Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Internet of vehicles (IoV) is an emerging area that gives support for via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation more than one radio access network. The mobility demands make handover in such heterogeneous at short range uses dedicated communication (DSRC), while it has use better technology long and any type traffic. Usually, previous work will directly select network or connects available access. Due this, occurrence takes place frequently. In this paper, integration DSRC, LTE mmWave on incorporated novel decision making, selection routing. ensure whether there a need vertical by using Dynamic Q-learning algorithm entropy function threshold prediction as per current characteristics environment. Then based fuzzy-convolution neural (F-CNN) creates fuzzy rules from signal strength, distance, vehicle density, data line sight. V2V chain routing proposed pairs jellyfish optimization (JOA) account channel, transmission metrics. This system developed OMNeT++ simulator performances are evaluated terms success probability, failure, unnecessary handover, mean throughput, delay packet loss.