Multi-path Selection Based on Attractor Selection in Heterogeneous Networks

This study looks at how to describe vehicular network connectivity using MATLAB simulation of an E. coli biological system. The model takes advantage of E. coli’s ability to adapt to environmental changes and variations by having stable states, which allows E. coli to continue surviving and developing, and uses this property to improve vehicular network connectivity to network service providers. As a result, an adaptive response to network pattern changes in terms of signal quality and stability can be obtained, as well as acceptable levels of connectivity in a changing environment via a handover mechanism. After applying it to four distinct networks, this probability-based technique was shown to work through simulation. The four networks used successfully completed the handover and maintained connectivity at various threshold levels. The impact of signal threshold variations and network sensitivity in reaction to surroundings on the handover process is also discussed in the study. By introducing two new physiologically based metrics (threshold and sensitivity), the goal of delivering Quality of Service (QoS) is realized. The used adaptive biological model allows for the selection probability λ to change according to the number of participating networks under certain environmental conditions. Noise effect is also discussed in the presented work as it affects network signals but does not affect the adaptive handover process, due to the built intelligence of the bio-inspired model.

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Multi-path Selection Based on Attractor Selection in Heterogeneous Networks

Cited by 2 publications

References 12 publications

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

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

Handover between Vehicular Network Providers Using Bioinspired Attractor Selection Technique

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