Multi-agent based governance model for Machine-to-Machine networks in a smart parking management system

Proposed in this paper is a multi-agent model that defines a set of global functioning rules for a flexible governance, adapted to parking management within a city. This is designed to aid drivers in finding a parking place, which satisfies a group of criteria, predefined in profiles, providing a better parking service to the public. The Multi-Agent model developed is integrated in the platform SensCity, which is dedicated to the development and deployment of Machine-to-Machine (M2M) systems. The city is divided into a number of parking areas that are equipped with sensors, which are responsible for transferring data from and to the parking places. Therefore, the agents can work to interpret and manipulate the governance principles modeled and implemented by the multi-agent model, independently from drivers and parking spaces. Moreover, this paper proposes an intelligent end-to-end management of parking system using the MOISE organization framework.