Performance evaluation of vertical handover in Internet of Vehicles

Abstract Internet of Vehicles (IoV) is developed by integrating the intelligent transportation system (ITS) and Things (IoT). The goal IoV to allow vehicles communicate with other vehicles, humans, pedestrians, roadside units, infrastructures. Two potential technologies V2X communication are dedicated short-range (DSRC) cellular network technologies. Each these has its benefits limitations. DSRC low latency but it limits coverage area lacks spectrum availability. Whereas 4G LTE offers high bandwidth, wider cell range, drawback transmission time intervals. 5G enormous present wireless technology providing higher data rates very latencies for transmissions prone blockages because inability penetrate through objects. Hence, considering above issues, single will not fully accommodate requirements which subsequently jeopardize effectiveness safety applications. Therefore, efficient communication, required interwork One open research challenge that gained attention community over past few years appropriate selection networks handover in a heterogeneous environment. Existing solutions have addressed issues related they failed address need while selecting network. Previous studies only mentioned being selected directly or was connected available radio access. Due this, occurrence had take place frequently. this research, integration DSRC, LTE, mmWave incorporated decision, selection, routing algorithms. decision ensure whether there vertical using dynamic Q-learning algorithm. Then, based on fuzzy-convolution neural creates fuzzy rules from signal strength, distance, vehicle density, type, line sight. V2V chain proposed select pairs jellyfish optimization algorithm takes into account channel, characteristics, metrics. This an OMNeT++ simulator performances evaluated terms mean handover, failure, throughput, delay, packet loss.