Changing Trends in Modeling Mobility

A phenomenal increase in the number of wireless devices has led to the evolution of several interesting and challenging research problems in opportunistic networks. For example, the random waypoint mobility model, an early, popular effort to model mobility, involves generating random movement patterns. Previous research efforts, however, validate that movement patterns are not random; instead, human mobility is predictable to some extent. Since the performance of a routing protocol in an opportunistic network is greatly improved if the movement patterns of mobile users can be somewhat predicted in advance, several research attempts have been made to understand human mobility. The solutions developed use our understanding of movement patterns to predict the future contact probability for mobile nodes. In this work, we summarize the changing trends in modeling human mobility as random movements to the current research efforts that model human walks in a more predictable manner. Mobility patterns significantly affect the performance of a routing protocol. Thus, the changing trend in modeling mobility has led to several changes in developing routing protocols for opportunistic networks. For example, the simplest opportunistic routing protocol forwards a received packet to a randomly selected neighbor. With predictable mobility, however, routing protocols can use the expected contact information between a pair of mobile nodes in making forwarding decisions. In this work, we also describe the previous and current research efforts in developing routing protocols for opportunistic networks

Multi-sensor finger ring for authentication based on 3D signatures

Traditional methods of authenticating a user, including password, a Personal Identification Number (PIN), or a more secure PIN entry method (A PIN entry method resilient against shoulder surfing [14]), can be stolen or accessed easily and, therefore, make the authentication unsecure. In this work, we present the usability of our multi-sensor based and standalone finger ring called Pingu in providing a highly secure access system. Specifically, Pingu allows users to make a 3D signature and record the temporal pattern of the signature via an advanced set of sensors. As a result, the user creates a 3D signature in air using his finger. Our approach has two main contributions: (1) Compared to other wearable devices, a finger ring is more socially acceptable, and (2) signatures created via a finger in the air or on a surface leaves no visible track and, thus, are extremely hard to forge. In other words, a 3D signature allows much higher flexibility in choosing a safe signature. Our experiment shows that the proposed hardware and methodology could result in a very high level of user authentication/identification performance

Deciphering Predictability Limits in Human Mobility

International audienc

GLIMMPSE Lite: calculating power and sample size on smartphone devices.

Researchers seeking to develop complex statistical applications for mobile devices face a common set of difficult implementation issues. In this work, we discuss general solutions to the design challenges. We demonstrate the utility of the solutions for a free mobile application designed to provide power and sample size calculations for univariate, one-way analysis of variance (ANOVA), GLIMMPSE Lite. Our design decisions provide a guide for other scientists seeking to produce statistical software for mobile platforms

ANOVA “Design” screen on (a) iOS and (b) Android platforms.

<p>ANOVA “Design” screen on (a) iOS and (b) Android platforms.</p

“Solving For” input screen on (a) iOS and (b) Android platforms.

<p>“Solving For” input screen on (a) iOS and (b) Android platforms.</p

Our scaled web and mobile software architecture.

<p>Our scaled web and mobile software architecture.</p