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In this paper, we present a Q-learning-enabled safe navigation system –S-Nav –that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations This feature of S-Nav reduces the commuter’s risk of getting exposed to these contaminated zones and contracting the virus To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms While the cloud is responsible for training on large road networks, the geographically-aware fog nodes take the results from the cloud and retrain them based on smaller road networks Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real-time In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones However, we observe 18% additional travel distances compared to precarious shortest paths IEEE
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