Reliability of Bluetooth-based connectivity traces for the characterization of human interaction

Cabero, José María; Urteaga, Iñigo; Molina, Virginia; Liberal, Fidel; Martín, José Luis Martín

doi:10.1016/j.adhoc.2014.08.010

Cited by 6 publications

(9 citation statements)

References 22 publications

(45 reference statements)

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“…One may however object that the previous study was done with a synthetic dataset instead of a real one. In fact, a similar study was also conducted in [6] for the Humanet dataset. Originally produced with a sampling period of 5 s, the authors processed the trace to virtually achieve sampling periods of 120 s as in [22] or 300 s as in [10] .…”

Section: Merging Contactsmentioning

confidence: 96%

“…This can be problematic because for such datasets, there is no straightforward way of assessing the impact of these choices due to the unavailability of the raw, unfiltered data; worse, users of such traces may not even realize the implications of this filtering. On the contrary, the more recent Humanet dataset [4] does not include any filter in the released version; some are however explicitly applied by the authors for their subsequent studies [6] .…”

Section: Merging Contactsmentioning

confidence: 99%

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Following the right path: Using traces for the study of DTNs

Baudic

Pérennou

Lochin

2016

Computer Communications

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Contact traces collected in real situations represent a popular material for the study of a Delay Tolerant Network. Three main use cases can be defined for traces: social analysis, performance evaluation and statistical analysis. In this paper, we perform a review on the technicalities of real trace collection and processing. First, we identify several factors which can influence traces during collection, filtering or scaling, and illustrate their impact on the conclusions, based on our experience with four datasets from the literature. We subsequently propose a list of criteria to be verified each time a trace is to be used, along with recommendations on which filters to apply depending on the envisioned use case. The rationale is to provide guidelines for researchers needing to perform trace analysis in their studies.

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Section: Merging Contactsmentioning

confidence: 96%

Section: Merging Contactsmentioning

confidence: 99%

Following the right path: Using traces for the study of DTNs

Baudic

Pérennou

Lochin

2016

Computer Communications

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“…Understanding the limitations of the BT technology for characterizing human behavior is a first step towards developing reliable BT data collection system, and was already investigated by [10,11,39]. The main identified limitations are the person-device uncertainty, the granularity (sample period) of the BT traces, and the bias caused by the particularities of the BT technology itself.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the particularities of the BT technology itself posses some limitations. As shown in [10], the rate of false negatives (not detecting a device that is in proximity) increases when more devices are connecting to a wireless medium.…”

Section: Introductionmentioning

confidence: 99%

Understanding the complexities of Bluetooth for representing real-life social networks

Simoski

Klein

Araújo

et al. 2020

Pers Ubiquit Comput

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Bluetooth (BT) data has been extensively used for recognizing social patterns and inferring social networks, as BT is widely present in everyday technological devices. However, even though collecting BT data is subject to random noise and may result in substantial measurement errors, there is an absence of rigorous procedures for validating the quality of the inferred BT social networks. This paper presents a methodology for inferring and validating BT-based social networks based on parameter optimization algorithm and social network analysis (SNA). The algorithm performs edge inference in a bruteforce search over a given BT data set, for deriving optimal BT social networks by validating them with predefined ground truth (GT) networks. The algorithm seeks to optimize a set of parameters, predefined considering some reliability challenges associated to the BT technology itself. The outcomes show that optimizing the parameters can reduce the number of BT data false positives or generate BT networks with the minimum amount of BT data observations. The subsequent SNA shows that the inferred BT social networks are unable to reproduce some network characteristics present in the corresponding GT networks. Finally, the generalizability of the proposed methodology is demonstrated by applying the algorithm on external BT data sets, while obtaining comparable results.

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“…They collected a voluminous database with contact traces of people during labour hours in the same office building for several weeks. The out-come of these experiments served as valuable motivation for the work presented in this article [23] [24].…”

Section: Related Workmentioning

confidence: 99%

DTN Routing Optimised by Human Routines: The HURRy Protocol

Pérez-Sánchez

Cabero

Urteaga

2015

Lecture Notes in Computer Science

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Abstract. This paper proposes the HURRy (HUman Routines used for Routing) protocol, which infers and benefits from the social behaviour of nodes in disruptive networking environments. HURRy incorporates the contact duration to the information retrieved from historical encounters among neighbours, so that smarter routing decisions can be made. The specification of HURRy is based on the outcomes of a thorough experiment, which highlighted the importance of distinguishing between short and long contacts and deriving mathematical relations in order to optimally prioritize the available routes to a destination. HURRy introduces a novel and more meaningful rating system to evaluate the quality of each contact and overcome the limitations of other routing approaches in social environments.

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Reliability of Bluetooth-based connectivity traces for the characterization of human interaction

Cited by 6 publications

References 22 publications

Following the right path: Using traces for the study of DTNs

Following the right path: Using traces for the study of DTNs

Understanding the complexities of Bluetooth for representing real-life social networks

DTN Routing Optimised by Human Routines: The HURRy Protocol

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