Crowding in city public transportation systems is a primary issue that causes delay in the mobility of passengers. Moreover, scheduled and unscheduled events in a city lead to excess crowding situations at the metro or bus stations. The Internet of Things (IoT) devices could be used for data collection, which are related to crowding situations in a smart city. The fog computing data centers located in different zones of a smart city can process and analyze the collected data to assist the passengers how to commute smoothly with minimum waiting time in the crowded situation. In this paper, Q-learning based passengers assistance system is designed to assist the commuters in finding less crowded bus and metro stations to avoid long queues of waiting. The traffic congestion and crowded situation data are processed in the fog computing data centers. From our experimental results, it is found that our proposed method can achieve higher reward values, which can be used to minimize the passengers’ waiting time with minimum computational delay as compared to the cloud computing platform.
Wastage of perishable and non-perishable products due to manual monitoring in shopping malls creates huge revenue loss in supermarket industry. Besides, internal and external factors such as calendar events and weather condition contribute to excess wastage of products in different regions of supermarket. It is a challenging job to know about the wastage of the products manually in different supermarkets region-wise. Therefore, the supermarket management needs to take appropriate decision and action to prevent the wastage of products. The fog computing data centers located in each region can collect, process and analyze data for demand prediction and decision making. In this paper, a product-demand prediction model is designed using integrated Principal Component Analysis (PCA) and K-means Unsupervised Learning (UL) algorithms and a decision making model is developed using State-Action-Reward-State-Action (SARSA) Reinforcement Learning (RL) algorithm. Our proposed method can cluster the products into low, medium, and high-demand product by learning from the designed features. Taking the derived cluster model, decision making for distributing low-demand to high-demand product can be made using SARSA. Experimental results show that our proposed method can cluster the datasets well with a Silhouette score of ≥60%. Besides, our adopted SARSA-based decision making model outperforms over Q-Learning, Monte-Carlo, Deep Q-Network (DQN), and Actor-Critic algorithms in terms of maximum cumulative reward, average cumulative reward and execution time.
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