Kamini Garg

On context awareness and social distance in human mobility traces

Opportunistic networks consist of mobile devices, carried by people in their everyday lives. They organize autonomously to exchange data with direct neighbors without the use of any infrastructural services. Since the devices are carried by humans, one of the main challenges to consider in opportunistic networks is the human mobility behavior. However, little work exists on how the social behavior of people drives their mobility behavior and how this context information can be systematically leveraged for opportunistic networking applications. This paper tackles this problem by providing both experimental and theoretical analysis of human mobility context information. We present a novel real world experiment with sensor nodes carried by people to demonstrate and study the effect of context on people mobility. Furthermore, we define a novel metric of social distance to put this new evidence on solid mathematical foundation. Thus, our work puts a basis to systematically leveraging context information for opportunistic networking applications and services. Additionally, our experimental data traces enable testing and evaluation of such novel services in a real world scenario.

FLEXOR: User friendly wireless sensor network development and deployment

Wireless sensor networks (WSNs) penetrated the market mainly as solutions for specific application scenarios. However, this strong specialization limits WSNs reuse both in terms of development as well as in terms of technical results: Every new application scenario requires a new design, development and validation, as well as management skills. This is frustrating for any WSNs user, developer or manager. To reverse this tendency and thus improve the quality of experience and user-friendliness in WSNs, we designed FLEXOR, a sustainable software architecture optimized to support the implementation, rapid prototyping, evaluation, and testing of wireless sensor network applications, that is platform independent and user-friendly. FLEXOR is designed to accommodate many different applications and services for wireless sensor networks and foster code re-usability and cross-platform component re-usability. FLEXOR offers high modularity, well defined interfaces, remote node management functionality as well as run-time module exchange. Finally, the introduction of a unifying way for WSNs development opens to a higher homogeneity and thus to more easy comparison among different solutions. We present here an analysis of FLEXOR from these new angles and show how effective it is for several purposes and in particular for non-experts and in education.

Towards Realistic and Credible Wireless Sensor Network Evaluation

This position paper explores the problem of realistically evaluating wireless sensor network (WSN) applications, algorithms and protocols. It surveys the currently available techniques, such as simulators, testbeds and real world deployments and compares their properties and challenges. While we underline the significance of simulation tools, we also observe that the state of the art simulation models at all levels (from physical to application) still lack realistic behavior. To demonstrate this gap we performed a broad study of simulation models and real world behavior of wireless links and compared those in various settings, including outdoor environments and battery-based deployments. Based on the provided survey and wireless link case study, we outline a strategy of how to enable realistic, efficient, low-cost and repeatable WSN evaluation scenarios.

A study to understand the impact of node density on data dissemination time in opportunistic networks

In this paper, we study the impact of node density on data dissemination time and achieved data quality in a distributed people-centric system. Our results are obtained through an extensive simulation campaign employing Random Way Point and Random Direction mobility and realistic node densities of real environments. Our simulation results show that, the impact of node density does not significantly affect the data dissemination time after a certain threshold of node density, without compromising the achieved data quality. This result is evident for both mobility models. Our study provides an insight to the parameters we need to consider while evaluating the success of any distributed people-centric system.

When Simpler Data Does Not Imply Less Information: A Study of User Profiling Scenarios With Constrained View of Mobile HTTP(S) Traffic

The exponential growth in smartphone adoption is contributing to the availability of vast amounts of human behavioral data. This data enables the development of increasingly accurate data-driven user models that facilitate the delivery of personalized services that are often free in exchange for the use of its customers’ data. Although such usage conventions have raised many privacy concerns, the increasing value of personal data is motivating diverse entities to aggressively collect and exploit the data. In this article, we unfold profiling scenarios around mobile HTTP(S) traffic, focusing on those that have limited but meaningful segments of the data. The capability of the scenarios to profile personal information is examined with real user data, collected in the wild from 61 mobile phone users for a minimum of 30 days. Our study attempts to model heterogeneous user traits and interests, including personality, boredom proneness, demographics, and shopping interests. Based on our modeling results, we discuss various implications to personalization, privacy, and personal data rights.

Synergistic user ↔ context analytics

Various flavours of a new research field on (socio − )physicalorpersonalanalytics have emerged, with the goal of deriving semanticallyrich insights from people’s low-level physical sensing combined with their (online) social interactions. In this paper, we argue for more comprehensive data sources, including environmental and application-specific data, to better capture the interactions between users and their context, in addition to those among users. We provide some example use cases and present our ongoing work towards a synergistic analytics platform: a testbed based on mobile crowdsensing and IoT, a data model for representing the different sources of data and their connections, and a prediction engine for analyzing the data and producing insights.

Modeling Social Interactions in Real Work Environments

We present a study for modeling the behavioral patterns of employees and keeping track of the social interactions among people in a real work environment. The main advantage of our approach to capture social interactions in a work environment is the use of off-the-shelf tools and devices - like smartphones available on the market - and the utilization of the discovered patterns for the optimum distribution of employees in a office building. We carried out an experiment in our building at Fernfachhochschule Schweiz and captured data about physical proximity, virtual interactions (i.e., email exchange) and individual performance satisfaction of 20 employees for 8 working days, during their working hours. The objective of the experiment was to investigate the interaction patterns of employees in relation to four aspects: quantity, space, performance and organization. Besides confirming the existence of different social interaction types, we also provide insights in how distance between office spaces affects type and amount of social interaction. Further, we describe the influence of contacts among workers on their performance. Finally, our analysis emphasizes the importance of an employees role in terms of number of physical and virtual interactions.

A TinyOS based tool for gathering real-world wireless traces

This paper presents a user-friendly TinyOS and Java-based tool for hassel-free collection of real-world wireless traces from any real-world environment (indoor or outdoor).

A Novel Approach to Predict Retweets and Replies Based on Privacy and Complexity-Aware Feature Planes

An efficient tweet dissemination predictor for retweets and replies is central both to a better understanding of influentials (people and messages), as well as of how social media revenue models can be better monetized. Traditionally research concentrated on retweets popularity and information cascades while neglecting the importance of features richness and classification. We propose a novel approach that introduces feature planes for better prediction of single step tweet dissemination. We show that our model can achieve a quasi-perfect prediction. This promises to be a seminal step towards a better understanding of data dissemination in social networks.

A Complete Tool-Chain for Developing and TestingWSNApplications with FLEXOR

In this paper, we present the complete tool-chainfor FLEXOR, a sustainable and platform independent softwarearchitecture that is optimized to support the implementation,rapid prototyping, evaluation, and testing of wireless sensornetwork applications. Keywords—Wireless SensorNetworks, Architecture, Tool