Transmission power adaption scheme for improving IoV awareness exploiting: evaluation weighted matrix based on piggybacked information

Abstract As part of the new era the Internet of Things, an evolved form of Vehicle Ad-hoc Networks has recently emerged as the Internet of Vehicles (IoV). IoV has obtained a lot of attention among smart vehicle manufactures and illustrations due to its promising potential, but there are still some problems and challenges that need to be addressed. Transmission error occurs when an emergency message is disseminated to provide traffic awareness, and vehicles have to increase their channel transmission power to ensure further coverage and mitigate possible accidents. This might cause channel congestion and unnecessary power consumption due to an inaccurate transmission power setup. A promising solution could be achieved via periodically and predictively evaluating channel and GEO information that is transmitted over piggybacked beacons. Thus, in this paper we propose a Transmission Power Adaptation (TPA) scheme for obtaining better power tuning, which senses and examines the probability of channel congestion. Afterwards, it proactively predicts upcoming channel statuses using developed evaluation-weighted matrix, which observes correlations between coefficients of variance for estimated metrics. Considering beacon transmission error rate, crowding inter-vehicle distance, and channel delay, the matrix is periodically constructed and proavtively weighted for each metric based on a predefined threshold value. Eventually, predicted channel status is used as an indicator to adjust transmission power. This leads to decreased channel congestion and better awareness in IoV. The performance of the proposed TPA scheme is evaluated using OMNeT++ simulation tools. The simulation results show that our proposed TPA scheme performs better than existing method in terms of overall throughput, average beacon congestion rate, beacon recipient rate probabilities, channel-busy time, transmission power over distance, and accident probabilities.