Beacon-Based Cluster Framework for Internet of People, Things, and Services (IoPTS)

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Abstract

Internet of Things (IoT) applications can offer a great potential of benefits to end users. We are moving towards an Internet of People, Things and Services (IoPTS) and a future world of ambient intelligence, where users can seamlessly interact with IoT devices and get information about the services they offer. Interaction can take place directly or through dedicated mobile apps that are built to acquire data from sensors or clusters of IoT devices. The article presents a user-centric cluster framework, taking into account the resource-constrained nature of the IoT devices. As part of the framework, the authors propose a Mobility Aware Clustering Scheme (MACS), which organizes the IoT devices in clusters using a fitness function for each node, and a Profile Aware Proactive Cluster Discovery (PAPCD) mechanism using Bluetooth Low Energy (BLE) beacons for service advertisement. The authors also propose an IoT service filtering and ranking algorithm to match the users' preferences and requirements.
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Internet of Things (IoT) applications can offer a great potential of benefits to end users. We are moving towards an Internet of People, Things and Services (IoPTS) and a future world of ambient intelligence, where users can seamlessly interact with IoT devices and get information about the services they offer. Interaction can take place directly or through dedicated mobile apps that are built to acquire data from sensors or clusters of IoT devices. The article presents a user-centric cluster framework, taking into account the resource-constrained nature of the IoT devices. As part of the framework, the authors propose a Mobility Aware Clustering Scheme (MACS), which organizes the IoT devices in clusters using a fitness function for each node, and a Profile Aware Proactive Cluster Discovery (PAPCD) mechanism using Bluetooth Low Energy (BLE) beacons for service advertisement. The authors also propose an IoT service filtering and ranking algorithm to match the users' preferences and requirements.
Original languageEnglish
Article number2
JournalInternational Journal of Ambient Computing and Intelligence
Volume9
Issue number4
Pages (from-to)15-33
Number of pages19
ISSN1941-6237
DOI
Publication statusPublished - 2018
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • Internet of Things, user preferences, clustering, service discovery, service framework
ID: 285027292