Global Peer-to-Peer Classification in Mobile Ad-Hoc Networks: A Requirements Analysis

Gordon, Dawud; Scholz, Markus; Ding, Yong; Beigl, Michael

doi:10.1007/978-3-642-24279-3_12

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…They are not applicable to all scenarios. Distributed approaches such as [23] have also been developed for group recognition. Group activities are recognized using belief propagation among mobile devices [24].…”

Section: Related Workmentioning

confidence: 99%

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

Han

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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People in social environments often appear in groups to have face-to-face interactions, automatically recognizing these groups will facilitate many applications while being unobtrusive. In this work, we focus on recognizing multiple concurrent intentional groups of people in proximity using a real-time distributed approach running on commodity mobile devices. Specifically, we study Bluetooth signal strength probability distribution for proximity estimation and model the group probability distribution under Bluetooth signal strength. Further, we develop a realtime distributed group determination algorithm. We implement a prototype system on iOS platforms which includes both the group recognition service and a mobile social application using the service. Both our simulation and prototype results show that the proposed approach can recognize proximity-based intentional groups with high accuracy.

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Section: Related Workmentioning

confidence: 99%

Grace: Recognition of Proximity-Based Intentional Groups Using Collaborative Mobile Devices

Han

2014

2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems

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“…Information from wearable sensors is centralized, features indicative of affiliation are extracted, and the result is clustered to identify groups and affiliations [17]. However in situations where centralized aggregation is not practical [11], such as emergencies [4], new methods for evaluating group affiliation using P2P analysis systems must be explored.…”

Section: Introductionmentioning

confidence: 99%

Group affiliation detection using model divergence for wearable devices

Gordon¹,

Wirz

Roggen

et al. 2014

Proceedings of the 2014 ACM International Symposium on Wearable Computers

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Methods for recognizing group affiliations using mobile devices have been proposed using centralized instances to aggregate and evaluate data. However centralized systems do not scale well and fail when the network is congested. We present a method for distributed, peer-to-peer (P2P) recognition of group affiliations in multi-group environments, using the divergence of mobile phone sensor data distributions as an indicator of similarity. The method assesses pairwise similarity between individuals using model parameters instead of sensor observations, and then interprets that information in a distributed manner. An experiment was conducted with 10 individuals in different group configurations to compare P2P and conventional centralized approaches. Although the output of the proposed method fluctuates, we can still correctly detect 93% of group affiliations by applying a filter. We foresee applications in mobile social networking, life logging, smart environments, crowd situations and possibly crowd emergencies.

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