified model. Simulation is carried out under different network activities and noise levels for two network providers to vehicular communication. Simulation results indicated better performance using attractor selection algorithm with Gaussian interpolation compared to the standard attractor selection algorithm in terms of network state allocation and stable states attainment under both different network activities, dissipation values, and different noise levels. The work proved that Gaussian-based attractor selection algorithm is much more efficient utility function compared to the standard one.