Benefits of a Fog-to-Cloud Approach in Proximity Marketing

Salis, Antonio; Mancini, Glauco; Bulla, Roberto; Cocco, Paolo; Lezzi, Daniele; Lordan, Francesc

doi:10.1007/978-3-030-10549-5_19

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Looking at the airport field collecting and sharing data on customer behavior can improve the stretch of marketing offering, even with the identity of customers is protected or unknown, using a smart fog gateway embedding cloud connectivity to process large amount of data or request extra data, even data coming from other fogs located in nearby places such as train or main bus station, in order to add value. The final deployed solution includes a new app based on Android, with an indoor navigator and recommender advisor [9][10][11], driven by machine learning algorithms, providing travelers with a more enjoyable and stress-free experience in the field. The proposed solution integrates all information that airports already provide through voice announcements, information kiosks and digital monitors, and uses a detailed map of the area, together with the list of available services represented by Points of Interest (POIs), such as restrooms, shops, duty-free areas, information desks, departure gates.…”

Section: The Airport Use Casementioning

confidence: 99%

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Salis¹

2021

HighTech. Innov. J.

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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

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Section: The Airport Use Casementioning

confidence: 99%

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Salis¹

2021

HighTech. Innov. J.

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“…On the other hand, there are application-specific benefits that placing devices in situ bring. A factory often lacks the necessary infrastructure for a proper data centre, an airport may have its own data centre but require smart sensing devices to analyse data from customers or even manage point-of-sale terminals [19]. Even with the lack of local device redundancy, in an event of a wider network outage, the local network could be retained, offering a limited set of functionalities locally.…”

Section: Levels Of Management Of Edge Architecturesmentioning

confidence: 99%

On the Similarities and Differences Between the Cloud, Fog and the Edge

Stanovnik

Cankar

2020

Euro-Par 2019: Parallel Processing Workshops

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The field of edge and fog computing is growing, but there are still many inconsistent and loosely-defined terms in current literature. With many articles comparing theoretical architectures and evaluating implementations, there is a need to understand the underlying meaning of information condensed into fog, edge, and similar terms. Through our review of current literature, we discuss these differences and extract key characteristics for basic concepts that appear throughout. The similarities to existing IaaS, PaaS and SaaS models are presented, contrasted against similar models modified for the specifics of edge devices and workloads. We also evaluate the different aspects existing evaluation and comparison works investigate, including the compute, networking, storage, security, and ease-of-use capabilities of the target implementations. Following that, we make a broad overview of currently available commercial and open-source platforms implementing the edge or fog paradigms, identifying key players, successful niche actors and general trends for featurelevel and technical development of these platforms.

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“…These features have fascinated many IoT applications to adopt edge computing and benefit from it. Smart fog hub services (SFHS) is an edge-tocloud platform deployed in Cagliari Airport in 2019 to provide a user-friendly and indoor map to the passengers to get their way to preferred shops, restaurants or any services during their waiting time in the airport [6]. This application promises the best customer experience and increases revenue for the airport and service providers.…”

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confidence: 99%

A data quarantine model to secure data in edge computing

Mahadevappa

Murugesan

2022

IJECE

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<p>Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, linear discriminant analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.</p>

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Benefits of a Fog-to-Cloud Approach in Proximity Marketing

Cited by 6 publications

References 5 publications

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

Towards the Internet of Behaviors in Smart Cities through a Fog-To-Cloud Approach

On the Similarities and Differences Between the Cloud, Fog and the Edge

A data quarantine model to secure data in edge computing

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