Driver Behavior Analysis in Connected Vehicles

Title:- Intersection Aware Routing, Driver Behavior Analysis and Secure Safety Message Dissemination Using Intellectual Approaches in Fog Enabled IoCV Environment
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Implementation Plan:
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Step 1: We create a Network , it consists of 4- Edge assisted RSUs, 2- fog servers , 1-cloud server , 1- blockchain node,1-Certificate Authority (CA) and 100- vehicles based on the sumo traffic model.

Step 2: we construct the mobility,road area and map based on the SUMO.

Step 3: Initially , Register the vehicles by consider ID, PUF and MAC address, and also register the ERSU by consider ID and
location, and also Driver will consider the current date and age. After registration the Certificate Authority (CA) provides the secret key by using Chebyshev Chaotic Map (CCM). All the information is stored in the blockchain for enhancing security.

Step 4: Next , The ERSU clusters the legitimate vehicles based on distance, direction, velocity, RSSI by using improved DBSCAN (IDBSCAN) algorithm and also selects the CH based on distance, direction, and link stability.

Step 5: Next , Detect the lane change by considering neighbor vehicle distance, direction, and history of lane changes by using Improved Kalman filter (IKF) which classifies the lane changes into two classes such as change, and no change.

Step 6: Next , Perform the route selection by using a Multi-Agent based TD3 algorithm (MATD3).

Step 7: Next, Fog nodes perform driver behavior analysis by using Advanced LSTM (ALSTM) .

Step 8: Next, The optimal fog nodes were selected for safety message dissemination by using Driver Training based Optimization algorithm (DTOA). Here, the safety messages are encrypted using CCM algorithm, the encrypted messages are sent to the vehicles.The encrypted information is stored in the blockchain to increase security.


[The process based on your proposal :- Intersection Aware Routing, Driver Behavior Analysis and Secure Safety Message Dissemination Using Intellectual Approaches in Fog Enabled IoCV Environment ]

Step 9: Finally the Performance metrics is defined as follows, Motion Prediction Error vs. Number of Vehicles,Latency vs. Number of Vehicles,Number of alerts vs. Number of Vehicles,Vehicle motion detection accuracy vs. Vehicle Speed,Number of risk maneuvers vs. Number of Vehicles,Security strength vs. Number of Vehicles and Behavior Detection Accuracy vs. Number of Drivers.

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s/w req:
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1) Tool: omnet-4.6
2) sumo 0.19.0
3) OS: Windows 10 – (64bit)


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Note ;-
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We perform the EXISTING process based on the REFERENCE Title: - Three Layered Architecture for Driver Behavior Analysis and Personalized Assistance with Alert Message Dissemination in 5G Envisioned Fog-IoCV

Need Software & Installation :

1) OS: Windows 7 [ultimatate -x86]

2) Install Omnet 4.6

3) Then paste the sumo-0.19.0 folder in C: drive

Excecution Procedure:

1 ) Open Omnet++ (follow running procedure)

2 ) File- Import - Your Project Code Location

3 ) Build Project

4 ) Run As Omnet++ Simulation