For several years, the population of cities has continued to multiply at a rapid pace. The main cause of this phenomenon in developing countries like Morocco is the rural exodus. In fact, rural youth are increasingly attracted by the modern way of life and the opportunities of employment offered by cities. This increase in population density has a large number of negative effects on the quality of life in the city. The most obvious is the intensity of the traffic, which has become an almost insurmountable problem and which causes a great deal of damage, such as the increase in the number of accidents that cause serious bodily harm to the road users, the pollution caused by the large amount of CO₂ released by the vehicles, and the continuous stress of drivers who must drive in often narrow and very busy roads and who must look for a long time to find a space to park. Thus, to solve the parking problem, several modern technologies have been created to equip car parks with smart devices that help road users identify the nearest car park that has a free space. These technologies most often use wireless sensor networks and Internet of Things (IoT) technology. In this paper, we present the design and development of a smart parking system using the latest technologies based on wireless sensor networks (WSN). Our system uses an adaptable and hybrid self-organization algorithm for wireless sensor networks that adapts to all types of car parks existing in the city (linear and mass parking), and offers a better management of the energy consumption during the wireless communication to increase the lifetime of the sensor nodes and the longevity of the WSN. This system also offers innovative services which facilitate the task to the drivers when looking for an available parking space in the city near their destination, in a fast and efficient manner.

Wireless sensor networks (WSNs) are small, autonomous, battery-powered nodes capable of sensing, storing, and processing data, while communicating wirelessly with a central base station (BS). Optimizing energy consumption is a major challenge to extend the lifetime of these networks. In this study, we propose an innovative approach combining the k-nearest neighbors (KNN) algorithm with hierarchical and flat routing protocols to improve node selection and clustering in three key protocols: low-energy adaptive clustering hierarchy (LEACH), threshold-sensitive energy efficient sensor network protocol (TEEN), and hybrid energy-efficient distributed clustering (HEED). Concretely, KNN is used to rank nodes based on their spatial and energy proximity, thus optimizing the choice of cluster heads (CHs) and reducing long and costly connections. Simulations show a reduction in the inter-CH distance, a decrease in overall energy consumption, and an extension of the network lifetime compared to conventional versions of the protocols. These improvements not only help increase operational efficiency, but also enhance communications stability and security, providing a robust and sustainable solution for critical WSN applications.