Tracking and visualization of space-time activities for a micro-scale flu transmission study

Abstract Background Infectious diseases pose increasing threats to public health with increasing population density and more and more sophisticated social networks. While efforts continue in studying the large scale dissemination of contagious diseases, individual-based activity and behaviour study benefits not only disease transmission modelling but also the control, containment, and prevention decision making at the local scale. The potential for using tracking technologies to capture detailed space-time trajectories and model individual behaviour is increasing rapidly, as technological advances enable the manufacture of small, lightweight, highly sensitive, and affordable receivers and the routine use of location-aware devices has become widespread (e.g., smart cellular phones). The use of low-cost tracking devices in medical research has also been proved effective by more and more studies. This study describes the use of tracking devices to collect data of space-time trajectories and the spatiotemporal processing of such data to facilitate micro-scale flu transmission study. We also reports preliminary findings on activity patterns related to chances of influenza infection in a pilot study. Methods Specifically, this study employed A-GPS tracking devices to collect data on a university campus. Spatiotemporal processing was conducted for data cleaning and segmentation. Processed data was validated with traditional activity diaries. The A-GPS data set was then used for visual explorations including density surface visualization and connection analysis to examine space-time activity patterns in relation to chances of influenza infection. Results When compared to diary data, the segmented tracking data demonstrated to be an effective alternative and showed greater accuracies in time as well as the details of routes taken by participants. A comparison of space-time activity patterns between participants who caught seasonal influenza and those who did not revealed interesting patterns. Conclusions This study proved that tracking technology an effective technique for obtaining data for micro-scale influenza transmission research. The findings revealed micro-scale transmission hotspots on a university campus and provided insights for local control and prevention strategies.</p

A survey of online data-driven proactive 5G network optimisation using machine learning

In the fifth-generation (5G) mobile networks, proactive network optimisation plays an important role in meeting the exponential traffic growth, more stringent service requirements, and to reduce capitaland operational expenditure. Proactive network optimisation is widely acknowledged as on e of the most promising ways to transform the 5G network based on big data analysis and cloud-fog-edge computing, but there are many challenges. Proactive algorithms will require accurate forecasting of highly contextualised traffic demand and quantifying the uncertainty to drive decision making with performance guarantees. Context in Cyber-Physical-Social Systems (CPSS) is often challenging to uncover, unfolds over time, and even more difficult to quantify and integrate into decision making. The first part of the review focuses on mining and inferring CPSS context from heterogeneous data sources, such as online user-generated-content. It will examine the state-of-the-art methods currently employed to infer location, social behaviour, and traffic demand through a cloud-edge computing framework; combining them to form the input to proactive algorithms. The second part of the review focuses on exploiting and integrating the demand knowledge for a range of proactive optimisation techniques, including the key aspects of load balancing, mobile edge caching, and interference management. In both parts, appropriate state-of-the-art machine learning techniques (including probabilistic uncertainty cascades in proactive optimisation), complexity-performance trade-offs, and demonstrative examples are presented to inspire readers. This survey couples the potential of online big data analytics, cloud-edge computing, statistical machine learning, and proactive network optimisation in a common cross-layer wireless framework. The wider impact of this survey includes better cross-fertilising the academic fields of data analytics, mobile edge computing, AI, CPSS, and wireless communications, as well as informing the industry of the promising potentials in this area

Using context-aware sub sorting of received signal strength fingerprints for indoor localisation

Mobile indoor localisation has numerous uses for logistics, health, sport and social networking applications. Current wireless localisation systems experience reliability difficulties while operating within indoor environments due to interference caused by the presence of metallic infrastructure. Current position localisation use wireless channel propagation characteristics, such as RF receive signal strength to localise a user\u27s position, which is subject to interference. To overcome this, we developed a Fingerprint Context Aware Partitioning tracking model for tracking people within a building. The Fingerprint Context Aware Partitioning tracking model used received RF signal strength fingerprinting, combined with localised context aware information about the user\u27s immediate indoor environment surroundings. We also present an inexpensive and robust wireless localisation network that can track the location of users in an indoor environment, using the Zigbee/802.15.4 wireless communications protocol. The wireless localisation network used reference nodes placed at known positions in a building. The reference nodes are used by mobile nodes, carried by users to localise their position. We found that the Fingerprint Context Aware Partitioning model had improved performance than using only multilateration, in locations that were not in range of multiple reference nodes. Further work includes investigating how multiple mobile nodes can be used by Fingerprint Context Aware Partition model to improve position accuracy

