Smart city fire surveillance: A deep state-space model with intelligent agents

In the realm of smart city development, the integration of intelligent agents has emerged as a pivotal strategy to enhance the efficacy of search methodologies. This study introduces a novel state-space navigational model employing intelligent agents tailored specifically for fire surveillance in urban environments. Central to this model is the fusion of a convolutional neural network and multilayer perceptron, enabling accurate fire detection and localisation. Leveraging this capability, the intelligent agent proactively navigates through the search space, guided by the shortest path to the identified fire location. The utilisation of the A* algorithm as the search mechanism underscores the efficiency and efficacy of our proposed approach. Implemented in Python and Gephi, our method surpasses traditional search algorithms, both informed and uninformed, demonstrating its effectiveness in navigating urban landscapes for fire surveillance. This research study contributes significantly to the field by offering a robust solution for proactive fire detection and surveillance in smart city environments, thereby enhancing public safety and urban resilience. This research study introduces a state-space navigational model using intelligent agents, combined with a convolutional neural network and multilayer perceptron, for efficient fire surveillance in smart cities. The model proactively guides agents through the shortest path to a fire, utilising the A & lowast; algorithm. Comparative analysis with other algorithms shows the proposed method's effectiveness in providing swift navigation. image