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Fires in smart cities can have devastating consequences, causing damage to property, and endangering the lives of citizens. Traditional fire detection methods have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. This paper proposes an improved fire detection approach for smart cities based on the YOLOv8 algorithm, called the smart fire detection system (SFDS), which leverages the strengths of deep learning to detect fire-specific features in real time. The SFDS approach has the potential to improve the accuracy of fire detection, reduce false alarms, and be cost-effective compared to traditional fire detection methods. It can also be extended to detect other objects of interest in smart cities, such as gas leaks or flooding. The proposed framework for a smart city consists of four primary layers: (i) Application layer, (ii) Fog layer, (iii) Cloud layer, and (iv) IoT layer. The proposed algorithm utilizes Fog and Cloud computing, along with the IoT layer, to collect and process data in real time, enabling faster response times and reducing the risk of damage to property and human life. The SFDS achieved state-of-the-art performance in terms of both precision and recall, with a high precision rate of 97.1% for all classes. The proposed approach has several potential applications, including fire safety management in public areas, forest fire monitoring, and intelligent security systems.

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Recently, many concepts in technology has been changed. According to the digital transformation trends, Internet of Things (IoT) represents an interested research issue. As the IoT grows, the data and the processes will need more space. The data in cases like healthcare, smart cities, autonomous vehicles, smart agriculture, etc. needs to be analyzed and processed in real-time. Cisco refers to the dependence of edge and cloud as “The Fog”. The data can be analyzed at the fog layer to maximize data utilization. This paper presents a new Effective Prediction and Resource Allocation Methodology (EPRAM) for Fog environment, which is suitable for Healthcare applications. Resource Allocation (RA) represents a hard mission as it involves a set of various resources and fog nodes to achieve the required computations for IoT systems. EPRAM tries to achieve effective resource management in Fog environment via real-time resource allocating as well as prediction algorithm. EPRAM is composed of three main modules, namely: (i) Data Preprocessing Module (DPM), (ii) Resource Allocation Module (RAM) and (ii) Effective Prediction Module (EPM). The EPM uses the PNN to predict a target field, using one or more predictors. In order to detect the probability of the heart attack, PNN is trained using the training dataset. Then PNN will be tested using the user’s sensing data coming from the IoT layer to predict the probability of heart attack and then take the most appropriate action accordingly. The main goal of the system is to achieve a low latency while improving the Quality of Service (QoS) metrics such as (the allocation cost, the response time, bandwidth efficiency and energy consumption). Unlike other RA techniques, EPRAM employs deep Reinforcement Learning (RL) algorithm in a new manner. It also uses the PNN for the prediction algorithm. It has achieved such acceptable performance due to using deep RL and PNN. Deep RL has shown impressive promises in resource allocation. PNN generates accurate predicted target and is much faster than multilayer perceptron networks. Comparing the EPRAM with the state-of-the-art algorithms, EPRAM achieved the minimum Makespan as compared to previous LB algorithms, while maximizing the Average Resource Utilization (ARU) and the Load Balancing Level (LBL). Accordingly, EPRAM is a suitable algorithm in the case of real-time systems in FC which leads to load balancing. ERAM is effective in monitoring and predicting the status of the patient accurately and quickly.