S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots
نویسندگان
چکیده
In this article, we present a Q-learning-enabled safe navigation system-S-Nav-that recommends routes in road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from commuters path for traveling. The system dodges hotspots ensures minimal passage them unavoidable situations. This feature of reduces commuter's risk getting exposed to these contaminated zones contracting virus. To achieve this, formulate reward function reinforcement learning model imposing zone-based penalties demonstrate that achieves convergence under all conditions. ensure real-time results, propose an Internet Things (IoT)-based architecture incorporating cloud fog computing paradigms. While is responsible training on large networks, geographically aware nodes take results retrain based smaller networks. Through extensive implementation experiments, observe reliable paths near real time. contrast state-of-the-art techniques, limits red/orange almost 2% close 100% green zones. However, 18% additional travel distances compared precarious shortest paths.
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ژورنال
عنوان ژورنال: IEEE Internet of Things Journal
سال: 2021
ISSN: ['2372-2541', '2327-4662']
DOI: https://doi.org/10.1109/jiot.2020.3037641