Learning automata and reservation based secure smart parking system: Methodology and simulation analysis

Abstract

The increase in the number of vehicles on roads has compounded the difficulty in parking them when people go out for movies, shopping, theatre, etc. Thus, this paper proposes smart parking system. The reservation of the parking slots can be made using this proposed smart parking system. Unlike the methods proposed in the existing research, the system proposed in this paper divides the parking area into 3 parts. One for conventional parking, one for vehicles with reservation and the other for the vehicles with and without reservation. Learning automata is used in the proposed system to determine the percentage of the parking area for conventional parking. In general, the proportionate of the slots for reserved parking, conventional parking and common slots need to be assumed. Learning Automata helps in determining this proportionate optimally. The AES-256 encryption algorithm is used to provide security for the details provided by the user during reservation process. Goodput value is maintained for each vehicle which increases or decreases the chances of getting a reservation. The time limit for the reservation of the parking slot is also maintained after which the reservation gets cancelled automatically. Markov Model is used to represent the system. The performance of the proposed algorithm, Learning Automata and Reservation based Secure Smart Parking System (LA-RSSPS) is simulated and evaluated in terms of average waiting time, search time, the probability with which the vehicles do not get parking slot when they do not have reservation and the probability with which the vehicles do not get reservation and is compared with ProNet and iERS. Results have shown that our scheme preforms better than the ProNet and iERS competing schemes. The comparison of analytical and simulations results are also presented.