An Outlook into the Future of Egocentric Vision

What will the future be? We wonder! In this survey, we explore the gap between current research in egocentric vision and the ever-anticipated future, where wearable computing, with outward facing cameras and digital overlays, is expected to be integrated in our every day lives. To understand this gap, the article starts by envisaging the future through character-based stories, showcasing through examples the limitations of current technology. We then provide a mapping between this future and previously defined research tasks. For each task, we survey its seminal works, current state-of-the-art methodologies and available datasets, then reflect on shortcomings that limit its applicability to future research. Note that this survey focuses on software models for egocentric vision, independent of any specific hardware. The paper concludes with recommendations for areas of immediate explorations so as to unlock our path to the future always-on, personalised and life-enhancing egocentric vision.Comment: We invite comments, suggestions and corrections here: https://openreview.net/forum?id=V3974SUk1

Geographical Counterpoint to Choreographic Information based on Approaches in GIScience and Visualization

This study provides geographical counterpoint to existing knowledge of a dance piece through approaches from GIScience and visualization by focusing on spatio-temporal movement of dancers in a large dataset of the dance. The goal of this study is to introduce a new application to bridging art and science in the domain of dance and geography disciplines. The study utilizes existing methodologies in GIScience, including exploratory spatial data analysis (ESDA), spatial analysis, Relative Motion (REMO) analysis, and Qualitative Trajectory Calculus (QTC) analysis for the reasoning of the dance data. The results of the study demonstrate the following. First, spatio-temporal information in the dance can be better understood by using approaches in geography, including ESDA, spatial analysis, REMO analysis, QTC analysis, and visualization. Second, the REMO analysis measured relative azimuth, speed, and δ-speed of the dancers per space and time and intuitively visualized their interactions. Third, the QTC analysis showed an example of measuring similarity and difference between repetitive movements of the dancers. The study exhibits how approaches of GIScience in geography could contribute to finding new knowledge of choreographic information that has been, in general, hard to recognize through other disciplines such as dance and statistics

Probabilistic models for mobile phone trajectory estimation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-161).This dissertation is concerned with the problem of determining the track or trajectory of a mobile device - for example, a sequence of road segments on an outdoor map, or a sequence of rooms visited inside a building - in an energy-efficient and accurate manner. GPS, the dominant positioning technology today, has two major limitations. First, it consumes significant power on mobile phones, making it impractical for continuous monitoring. Second, it does not work indoors. This dissertation develops two ways to address these limitations: (a) subsampling GPS to save energy, and (b) using alternatives to GPS such as WiFi localization, cellular localization, and inertial sensing (with the accelerometer and gyroscope) that consume less energy and work indoors. The key challenge is to match a sequence of infrequent (from sub-sampling) and inaccurate (from WiFi, cellular or inertial sensing) position samples to an accurate output trajectory. This dissertation presents three systems, all using probabilistic models, to accomplish this matching. The first, VTrack, uses Hidden Markov Models to match noisy or sparsely sampled geographic (lat, lon) coordinates to a sequence of road segments on a map. We evaluate VTrack on 800 drive hours of GPS and WiFi localization data collected from 25 taxicabs in Boston. We find that VTrack tolerates significant noise and outages in location estimates, and saves energy, while providing accurate enough trajectories for applications like travel-time aware route planning. CTrack improves on VTrack with a Markov Model that uses "soft" information in the form of raw WiFi or cellular signal strengths, rather than geographic coordinates. It also uses movement and turn "hints" from the accelerometer and compass to improve accuracy. We implement CTrack on Android phones, and evaluate it on cellular signal data from over 126 (1,074 miles) hours of driving data. CTrack can retrieve over 75% of a user's drive accurately on average, even from highly inaccurate (175 metres raw position error) GSM data. iTrack uses a particle filter to combine inertial sensing data from the accelerometer and gyroscope with WiFi signals and accurately track a mobile phone indoors. iTrack has been implemented on the iPhone, and can track a user to within less than a metre when walking with the phone in the hand or pants pocket, over 5 x more accurately than existing WiFi localization approaches. iTrack also requires very little manual effort for training, unlike existing localization systems that require a user to visit hundreds or thousands of locations in a building and mark them on a map.by Arvind Thiagarajan.Ph.D

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018