Towards the Internet of Behaviors in airports with a fog-to-cloud approach
Antonio Salis

TL;DR
This paper presents a fog-to-cloud IoB system for airports that enhances traveler assistance, optimizes airport operations, and adapts to pandemic safety needs using machine learning and privacy-aware data handling.
Contribution
It introduces a novel fog-to-cloud architecture for IoB in airports, integrating real-time traveler assistance, infrastructure monitoring, and privacy considerations.
Findings
Improved traveler experience through personalized recommendations.
Enhanced airport management with heat maps and bottleneck detection.
Potential for social distancing enforcement during pandemics.
Abstract
Recent advances in Internet of Things (IoT) and the rising of the Internet of Behavior (IoB) have made it possible to develop real-time improved traveler assistance tools for mobile phones, assisted by cloud-based machine learning, and using fog computing in between IoT and the Cloud. Within the Horizon2020-funded mF2C project an Android app has been developed exploiting the proximity marketing concept and covers the essential path through the airport onto the flight, from the least busy security queue through to the time to walk to gate, gate changes, and other obstacles. It gives chance to travelers to discover the facilities of the airport, aided by a recommender system using machine learning, that can make recommendations and offer voucher according with the traveler's preferences or on similarities to other travelers. The system provides obvious benefits to the airport planners,…
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Taxonomy
TopicsAviation Industry Analysis and Trends · Air Traffic Management and Optimization · Human Mobility and Location-Based Analysis
