MobiGroup: Enabling Lifecycle Support to Social Activity Organization and Suggestion with Mobile Crowd Sensing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs

A Simultaneous Extraction of Context and Community from pervasive signals using nested Dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated

Group Activity Recognition Using Wearable Sensing Devices

Understanding behavior of groups in real time can help prevent tragedy in crowd emergencies. Wearable devices allow sensing of human behavior, but the infrastructure required to communicate data is often the first casualty in emergency situations. Peer-to-peer (P2P) methods for recognizing group behavior are necessary, but the behavior of the group cannot be observed at any single location. The contribution is the methods required for recognition of group behavior using only wearable devices

Proximitäts- und Aktivitätserkennung mit mobilen Endgeräten

Mit der immer größeren Verbreitung mobiler Endgeräte wie Smartphones und Tablets aber auch am Körper getragener Technik (Wearables), ist die Vision einer ubiquitär von Computern durchzogenen Welt weitgehend Realität geworden. Auf Basis dieser überall verfügbaren Technologien lassen sich mehr und mehr kontextbezogene Anwendungen umsetzen, also solche, die ihre Diensterbringung an die aktuelle Situation des Benutzers anpassen. Ein wesentliches Kontextelement ist dabei die Proximität (Nähe) eines Benutzers zu anderen Benutzern oder Objekten. Dabei ist diese Proximität nicht nur rein örtlich zu verstehen, sondern ihre Bedeutung kann auf sämtliche Kontextelemente ausgedehnt werden. Insbesondere ist auch die Übereinstimmung von Aktivitäten verschiedener Benutzer von Interesse, um deren Zusammengehörigkeit abzuleiten. Es existiert gerade im Hinblick auf örtliche Nähe eine Reihe von Standardtechnologien, die eine Proximitätserkennung grundsätzlich erlauben. Alle diese Verfahren weisen jedoch deutliche Schwächen im Hinblick auf Sicherheit und Privatsphäre der Nutzer auf. Im Rahmen dieser Arbeit werden drei neue Verfahren zur Proximitätserkennung vorgestellt. Dabei spielen die Komponenten "Ort" und "Aktivität" jeweils in unterschiedlichem Maße ein wichtige Rolle. Das erste Verfahren benutzt WLAN-Signale aus der Umgebung, um sichere, d.h. unfälschbare, Location Tags zu generieren, mit denen ein privatsphäre-schonender Proximitätstest durchgeführt werden kann. Während das erste Verfahren rein auf örtliche Nähe abzielt, berücksichtigt das zweite Verfahren implizit auch die Aktivität der betrachteten Benutzer. Der Ansatz basiert auf der Auswertung und dem Vergleich visueller Daten, die von am Körper getragenen Kameras aufgenommen werden können. Die Grundidee des dritten Verfahrens besteht darin, dass auch rein auf Basis von Aktivitäten bzw. Aktivitätssequenzen eine kontextuelle Proximität zwischen verschiedenen Nutzern festgestellt werden kann. Zur Umsetzung dieser Idee ist eine sehr feingranulare Aktivitätserkennung notwendig, deren Machbarkeit in dieser Arbeit ebenfalls gezeigt wird. Zusammengenommen werden in der vorliegenden Arbeit mehrere Wege aufgezeigt, unterschiedliche Arten von kontextueller Proximität auf sichere und privatsphäre-schützende Weise festzustellen.With the now widespread usage of mobile devices such as smartphones and tablets as well as body-worn technical gear (Wearables), the vision of a world in which computing resources are ubiquitously available has become reality. Based on these pervasively available technologies, context-aware applications, i.e., applications adapting their provided services to a user's current situation, are becoming more and more feasible. A primary element of a user's context is the proximity of the user to other users or objects. Proximity should not only be considered in a spatial manner but its meaning can be broadened to comprise any context element. In particular, the similarity of different users' activities is an important information to infer their contextual closeness. With regard to spatial proximity, there is a range of standard technologies which on principle allow to perform proximity detection. However, they all face severe problems with regard to security and privacy of the participants in the proximity test. In this work, three new approaches for proximity detection are presented. Within the newly introduced systems, the contextual components "location" and "activity" are considered with different importance. The first approach uses Wifi signals from the surroundings to construct secure, i.e., unforgeable location tags, with which a privacy-preserving proximity test can be performed. While the first method is exclusively focused on spatial proximity, the second approach also implicitly considers the users' activities. This technique is based on analyzing and comparing visual information obtained from body-mounted cameras. The basic idea of the third approach is that contextual proximity can also be obtained based on activities alone. By comparing sequences of activities, the proximity between participating users can be inferred. In order to be realizable, this approach needs very fine-grained activity recognition capabilities. The feasibility of the latter is also shown in this work. Summing up, in this work several ways are shown how to detect contextual proximity in a secure and privacy-preserving manner