Large urban areas face challenges that are able to be solved in the form of smart cities. The growing urban web necessitates the use of electronic media and services to serve its residents. At the same time the population growth in urban web and is estimated to be 70% of the total population of the earth until 2050. Thus it is critical to manage urban green effectively under climate change.
Urban green encompasses all areas of vegetation that contribute to oxygen production, contribute positively to the aesthetics of the city and produce the necessary fruit and vegetables. IoT refers to a multitude of sensor devices that provide the necessary information for managing the system through broadband connections. Along with sensors, the system will receive rss feeds from data services, wherever this is feasible, in order to reduce the cost of the sensors. This process will reduce response time and costs, resulting in a more techno-economically efficient urban green management.
The contribution of this PHD will be the development of a mathematical model to the management of the system, in order to improve the speed of transmission of information by transmitting only useful information as appropriate. This mathematical model will also include an algorithm that enables self-learning through feedback, thereby reducing human intervention in decision making.