Modeling and Simulation of Smart Parking Management in IoT Cities
Implementation plan:
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Step 1: Initially, We create car parking simulation environment with 10 cars .
Step 2: Then, we collect the vehicle data such as position of vehicle in parking lot with time.
Step 3: Next, we register the car users data and secure the data using ECC and LDP cryptographic techniques.
Step 4: Next, we manage the pricing system using Stackelberg leader-follower game framework (SL-FGF) method for parking availability..
Step 5: Next, we implement Acar Agent together with Multi-Criteria Decision Making (AcarA-MCDM) methods to optimize parking space allocation.
Step 6: Next, we predict the parking slot using (CNN-LSTM-LOA)Algorithm and allocate the vehicles using the Intelligent Parking Reservation System (IPRS).
Step 7:Next, we detect mis-parking behaviors and GPS navigation to guide drivers to designated spots using Autoencoders.
Step 8: Finally, we plot performance metrics for the following
8.1: Number of Cars vs processing time (S)
8.2: Number of Cars vs Overhead (KB)
8.3: Number of Cars vs Detection Accuracy (%)
8.4: Allocated Time vs. Efficiency (%)
Software Requirements:
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1. Development Tool: Matlab-R2024a
2. Operating System: Windows-11 (64-bit)
Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
2) Please note that this implementation plan does not include any further steps after it is put into implementation.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only, not a real time project.
5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.
We perform with an existing approach Reference 4:-Title: Deep Learning-Based Mobile Application Design for Smart Parking
