Controlling Traffic in Internet of Vehicles Using Energy Aware Optimized Intelligent Transport System Using Red deer algorithm with New FNN Method

Abstract

Networks deployed in Internet of Vehicle (IoV) has an eminent rolein message exchanging in addition to associated services or application.  The advancement in Intelligent transportation system has eased existing traffic conditions which has gained attention due to smart cities besidesIoVsinvolved in Internet of Things up gradation. On the basis of traffic intensity environment, proficient establishment besides consistent intercommunication routes amid vehicular node is greatly necessitated. One among proposed methodology   namely fuzzy logic based traffic intensity calculation function is utilized previously for mitigating those issues for huge traffic modelling. Conversely, membership function selection through iterative methodology is regarded as a time consuming task. On the basis of on Takagi–Sugeno (TS) model with intelligent water drop algorithm (TSFNN-IWD),a novel fuzzy neural network (FNN) is presented for precise traffic intensity assessment, currently IWD is exploited for optimal membership value selection. The optimal route path  selection outcome is extemporized on the basis of Red deer algorithm (EARD) with TSFNN-IWD through an intelligent transportation system establishment with energy-aware routing  which is entitled as EARD-TSFNN-IWD-IoV for transmission range adapting  intensity in local traffic concern. The various performance metrics such as throughput, packet delivery, drop ratio in addition to average end-to-end delay are assessed for proposed EARD-TSFNN-IWD-IoV protocol and validated the superiority of the proposed method in contradiction to 0prevailing EACOFNNIoV besides ELHACOGFNNGAIoV protocols.