Time-slot Reservation and Channel Switching in VANETs Using the Markovian Model for Multi-channel TDMA MAC PROJECT TITLE : Time-slot Reservation and Channel Switching using Markovian Model for Multi-channel TDMA MAC in VANETs ABSTRACT: In order for there to be effective Communication within the intelligent transportation system, the vehicles themselves need to be able to send both safety and non-safety messages in a timely manner and without any loss. The frequency range, frequency limit, power limit, distance covered, and channel performance are the characteristics that are associated with SCH channels in multi-channel MAC. Messages have to be sent over the channel that is best suited for their transmission based on the properties of the channel. The messages are transmitted using one of the available SCH channels, which is chosen at random by the existing multi-channel MAC approaches. The selection of the channel at random can lead to either an excessive or insufficient consumption of the channel's resources. The channel allocation that is proposed as part of the approach sets aside the SCH that is the most suitable and maximizes the use of the resources. The variable size TDMA approach that has been proposed for the allocation of SCH channels takes into consideration a large number of competing vehicles while still maintaining a collision-free allocation of resources. The reusability of the time slot for message transmission is another primary concentration of this paper, along with the mitigation of transmission collisions. The Markovian modeling technique is used to calculate the likelihood of successfully reserving the time slot and switching to the necessary SCH. The results of the simulation show that the probability of reserving a time slot in the required SCH is approximately 0.6 for the approach that has been proposed, while it is approximately 0.2 for approaches that have not been proposed. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Network Traffic Prediction Model in VANET Based on Road Traffic Parameters Using Artificial Intelligence Methods Two-layer Distributed Content Caching for VANET Infotainment Applications