Sprinkler: A probabilistic dissemination protocol to provide fluid user interaction in multi-device ecosystems

Luxey, Adrien; Bromberg, Yérom-David; Costa, Fábio M.; Lima, Vinicius S. S.; Rocha, Ricardo Couto Antunes da; Taı̈ani, François

doi:10.1109/percom.2018.8444577

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…Social networking is another example of LBS that can benefit from IndoorHash by comparing the hash histories in order to identify potential connections within a crowd of users without revealing the exact locations where these users use to meet. IndoorHash can therefore support the definition of proximity datasets that are commonly used for various purposes, such as mobile testing [22], by reporting on the colocation of end users and potential connections in a crowd that can support the evaluation of dissemination protocols [19,21].…”

Section: Towards Privacy-preserving Location-based Servicesmentioning

confidence: 99%

Capturing Privacy-Preserving User Contexts with IndoorHash

Meftah

Rouvoy

Chrisment

2020

Lecture Notes in Computer Science

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IoT devices are ubiquitous and widely adopted by end-users to gather personal and environmental data that often need to be put into context in order to gain insights. In particular, location is often a critical context information that is required by third parties in order to analyse such data at scale. However, sharing this information is i) sensitive for the user privacy and ii) hard to capture when considering indoor environments. This paper therefore addresses the challenge of producing a new location hash, named IndoorHash, that captures the indoor location of a user, without disclosing the physical coordinates, thus preserving their privacy. This location hash leverages surrounding infrastructure, such as WiFi access points, to compute a key that uniquely identifies an indoor location. Location hashes are only known from users physically visiting these locations, thus enabling a new generation of privacy-preserving crowdsourcing mobile applications that protect from third parties reidentification attacks. We validate our results with a crowdsourcing campaign of 31 mobile devices during one month of data collection.

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Section: Towards Privacy-preserving Location-based Servicesmentioning

confidence: 99%

Capturing Privacy-Preserving User Contexts with IndoorHash

Meftah

Rouvoy

Chrisment

2020

Lecture Notes in Computer Science

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“…However, context reasoning is based only on a pre-defined static distance function with fixed contextual inputs. Probabilistic and association based solutions [27], [23] provide efficient activity sensing and fluid device interaction, while other approaches use Hidden Markov Models (HMMs) to model context-awareness [4], [5], [25], [34]. These approaches either require a list of pre-defined "sit- uations" to which they are restricted, or they make assumptions restricting the context and the environment.…”

Section: Motivation and Related Workmentioning

confidence: 99%

rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

Hua

Liu

Kalbarczyk

et al. 2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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Advances in mobile computing capabilities and an increasing number of Internet of Things (IoT) devices have enriched the possibilities of the IoT but have also increased the cognitive load required of IoT users. Existing context-aware systems provide various levels of automation in the IoT. Many of these systems adaptively take decisions on how to provide services based on assumptions made a priori. The approaches are difficult to personalize to an individual's dynamic environment, and thus today's smart IoT spaces often demand complex and specialized interactions with the user in order to provide tailored services. We propose rIoT, a framework for seamless and personalized automation of human-device interaction in the IoT. rIoT leverages existing technologies to operate across heterogeneous devices and networks to provide a one-stop solution for device interaction in the IoT. We show how rIoT exploits similarities between contexts and employs a decision-tree like method to adaptively capture a user's preferences from a small number of interactions with the IoT space. We measure the performance of rIoT on two realworld data sets and a real mobile device in terms of accuracy, learning speed, and latency in comparison to two state-of-the-art machine learning algorithms.

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“…ISSUE 1, representing the user's behavior: as in our previous work [30], we represent Alice's use of her devices as an ever-growing sequence containing k interactions:…”

Section: B Predictive and Reliable Peers Selectionmentioning

confidence: 99%

“…To achieve this aggregation, we leverage on probabilistic dissemination protocols [13], [17], [25], precisely implementing a probabilistic distributed broadcast. This algorithm from earlier works in our team [30] has been called STORYTELLER in the remainder of the paper. ISSUE 3, selecting adequate peers: a device must have some insight on which device will most probably be used next to intelligently choose peers to share its new session with.…”

Section: B Predictive and Reliable Peers Selectionmentioning

confidence: 99%

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CASCADE: Reliable Distributed Session Handoff for Continuous Interaction Across Devices

Bromberg

Luxey

Taı̈ani

2018

2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS)

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Allowing users to navigate seamlessly between their personal devices while protecting their privacy remains today an ongoing challenge. Existing solutions rely on peer-to-peer designs, and blindly flood the network with session messages. It is particularly hard to come up with proposals that are both costefficient and dependable while relying on poorly connected mobile appliances. We propose CASCADE, a distributed protocol to share applicative sessions among one's devices. Our proactive session handoff algorithm takes inspiration from the BitTorrent P2P file sharing protocol, but adapts it to the specific characteristics of our problem. It eschews in particular trackers, and limits the seeders of each session to the devices most likely to be used next, as computed by a decentralized aggregation protocol. A key aspect of our approach is to trade off network costs for reliability, while providing a faster session handoff than centralized solutions in the vast majority of the cases.

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Sprinkler: A probabilistic dissemination protocol to provide fluid user interaction in multi-device ecosystems

Cited by 4 publications

References 27 publications

Capturing Privacy-Preserving User Contexts with IndoorHash

Capturing Privacy-Preserving User Contexts with IndoorHash

rIoT: Enabling Seamless Context-Aware Automation in the Internet of Things

CASCADE: Reliable Distributed Session Handoff for Continuous Interaction Across Devices

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