A Hybrid Resource Optimization Technique using Improved Fuzzy Logic Guided Genetic Algorithm for 5G VANETs

Abstract

Huge amount date, multi-application services and heavy mobile devices can be accommodated by Vehicular networks. Mobile traffic which is increasing rapidly is a major difficulty in vehicular networks. Capacity of network is enhanced by proposing a new paradigm of 5G-enabled vehicular networks in this paper. It also enhances the capability of system’s computing. Scalable as well as flexible methods of resource allocation is required by resource of network for supporting user’s dynamic requirements of resources and diversified quality of services in 5G driven VANETs.

With connection-centric mind set, recent heterogeneous vehicular networks are designed. In this, irrespective of cell’s capacity, static coverage, traffic conditions, resources are allocated in a fixed manner. For a networking controller which is software defined, this work proposes a Hybrid-Improved fuzzy logic guided genetic algorithm (H-FLGA) method. For 5G driven VANETs, problems in optimization of multi-objective resource are solved by this method. In 5G VANETs, five various conditions of network resource optimization is formulated by this proposed method to realize service oriented view.

Based on customers’ requirements on type of service, multi-objective weights are optimized by using proposed fuzzy inference system. When compared with GA, multi-objective cost function is reduced by proposed method and it also resulted in less end-to end delay as shown by the results of simulation. A flexible customer-centric network infrastructure can be implemented by using this work which enhances the spectral efficiency.