Abstract
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.
Highlights
The world’s population is moving towards urbanization
Upon receiving the requests of Internet of Things (IoT) users, those requests were executed in the neighboring fog nodes or needed to be forwarded to the cloud platform based on the availability of the computing resources as given in Equation (17)
In our simulation we considered IoT devices of task requests with different data size ranging from 103 ∼ 10 × 103 (Mega Bytes), which were randomly offloaded to the fog and cloud computing platform
Summary
The world’s population is moving towards urbanization. According to the United Nations. The governments take steps to make hassle free transportation by embedding smart, sophisticated and reliable systems, which can be used in assisting passengers and encouraging private vehicle users to utilize the well organized public transportation system. A fog computing environment is used for data processing and to assist passengers, where the fog node data centers could be deployed in distributed computing environment closer to the end user’s location These fog nodes enable IoT data computation with low-latency as compared to the conventional centralized cloud computing [9]. The fog computing nodes are hosted with intelligent algorithms to analyze the user’s request based on the input IoT data from various locations of a smart city. The RL based Q-learning algorithm will be a better approach to assist the passengers in smart transportation system, which is used by most of the research works in minimizing the waiting time
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