Following the Right Path: Using Traces for the Study of DTNs

Contact traces collected in real situations represent a popular material for the study of a Delay Tolerant Network. Three main use cases can be defined for traces: social analysis, performance evaluation and statistical analysis. In this paper, we perform a review on the technicalities of real trace collection and processing. First, we identify several factors which can influence traces during collection, filtering or scaling, and illustrate their impact on the conclusions, based on our experience with four datasets from the literature. We subsequently propose a list of criteria to be verified each time a trace is to be used, along with recommendations on which filters to apply depending on the envisioned use case. The rationale is to provide guidelines for researchers needing to perform trace analysis in their studies

Providing Real-time Driver Advisories in Connected Vehicles: A Data-Driven Approach Supported by Field Experimentation

Approximately 94\% of the annual transportation crashes in the U.S. involve driver errors and violations contributing to the $1 Trillion losses in the economy. Recent V2X communication technologies enabled by Dedicated Short Range Communication (DSRC) and Cellular-V2X (C-V2X) can provide cost-effective solutions for many of these transportation safety applications and help reduce crashes up to 85%. This research aims towards two primary goals. First, understanding the feasibility of deploying V2V-based safety critical applications under the constraints of limited communication ranges and adverse roadway conditions. Second, to develop a prototype application for providing real-time advisories for hazardous driving behaviors and to notify neighboring vehicles using available wireless communication platform. Towards accomplishing the first goal, we have developed a mathematical model to quantify V2V communication parameters and constraints pertaining to a DSRC-based “Safe pass advisory” application and validated the theoretical model using field experiments by measuring the communication ranges between two oncoming vehicles. We also investigated the impacts of varying altitudes, vehicle-interior obstacles, and OBU placement on V2V communication reliability and its implications. Along the direction of the second goal, we derived a data-driven model to characterize the acceleration/deceleration profile of a regular passenger vehicle with respect to speed and throttle position. As a proof of concept demonstration, we implemented an IoT-based communication architecture for disseminating the hazardous driving alerts to vulnerable drivers through cellular and/or V2X communication infrastructure

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

E-Textiles. Study of the interaction between devices, connection methods and substrates

It is common knowledge that textiles and textile products are available in different forms along the textile chain. However, right now, all the attention is paid to the relationship between these materials and the most recent developments in the world of electronics. When a garment is implementing electronic components, ranging from sensors to conductor paths, that are applied or integrated into the textile surface, it’s commonly called an E-Textile. Nowadays there is a continuous demand for these products because they can satisfy a great extent of needs without interfering too much with the user’s life or without the obligation of modifying too much the original product in the beginning. This study, however, focuses on analyzing the interaction and interactivity which allows electronic communication between the components present in the textiles. It is believed that there is a knowledge gap in these most recent developments. In this paper, a literature investigation is carried out using various databases, which resulted in different contributions to the researched subjects. Also, an overview of materials, production technologies and testing methods is given. The concepts of smart and e-textile, textile structure, conductive materials, electronic communication and connection are classified. The influencing factors on the properties of the material structure are presented and a discussion is made referring to the potentials and challenges related to e-textiles. Finally, a brief consideration of sustainability and environmental aspects is done and, in the end, the main conclusions of the investigation are stated. The main focus of the research lies in defining processes and material properties for improving connection techniques between the textiles and the electronic components, and also between the components themselves. Only limited research will be conducted on simulating the behavior of these technologies. Various ideas for applications exist, starting from ones of classical nature (flexural rigid materials), to 3d printed additive manufacturing or other ones of a textile nature in the form of embroidered conductor paths. Unluckily little research has been conducted on real applications. Therefore, the challenges are only identified, and future research directions are derive

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations

The Impact of Spatial Resolution and Representation on Human Mobility Predictability

The study of human mobility patterns is important for both understanding human behaviour, a social phenomenon and to simulate infection transmission. Factors such as geometry representation, granularity, missing data and data noise affect the reliability, validity, and credibility of human mobility data, and any models drawn from this data. This thesis discusses the impact of spatial representations of human mobility patterns through a series of analyses using entropy and trip-length distributions as evaluation criteria, Voronoi decomposition and square grid decomposition as alternative geometry representations. I further examine a spectrum of spatial granularity, from dimensions associated with social interaction, to city, and provincial scale, and toggle analysis between raw data and post-processed data to understand the impact of noisy data and missing data influence estimation. A dataset I was involved with collecting – SHED1 – featuring multi-sensor data collection over 5 weeks among 39 participants – has been used for the experiments. An analysis of the results further strengthens the findings of Song et al., and demonstrates comparability in predictability of human mobility through geometric representation between Voronoi decomposition and square grid decompositions, suggesting a scale dependence of human mobility analysis, and demonstrating the value of using missing data analysis throughout the study