Abstract

The concept of vehicular platooning is emerging due to its self-driving capability, whereas a set of cars are arranged closely and that can drive safely even in high speed. Moreover the communication can also takes place between two platoons and hence the platoon leader can control the speed and direction. Traffic difficulties, like as collisions and delay at road junctions, need the use of platoon mechanisms. The most efficient vehicle traffic management in platoons is acknowledged in Intelligent Transportation System (ITS) for improving energy efficiency, road capacity, and road safety. Essentially, it is critical to analyse the connection characteristic of any two platoons in order to improve connectivity. As a result, under bi-directional platoon architecture, the possibility of connectivity between platoon members and Road Side Units (RSUs) is generated. Providing Quality of Service (QoS) to automotive network is difficult issue because elements such as asynchronous transmission or high mobility, crowded and fragmented channels, hardware defects, and various vehicular gadgets. As a result, it is extremely desired to utilize and distribute resources efficiently. Despite the existence of other bio-inspired meta-heuristic optimization methods, the Bacterial Foraging Algorithm has the benefit of simplicity and efficiency, and so may be used to a wide range of engineering applications. However, in the event of sophisticated analysis, the constraint is a lower convergence rate. To improve resource allocation, this study employs the Improved Bacterial Foraging Optimization based Channel Allocation (IBFOCA) technique. Set of channels allocated to platoon members are signified as bacteria’s position. By using latency, Signal-to-Interference Ratio (SINR) and Quality of Service (QoS), available channel is initially determined. This approach is used to find a channel that is acceptable for platoon members. Simulation results are used to examine the presentation of multi-channel distributions in platoons utilizing suggested optimization strategy. Signal-to-interference ratio is lowered by 29 decibel, throughput is raised by 69 kilobits per second, collision probability is reduced by 21.4 percent, and average batch size is 57 percent with a delay of 49 milliseconds.

Full Text
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