Sensors Positioning in IoT-Based Smart Parking Systems with Grasshopper Optimization Algorithm

Considering the growth of the population of cities and the number of vehicles that are increasing exponentially, a challenge in parking lots is the positioning of vehicles. In a smart parking system, the driver can park without delay and by spending less energy; But its requirement is to use sensors (empty parking spaces) and parking guides for this purpose. With the progress of research in the Internet of Things, researchers have provided promising solutions in the smart parking system based on wireless sensors. Among these researches is the use of the gray wolf algorithm (GWO) in the optimal positioning of wireless sensors in the Internet of Things parking environment. In this article, due to the search power and high convergence of the Grasshopper Optimization Algorithm (GOA), this algorithm is used for the first time in the positioning of wireless sensors in the parking lot. The grasshopper optimization algorithm is used to determine the best anchor nodes to collect data from other sensors; So that it can reduce the positioning error and energy consumption of the sensors and increase their lifespan. The results showed that the proposed method was able to achieve an average improvement of 5.92% in reducing the positioning error, 6.43% in reducing the amount of energy consumption and 23.6% in increasing the lifetime of the network compared to the gray wolf algorithm. Also, the proposed method has been able to have more time for the first node to die, and this is an important advantage in smart parking because the efficiency of all sensors in the parking environment is required.