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 that airports tend to entertain travelers with. 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, not only people tracking in the shops area, but also aggregated and anonymized view, like heat maps that can highlight bottlenecks in the infrastructure, or suggest situations that require intervention, such as emergencies. With the emerging of the COVID pandemic the tool could be adapted to help in the social distancing to guarantee safety. The use of the fog-to-cloud platform and the fulfilling of all centricity and privacy requirements of the IoB give evidence of the impact of the solution. Doi: 10.28991/HIJ-2021-02-04-01 Full Text: PDF
Highlights
While the diffusion of the Internet of Things (IoT), as an environment that interconnects an ever-growing number of heterogeneous physical things such as appliances, facilities, vehicles, sensors, etc., to the internet to provide sophisticated applications built with these data [1, 2], is continuously proposing new applications and services, the new Internet of Behavior (IoB) has been proposed by Gartner as an extension of IoT, that collects the digital tracks of people lives from a multitude of sources, determining people’s attitudes, their interests, preferences and regular habits and practices, and these information could reveal significant information on themselves and can be used to influence their behavior
Given the need to spot an environment for IoB implementation, the analysis has been focused on parts of a smart city, like airports, train stations, hospitals, malls and related parking areas, where there is a concentration of devices, in our case users smartphones, and setup gateways and any other processing elements able to track and engage people in these places, and developing value added services for proximity marketing, with suggestions on best sites to visit, prediction of behavior and movements of consumers, and taking real time decisions, showing in practise the IoB principles
The data-driven approach derived from the IoT is the perfect enabler for the IoB adoption, at the same time it is possible to merge users’ data coming from different fog areas in the smart city, boosting the IoB effectiveness
Summary
While the diffusion of the Internet of Things (IoT), as an environment that interconnects an ever-growing number of heterogeneous physical things such as appliances, facilities, vehicles, sensors, etc., to the internet to provide sophisticated applications built with these data [1, 2], is continuously proposing new applications and services, the new Internet of Behavior (IoB) has been proposed by Gartner (https://www.gartner.com/smarterwithgartner/gartnertop-strategic-technology-trends-for-2021/) as an extension of IoT, that collects the digital tracks of people lives from a multitude of sources, determining people’s attitudes, their interests, preferences and regular habits and practices, and these information could reveal significant information on themselves and can be used to influence their behavior. Location independence and the ability to operate from anywhere will constitute a major shift in terms of business, requiring a secured distributed cloud processing environment with fast connections, enabling a composable business and leveraging advanced ML/AI technology to enhance the ability to adapt under changing conditions To support such a challenging shift, the straight "Cloudification" of IoT is problematic, since the approach of transferring all data from the device to the cloud, hosted in remote data centers, generates considerable latency and a large computational load and storage with sensible economic costs. This manuscript is structured as follows: Section II introduces the research questions, the Fog-to-cloud approach and the mF2C system developed within the project; Section III provides a description of the airport use case, its unique proposition, taking advantage of the mF2C platform and fulfilling the IoB concept; Section IV describes in details the deployment in the airport and experimental performance results; Section V describes the benefits, outcomes and airport managers and ICT/Telco providers' exploitation opportunities; Section VI describes the relevance of present work, future work and concludes the paper
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
