ML-based Minimization of AoI in a Vehicular Communication Network

Prabhakaran, Prarthana; Chavhan, Suresh; Kumar, Manish; Rodrigues, Joel J. P. C.

doi:10.23919/splitech58164.2023.10193386

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2024

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Cited by 3 publications

References 19 publications

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Edge-Intelligence based Federated Learning in the Internet of Medical Things

Chavhan,

Srinitha,

Kathat

et al. 2024

2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)

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Edge-Intelligence based Federated Learning in the Internet of Medical Things

Chavhan,

Srinitha,

Kathat

et al. 2024

2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)

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Design and Development of Drone Seed Dispersal Mechanism using Novel Narcondam Hornbill Algorithm in Barren Lands

Chavhan,

Srinitha,

Kathat

et al. 2024

2024 9th International Conference on Smart and Sustainable Technologies (SpliTech)

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Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels

Zhang,

Ji,

et al. 2024

Sensors

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In the Internet of Vehicles (IoV), age of information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately consider the queue characteristics. Moreover, vehicle mobility leads to rapid changes in network topology and channel conditions, making it difficult to accurately reflect the unique characteristics of vehicles with the calculated AoI under ideal channel conditions. This paper examines the impact of Doppler shifts caused by vehicle speeds on data transmission in error-prone channels. Based on the M/M/1 and D/M/1 queuing theory models, we derive expressions for the age of information and optimize the system’s average AoI by adjusting the data extraction rates of vehicles (which affect system utilization). We propose an online optimization algorithm that dynamically adjusts the vehicles’ data extraction rates based on environmental changes to ensure optimal AoI. Simulation results have demonstrated that adjusting the data extraction rates of vehicles can significantly reduce the system’s AoI. Additionally, in the network scenario of this work, the AoI of the D/M/1 system is lower than that of the M/M/1 system.

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ML-based Minimization of AoI in a Vehicular Communication Network

Cited by 3 publications

References 19 publications

Edge-Intelligence based Federated Learning in the Internet of Medical Things

Edge-Intelligence based Federated Learning in the Internet of Medical Things

Design and Development of Drone Seed Dispersal Mechanism using Novel Narcondam Hornbill Algorithm in Barren Lands

Optimizing Age of Information in Internet of Vehicles over Error-Prone Channels

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