A Multilevel Authentication Protocol (MAP) for Human Safety in VANET

Meddeb, Nesrine; Meddeb-Makhlouf, Amel; Ayed, Mohamed Ali Ben

doi:10.1109/iwcmc.2018.8450533

Cited by 1 publication

(2 citation statements)

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“…For this reason, the proposed mechanism has been designed to ensure the transmission of video with the highest quality, as well as to reduce the footprint of network overload. The SETV mechanism is an improvement in our precedent work [3]. The new architecture features, and key enhancements are discussed below.…”

Section: Setv: Secure and Efficient Video Transmission In Vanetmentioning

confidence: 99%

“…After analyzing the problems mentioned above, we put a solution called "SETV" which takes into consideration the criteria of the VANET network and driven by QoE to guarantee high resolution video transmission. It is based on our previous work, published in [3]. SETV uses the "Ant Colony Optimization (ACO)" [4] to solve computational problems in real-time since this probabilistic algorithm is simulated according to the behavior of the ant.…”

Section: Introductionmentioning

confidence: 99%

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Secure and Efficient Video Transmission in VANET

Meddeb¹,

Meddeb-Makhlouf²,

Ayed³

2022

Int. j. commun. netw. inf. secur.

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Currently, vehicular communications have become a reality used by various applications, especially applications that broadcast video in real time. However, the video quality received is penalized by the poor characteristics of the transmission channel (availability, non-stationarity, the ration of signal-to-noise, etc.). To improve and ensure minimum video quality at reception, we propose in this work a mechanism entitled “Secure and Efficient Transmission of Videos in VANET (SETV)”. It's based on the "Quality of Experience (QoE)" and using hierarchical packet management. This last is based on the importance of the images of the stream video. To this end, the use of transmission error correction with uneven error protection has proven to be effective in delivering high quality videos with low network overhead. This is done based on the specific details of video encoding and actual network conditions such as signal to noise ratio, network density, vehicle position and current packet loss rate (PLR) not to mention the prediction of the future DPP.Machine learning models were developed on our work to estimate perceived audio-visual quality. The protocol previously gathers information about its neighbouring vehicles to perform distributed jump reinforcement learning. The simulation results obtained for several types of realistic vehicular scenarios show that our proposed mechanism offers significant improvements in terms of video quality on reception and end-to-end delay compared to conventional schemes. The results prove that the proposed mechanism has showed 11% to 18% improvement in video quality and 9% load gain compared to ShieldHEVC.

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Section: Setv: Secure and Efficient Video Transmission In Vanetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%