1,306 research outputs found

    PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms

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    Mobile phones provide a powerful sensing platform that researchers may adopt to understand proximity interactions among people and the diffusion, through these interactions, of diseases, behaviors, and opinions. However, it remains a challenge to track the proximity-based interactions of a whole community and then model the social diffusion of diseases and behaviors starting from the observations of a small fraction of the volunteer population. In this paper, we propose a novel approach that tries to connect together these sparse observations using a model of how individuals interact with each other and how social interactions happen in terms of a sequence of proximity interactions. We apply our approach to track the spreading of flu in the spatial-proximity network of a 3000-people university campus by mobilizing 300 volunteers from this population to monitor nearby mobile phones through Bluetooth scanning and to daily report flu symptoms about and around them. Our aim is to predict the likelihood for an individual to get flu based on how often her/his daily routine intersects with those of the volunteers. Thus, we use the daily routines of the volunteers to build a model of the volunteers as well as of the non-volunteers. Our results show that we can predict flu infection two weeks ahead of time with an average precision from 0.24 to 0.35 depending on the amount of information. This precision is six to nine times higher than with a random guess model. At the population level, we can predict infectious population in a two-week window with an r-squared value of 0.95 (a random-guess model obtains an r-squared value of 0.2). These results point to an innovative approach for tracking individuals who have interacted with people showing symptoms, allowing us to warn those in danger of infection and to inform health researchers about the progression of contact-induced diseases

    A survey on Human Mobility and its applications

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    Human Mobility has attracted attentions from different fields of studies such as epidemic modeling, traffic engineering, traffic prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diffusion and flow in the network and makes it easier to estimate how much one part of the network influences another by using metrics like centrality measures. We aim to study population flow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of different data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges

    Master of Science

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    thesisCurrent approaches to secret key extraction using Received Signal Strength Indicator (RSSI) measurements mainly use the WiFi interface. However, in the presence of jamming adversaries and other interfering devices, the efficiency of RSSI-based secret key extraction using WiFi degrades and sometimes the key extraction may even fail completely. A possible method to overcome this problem is to collect RSSI measurements using the Bluetooth interface. Bluetooth appears to be very promising for secret key extraction since the adaptive frequency hopping technique in Bluetooth automatically detects and avoids the use of bad or interfering channels. In order to collect Bluetooth RSSI values, we design a protocol where Alice and Bob use Google Nexus one phones to exchange L2CAP packets and then we measure the RSSI for each received packet. We use a prequantization interpolation step to reduce the probability of bit mismatches that are caused due to the inabililty to measure the time-duplex channel simultaneously by Alice and Bob. We then use the ASBG quantization scheme followed by information reconciliation and privacy amplification to extract the secret key bits. We conduct numerous experiments to evaluate the efficiency of Bluetooth for secret key extraction under two di↵erent mobile environments - hallways and outdoors. The secret bit rates obtained from these experiments highlight that outdoor settings are better suited for key extraction using Bluetooth when compared to hallway settings. Furthermore, we show that for very small distances such as 2 ft, the number of consecutive "0" RSSI values and bit mismatch is too high to extract any secret key bits under hallway settings. Finally, we also show that Bluetooth key extraction in outdoors achieves secret bit rates that are comparable toWiFi, even when using lower transmit power than WiFi

    Intelligent Sensing and Learning for Advanced MIMO Communication Systems

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    WiROS: WiFi sensing toolbox for robotics

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    Many recent works have explored using WiFi-based sensing to improve SLAM, robot manipulation, or exploration. Moreover, widespread availability makes WiFi the most advantageous RF signal to leverage. But WiFi sensors lack an accurate, tractable, and versatile toolbox, which hinders their widespread adoption with robot's sensor stacks. We develop WiROS to address this immediate need, furnishing many WiFi-related measurements as easy-to-consume ROS topics. Specifically, WiROS is a plug-and-play WiFi sensing toolbox providing access to coarse-grained WiFi signal strength (RSSI), fine-grained WiFi channel state information (CSI), and other MAC-layer information (device address, packet id's or frequency-channel information). Additionally, WiROS open-sources state-of-art algorithms to calibrate and process WiFi measurements to furnish accurate bearing information for received WiFi signals. The open-sourced repository is: https://github.com/ucsdwcsng/WiRO

    Recent Advances in Indoor Localization Systems and Technologies

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    Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods
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