Earthquake Early Warning and Beyond: Systems Challenges in Smartphone-based Seismic Network

Earthquake Early Warning (EEW) systems can effectively reduce fatalities, injuries, and damages caused by earthquakes. Current EEW systems are mostly based on traditional seismic and geodetic networks, and exist only in a few countries due to the high cost of installing and maintaining such systems. The MyShake system takes a different approach and turns people's smartphones into portable seismic sensors to detect earthquake-like motions. However, to issue EEW messages with high accuracy and low latency in the real world, we need to address a number of challenges related to mobile computing. In this paper, we first summarize our experience building and deploying the MyShake system, then focus on two key challenges for smartphone-based EEW (sensing heterogeneity and user/system dynamics) and some preliminary exploration. We also discuss other challenges and new research directions associated with smartphone-based seismic network.Comment: 6 pages, conference paper, already accepted at hotmobile 201

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Geo-located Twitter as the proxy for global mobility patterns

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns.Comment: 17 pages, 13 figure

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

Exploiting Cellular Data for Disease Containment and Information Campaigns Strategies in Country-Wide Epidemics

Human mobility is one of the key factors at the basis of the spreading of diseases in a population. Containment strategies are usually devised on movement scenarios based on coarse-grained assumptions. Mobility phone data provide a unique opportunity for building models and defining strategies based on very precise information about the movement of people in a region or in a country. Another very important aspect is the underlying social structure of a population, which might play a fundamental role in devising information campaigns to promote vaccination and preventive measures, especially in countries with a strong family (or tribal) structure. In this paper we analyze a large-scale dataset describing the mobility and the call patterns of a large number of individuals in Ivory Coast. We present a model that describes how diseases spread across the country by exploiting mobility patterns of people extracted from the available data. Then, we simulate several epidemics scenarios and we evaluate mechanisms to contain the epidemic spreading of diseases, based on the information about people mobility and social ties, also gathered from the phone call data. More specifically, we find that restricting mobility does not delay the occurrence of an endemic state and that an information campaign based on one-to-one phone conversations among members of social groups might be an effective countermeasure.Comment: 9 pages, 9 figures. Appeared in Proceedings of NetMob 2013. Boston, MA, USA. May 201

Human Mobility Monitoring using WiFi: Analysis, Modeling, and Applications

Understanding and modeling humans and device mobility has fundamental importance in mobile computing, with implications ranging from network design and location-aware technologies to urban infrastructure planning. Today\u27s users carry a plethora of devices such as smartphones, laptops, tablets, and smartwatches, with each device offering a different set of services resulting in different usage and mobility leading to the research question of understanding and modeling multiple user device trajectories. Additionally, prior research on mobility focuses on outdoor mobility when it is known that users spend 80% of their time indoors resulting in wide gaps in knowledge in the area of indoor mobility of users and devices. Here, I try to fill the gaps in mobility modeling in the areas of understanding and modeling indoor-outdoor human mobility as well as multi-device mobility. In this thesis, I propose the characterization and modeling of human and device mobility. Further, I design and deploy mobility-aware applications for contact tracing of infectious diseases and energy-aware Heating, Ventilation, and Air Conditioning (HVAC) scheduling. I try and answer a sequence of four primary inter-related questions : (1) how is indoor and outdoor user mobility different, (2) are multiple device trajectories belonging to a single user correlated, (3) how to model indoor mobility of users and (4) how to design effective mobility aware applications that are easily deployable and align with long term goals of sustainability as well relay positive societal impact. The insights gained from each question serves as a base to build up on the next question in the series. I present answers to these questions across three main parts of my thesis. The first part comprises of characterization and analysis of human and device mobility. In this part I design and develop tool to extract device trajectories from WiFi system logs syslog and map devices to users. These extracted trajectories and device to user mapping are used to characterize and empirically analyze the mobility of users at varying spatial granularity (indoor, outdoor) and extract device mobility correlations between multiple devices of users and forms the first part of my thesis. In the second part, based on the insights gained from the multi-granular and multi-device mobility characterization stated above, I argue that mobility is inherently hierarchical in nature and propose novel indoor human mobility modeling approach. Third, I leverage the passively observed mobility to design mobility-aware applications that either look back or look ahead in time. WiFiTrace is a look back or backtracking application that is a network-centric contact tracing tool to aid healthcare workers in manual contact tracing of infectious diseases and iSchedule is a look ahead machine learning based mobility-aware energy-saving application that predicts Heating, Ventilation, and Air Conditioning (HVAC) schedule for higher energy savings while increasing user comfort