10,884 research outputs found
Human Interaction Discovery in Smartphone Proximity Networks
Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance
Validation of a smartphone app to map social networks of proximity
Social network analysis is a prominent approach to investigate interpersonal
relationships. Most studies use self-report data to quantify the connections
between participants and construct social networks. In recent years smartphones
have been used as an alternative to map networks by assessing the proximity
between participants based on Bluetooth and GPS data. While most studies have
handed out specially programmed smartphones to study participants, we developed
an application for iOS and Android to collect Bluetooth data from participants
own smartphones. In this study, we compared the networks estimated with the
smartphone app to those obtained from sociometric badges and self-report data.
Participants (n=21) installed the app on their phone and wore a sociometric
badge during office hours. Proximity data was collected for 4 weeks. A
contingency table revealed a significant association between proximity data
(rho = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%)
than for the badges (1.3%), indicating that dyads were more often detected by
the app. We then compared the networks that were estimated using the proximity
and self-report data. All three networks were significantly correlated,
although the correlation with self-reported data was lower for the app (rho =
0.25) than for badges (rho = 0.67). The scanning rates of the app varied
considerably between devices and was lower on iOS than on Android. The
association between the app and the badges increased when the network was
estimated between participants whose app recorded more regularly. These
findings suggest that the accuracy of proximity networks can be further
improved by reducing missing data and restricting the interpersonal distance at
which interactions are detected.Comment: 20 pages, 5 figure
Robust modeling of human contact networks across different scales and proximity-sensing techniques
The problem of mapping human close-range proximity networks has been tackled
using a variety of technical approaches. Wearable electronic devices, in
particular, have proven to be particularly successful in a variety of settings
relevant for research in social science, complex networks and infectious
diseases dynamics. Each device and technology used for proximity sensing (e.g.,
RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with
specific biases on the close-range relations it records. Hence it is important
to assess which statistical features of the empirical proximity networks are
robust across different measurement techniques, and which modeling frameworks
generalize well across empirical data. Here we compare time-resolved proximity
networks recorded in different experimental settings and show that some
important statistical features are robust across all settings considered. The
observed universality calls for a simplified modeling approach. We show that
one such simple model is indeed able to reproduce the main statistical
distributions characterizing the empirical temporal networks
Survey and Systematization of Secure Device Pairing
Secure Device Pairing (SDP) schemes have been developed to facilitate secure
communications among smart devices, both personal mobile devices and Internet
of Things (IoT) devices. Comparison and assessment of SDP schemes is
troublesome, because each scheme makes different assumptions about out-of-band
channels and adversary models, and are driven by their particular use-cases. A
conceptual model that facilitates meaningful comparison among SDP schemes is
missing. We provide such a model. In this article, we survey and analyze a wide
range of SDP schemes that are described in the literature, including a number
that have been adopted as standards. A system model and consistent terminology
for SDP schemes are built on the foundation of this survey, which are then used
to classify existing SDP schemes into a taxonomy that, for the first time,
enables their meaningful comparison and analysis.The existing SDP schemes are
analyzed using this model, revealing common systemic security weaknesses among
the surveyed SDP schemes that should become priority areas for future SDP
research, such as improving the integration of privacy requirements into the
design of SDP schemes. Our results allow SDP scheme designers to create schemes
that are more easily comparable with one another, and to assist the prevention
of persisting the weaknesses common to the current generation of SDP schemes.Comment: 34 pages, 5 figures, 3 tables, accepted at IEEE Communications
Surveys & Tutorials 2017 (Volume: PP, Issue: 99
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN
Mobile devices are rapidly becoming the primary computing device in people's
lives. Application delivery platforms like Google Play, Apple App Store have
transformed mobile phones into intelligent computing devices by the means of
applications that can be downloaded and installed instantly. Many of these
applications take advantage of the plethora of sensors installed on the mobile
device to deliver enhanced user experience. The sensors on the smartphone
provide the opportunity to develop innovative mobile opportunistic sensing
applications in many sectors including healthcare, environmental monitoring and
transportation. In this paper, we present a collaborative mobile sensing
framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on
smartphones capturing and sharing sensed data between multiple distributed
applications and users. MOSDEN follows a component-based design philosophy
promoting reuse for easy and quick opportunistic sensing application
deployments. MOSDEN separates the application-specific processing from the
sensing, storing and sharing. MOSDEN is scalable and requires minimal
development effort from the application developer. We have implemented our
framework on Android-based mobile platforms and evaluate its performance to
validate the feasibility and efficiency of MOSDEN to operate collaboratively in
mobile opportunistic sensing applications. Experimental outcomes and lessons
learnt conclude the paper
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
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