C3PO: A Network and Application Framework for Spontaneous and Ephemeral Social Networks

Boutet, Antoine; Frénot, Stéphane; Laforest, Frédérique; Launay, Pascale; Sommer, Nicolas Le; Mahéo, Yves; Reimert, Damien

doi:10.1007/978-3-319-26187-4_33

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…It uses Wi-Fi, Bluetooth, and Ethernet for underlying communication. A framework proposed in [27] enables mobile devices to communicate with each other via short-range wireless communication technologies such as Wi-Fi and Bluetooth. It introduces the concept of micronet and macronet.…”

Section: Middleware Platforms For Distributed Computing Systemsmentioning

confidence: 99%

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Shah

2021

Sensors

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Recent advances in mobile technologies have facilitated the development of a new class of smart city and fifth-generation (5G) network applications. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. A heterogeneous private edge cloud system was proposed to address the requirements of these applications. The proposed heterogeneous private edge cloud system is characterized by a complex and dynamic multilayer network and computing infrastructure. Efficient management and utilization of this infrastructure may increase data rates and reduce data latency, data privacy risks, and traffic to the core Internet network. A novel intelligent middleware platform is proposed in the current study to manage and utilize heterogeneous private edge cloud infrastructure efficiently. The proposed platform aims to provide computing, data collection, and data storage services to support emerging resource-intensive and non-resource-intensive smart city and 5G network applications. It aims to leverage regression analysis and reinforcement learning methods to solve the problem of efficiently allocating heterogeneous resources to application tasks. This platform adopts parallel transmission techniques, dynamic interface allocation techniques, and machine learning-based algorithms in a dynamic multilayer network infrastructure to improve network and application performance. Moreover, it uses container and device virtualization technologies to address problems related to heterogeneous hardware and execution environments.

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Section: Middleware Platforms For Distributed Computing Systemsmentioning

confidence: 99%

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Shah

2021

Sensors

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“…In [26], the authors implement a Bluetooth-based middleware to create opportunistic networks between passing users. On top of such enablers, the authors in [7] leverage real-world social phenomena such as large crowds at sports events for event-based mobile communication.…”

Section: Related Workmentioning

confidence: 99%

On gossip-based information dissemination in pervasive recommender systems

Eichinger

Beierle

Papke

et al. 2019

Proceedings of the 13th ACM Conference on Recommender Systems

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Pervasive computing systems employ distributed and embedded devices in order to raise, communicate, and process data in an anytime-anywhere fashion. Certainly, its most prominent device is the smartphone due to its wide proliferation, growing computation power, and wireless networking capabilities. In this context, we revisit the implementation of digitalized word-of-mouth that suggests exchanging item preferences between smartphones offline and directly in immediate proximity. Collaboratively and decentrally collecting data in this way has two benefits. First, it allows to attach for instance location-sensitive context information in order to enrich collected item preferences. Second, model building does not require network connectivity. Despite the benefits, the approach naturally raises data privacy and data scarcity issues. In order to address both, we propose Propagate and Filter, a method that translates the traditional approach of finding similar peers and exchanging item preferences among each other from the field of decentralized to that of pervasive recommender systems. Additionally, we present preliminary results on a prototype mobile application that implements the proposed device-to-device information exchange. Average ad-hoc connection delays of 25.9 seconds and reliable connection success rates within 6 meters underpin the approach's technical feasibility.

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“…The idea is to encourage social interaction. The authors of [31] developed a framework for ephemeral social networks. The goal is content dissemination in opportunistic networks in scenarios with large crowds like sports events.…”

Section: Related Workmentioning

confidence: 99%

Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems

Beierle¹,

Eichinger²

2019

Preprint

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Typically, recommender systems from any domain, be it movies, music, restaurants, etc., are organized in a centralized fashion. The service provider holds all the data, biases in the recommender algorithms are not transparent to the user, and the service providers often create lock-in effects making it inconvenient for the user to switch providers. In this paper, we argue that the user's smartphone already holds a lot of the data that feeds into typical recommender systems for movies, music, or POIs. With the ubiquity of the smartphone and other users in proximity in public places or public transportation, data can be exchanged directly between users in a device-to-device manner. This way, each smartphone can build its own database and calculate its own recommendations. One of the benefits of such a system is that it is not restricted to recommendations for just one user -ad-hoc group recommendations are also possible. While the infrastructure for such a platform already existsthe smartphones already in the palms of the users -there are challenges both with respect to the mobile recommender system platform as well as to its recommender algorithms. In this paper, we present a mobile architecture for the described systemconsisting of data collection, data exchange, and recommender system -and highlight its challenges and opportunities.

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C3PO: A Network and Application Framework for Spontaneous and Ephemeral Social Networks

Cited by 8 publications

References 10 publications

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

Design of a Machine Learning-Based Intelligent Middleware Platform for a Heterogeneous Private Edge Cloud System

On gossip-based information dissemination in pervasive recommender systems

Collaborating with Users in Proximity for Decentralized Mobile Recommender Systems

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