Best nodes approach for alert message dissemination in VANET (BNAMDV)

Vehicular ad hoc network (VANET) has gained much attention recently to improve road safety, reduce traffic congestion, and enable efficient traffic management because of its many important applications in transportation. In this paper, an early warning intelligence broadcasting algorithm is proposed, EW-ICAST, to disseminate a safety message for VANETs. The proposed EW-ICAST uses not only the early warning system on the basis of time to collision (TTC) but also the intelligent broadcasting algorithm on the basis of fuzzy logic. Thus, the EW-ICAST resolves effectively broadcast storm problem and meets time-critical requirement. The performance of EW-ICAST is evaluated through simulation and compared with that of other alert message dissemination algorithms. From the simulation results, we know that EW-ICAST is superior toSimple,P-persistence, andEDBalgorithms.

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A New Alert Message Dissemination Protocol for VANETs and Simulation in a Real Scenario

International Conference on Information Technology and Communication Systems - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-64719-7_18 ◽

2017 ◽

pp. 205-218

Author(s):

Hamid Barkouk ◽

El Mokhtar En-Naimi

Keyword(s):

Message Dissemination ◽

Alert Message ◽

Real Scenario

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Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

Future Internet ◽

10.3390/fi14010012 ◽

2021 ◽

Vol 14 (1) ◽

pp. 12

Author(s):

Mazen Alowish ◽

Yoshiaki Shiraishi ◽

Masami Mohri ◽

Masakatu Morii

Keyword(s):

Behavior Analysis ◽

Driver Behavior ◽

Environmental Parameters ◽

Network Simulator ◽

Correlation Clustering ◽

Layered Architecture ◽

Message Dissemination ◽

Search Optimization ◽

Alert Message ◽

And Performance

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

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