A Stylized Presence Detection System in the Era of Blockchain and Big Data

Anastasios Alexandridis, Ghassan Al-Sumaidaee (), Rami Alkhudary () and Zeljko Zilic
Additional contact information
Anastasios Alexandridis: McGill University = Université McGill [Montréal, Canada]
Ghassan Al-Sumaidaee: McGill University = Université McGill [Montréal, Canada]
Rami Alkhudary: LARGEPA - Laboratoire de recherche en sciences de gestion Panthéon-Assas - Université Paris-Panthéon-Assas
Zeljko Zilic: McGill University = Université McGill [Montréal, Canada]

Post-Print from HAL

Abstract: The concept of smart cities has gained popularity due to technological advances in areas such as the Internet of Things (IoT) and Big Data Analytics (BDA). Location-based services have emerged in such smart environments to improve people's quality of life and generate statistics for mutual benefit. In this work, a stylized presence detection concept is proposed which uses Bluetooth Low Energy (BLE) beacons placed in locations of interest. Users can detect the BLE beacon identification number (ID) with personal devices such as cell phones and connected watches and transmit it along with a unique and randomly generated user ID. Blockchain technology is used for a storage back-end. Our proposal is by no means exhaustive and is intended to advance the discussion of location-based services that deal with big data.

Date: 2022-12-17
References: Add references at CitEc
Citations:

Published in 2022 IEEE International Conference on Big Data (Big Data), Dec 2022, Osaka, Japan. IEEE, 2022 IEEE International Conference on Big Data (Big Data), pp.6575-6577, ⟨10.1109/BigData55660.2022.10020239⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04036467

DOI: 10.1109/BigData55660.2022.10020239

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().