Aarti Munjal

Trace-based mobility modeling for multi-hop wireless networks

Realistic and scenario-dependent mobility modeling is crucial for the reliable performance evaluation of multi-hop networks. In the last decade, a significant number of synthetic mobility models have been proposed. However, only a few of these models have been validated by realistic movement traces. In the last few years, several of such traces have been collected, analyzed, and made available to the community. This paper provides a comprehensive and up-to-date survey of (1) available movement traces, (2) modeling/analyses of these traces, and (3) synthetic mobility models. The focus of the paper is on mobility traces/models that include position information. The contribution of this paper is to summarize the research that has been done in the area of mobility modeling over the last few years and present challenges for future work.

SMOOTH: a simple way to model human mobility

In addition to being realistic, a mobility model should be easy to understand and use. Unfortunately, most of the simple mobility models proposed thus far are not realistic and most of the realistic mobility models proposed thus far are not simple to use. The main contribution of this work is to present SMOOTH, a new mobility model that is realistic (e.g., SMOOTH is based on several known features of human movement) and is simple to use (e.g., SMOOTH does not have any complex input parameters). We first present SMOOTH. We then validate that SMOOTH imitates human movement patterns present in real mobility traces collected from a range of diverse scenarios. In addition, we compare SMOOTH with the other mobility models developed based on these mobility traces. Thus, with SMOOTH, we provide researchers with a tool that allows them to leverage the statistical features present in real human movement in a simple and easy to understand manner.

Constructing rigorous MANET simulation scenarios with realistic mobility

Researchers need to choose an appropriate scenario to study the performance of a Mobile Ad hoc NETwork (MANET) via simulation. For example, routing is not properly evaluated when the shortest path between each pair of nodes in the simulation scenario is two or less. Various standards may be required to construct a credible MANET simulation scenario. In this work, we concentrate upon three standards for evaluating MANET routing protocols. Metrics involved in these standards are: average shortest-path hop count, average network partitioning, and average neighbor count. The main contribution of this work is to provide researchers with models that allow them to easily construct rigorous MANET simulation scenarios. The input to our models is the desired values for the three metrics mentioned; our models then output parameters for a simulation scenario that approximately meet the researchers target values for the metrics. Our models were designed using a recently published mobility model that was constructed by extracting the statistical features of real human movement. Our models enable researchers to test MANET routing protocols in a more realistic manner, thereby improving the credibility of their MANET simulation studies.

SMOOTH: a simple way to model human walks

In addition to being realistic, a mobility model should be easy to understand and use. Unfortunately, most of the simple mobility models proposed thus far are not realistic and most of the realistic mobility models proposed thus far are not simple to use. In this work, we present SMOOTH, a new mobility model that is realistic and simple to use. SMOOTH imitates a recently published mobility model, SLAW, that is realistic but not simple to use. While SLAW was constructed by extracting statistical features of real human movement, it has input parameters that are very complex to set. SMOOTH, on the other hand, is realistic (as SMOOTH is based on the real GPS traces used by SLAW) and is simple to use (as SMOOTH avoids SLAWs complex input parameter.

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.

A queuing-theoretic framework for modeling and analysis of mobility in WSNs

In this paper, we present a complete framework for modeling and analysis of Mobility in Wireless Sensor Networks using OQNs with GI/G/1 nodes and single-class customers. We formalize and present three variations - gated queues, intermittent links and intermittent servers. We suitably modify and use the Queuing Network Analyzer (QNA) to study performance measures including: throughput, average waiting time (end-to-end delay), and packet loss probability. The results are verified by simulation in OMNeT++.

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.

Multi-sensor Based Gestures Recognition with a Smart Finger Ring

Recently several optical and non-optical sensors based gesture recognition techniques have been developed to interact with computing devices. However, these techniques mostly suffer from problems such as occlusion and noise. In this work, we present Pingu, a multi-sensor based framework that is capable of recognizing simple, sharp, and tiny gestures without the problems mentioned above. Pingu has been calibrated in the form of a wearable finger ring, capable of interacting even when the device is not in the vicinity of the user. An advanced set of sensors, wireless connectivity, and feedback facilities enable Pingu for a wide range of potential applications, from novel gestures to social computing. In this paper, we present our results based on experiments conducted to explore Pingu’s use as a general gestural interaction device. Our analysis, based on simple machine learning algorithms, shows that simple and sharp gestures performed by a finger can be detected with a high accuracy, thereby, stablishing Pingu as a wearable ring to control a smart environment effectively.

Reducing decision errors in the paired comparison of the diagnostic accuracy of screening tests with Gaussian outcomes

BackgroundScientists often use a paired comparison of the areas under the receiver operating characteristic curves to decide which continuous cancer screening test has the best diagnostic accuracy. In the paired design, all participants are screened with both tests. Participants with suspicious results or signs and symptoms of disease receive the reference standard test. The remaining participants are classified as non-cases, even though some may have occult disease. The standard analysis includes all study participants, which can create bias in the estimates of diagnostic accuracy since not all participants receive disease status verification. We propose a weighted maximum likelihood bias correction method to reduce decision errors.MethodsUsing Monte Carlo simulations, we assessed the method’s ability to reduce decision errors across a range of disease prevalences, correlations between screening test scores, rates of interval cases and proportions of participants who received the reference standard test.ResultsThe performance of the method depends on characteristics of the screening tests and the disease and on the percentage of participants who receive the reference standard test. In studies with a large amount of bias in the difference in the full areas under the curves, the bias correction method reduces the Type I error rate and improves power for the correct decision. We demonstrate the method with an application to a hypothetical oral cancer screening study.ConclusionThe bias correction method reduces decision errors for some paired screening trials. In order to determine if bias correction is needed for a specific screening trial, we recommend the investigator conduct a simulation study using our software.

Large-Scale Human Mobility Analysis Based on Mobile Phone and Social Media Communication: A Case-Study in Africa

Mobile phone and social media communication are sometimes explored as viable sources of location information for human mobility research. To the best of our knowledge, however, no one has studied how location information obtained via these two event-based techniques compare to each other. In this paper, we present a comparative analysis of obtaining location information using mobile phone records and social media communication. The comparison was made under similar conditions. Specifically, the two datasets are from the same geographical area (a developing country in Africa), have the same number of users, and were collected over a similar period of time (months and days). Our analysis shows that the source of the location information used has a significant impact on what can be perceived in terms of individual mobility within a population. Using the results from these two datasets, we conclude that mobile phone communication is a better source of location information for human mobility research when compared to social media communication. We argue that our conclusion exists due to the relation of social media communication to economic and demographic factors.