3,625 research outputs found

    Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

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    Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

    Crowd sensing and forecasting for Smart Cities

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    Dissertação de mestrado integrado em Engenharia InformáticaA utilização de inteligência sob forma de tecnologia no nosso dia-a-dia é uma realidade em crescimento e, portanto, devemos fazer uso da tecnologia disponível para melhorar várias áreas do nosso quotidiano. Por exemplo, a tecnologia atual permite a conceção de sensores inteligentes, mais especificamente sensores de multidão, para detetar passiva mente dispositivos como smartphones ou smartwatches através de probe requests emitidos por estes dispositivos que, por sua vez, fazem parte de um processo de comunicação que ocorre sempre que o Wi-Fi dos dispositivos está ativado. Adicionalmente, crowd sensing - uma solução de Ambient Intelligence (AmI) - é estudada hoje em dia em várias áreas com bons resultados. Portanto, esta dissertação visa investigar e utilizar sensores de multidão para capturar passivamente dados acerca da densidade de multidões, explorar as capacidades do sensor escolhido, analisar e processar os dados para obter melhores estimativas, e conceber e desenvolver modelos de Machine Learning (ML) para prever a densidade nas áreas sensorizadas. Áreas nas quais o sensor de multidão está inserido - AmI, Smart Cities, Wi-Fi Probing - são estudadas, juntamente com a análise de diferentes abordagens ao crowd sensing, assim como paradigmas e algoritmos de ML. Em seguida, é explicado como os dados foram capturados e analisados, seguido por uma experiência feita às capacidades do sensor. Além disso, é apresentado como os modelos de ML foram concebidos e otimizados. Finalmente, os resultados dos vários testes de ML são discutidos e o modelo com melhor desempenho é apresentado. A investigação e os resultados práticos abrem perspetivas importantes para a implementação deste tipo de soluções na nossa vida diária.Bringing intelligence to our everyday environments is a growing reality and therefore we should take advantage of the technology available to improve several areas of our daily life. For example, current technology allows the conception of smart scanners, more specifically crowd sensors, to passively detect devices such as smartphones or smartwatches through probe requests emitted by such devices, that, in turn, are part of a communication process that happens every time the devices’ Wi-Fi is enabled. Additionally, crowd sensing - an Ambient Intelligence (AmI) solution - is being studied nowadays in several areas with good results. Therefore, this dissertation aims to research and use crowd sensors to passively collect crowd density data, explore the capabilities of the chosen sensor, analyse and process the data to get better estimations and conceive and develop Machine Learning (ML) models to forecast the density of the sensed areas. Areas in which crowd sensing is inserted - AmI, Smart Cities, Wi-Fi probing - are studied, along with the analysis of different crowd sensing approaches and ML paradigms and algorithms. Then, it’s explained how the data was collected and analysed together with the insights obtained from it, followed by an experiment done on the crowd sensor capabilities. Moreover, it’s presented how the ML models were conceived and tuned. Finally, the results from the ML several tests are discussed and the best performing model is found. The investigation, together with practical results, opens important perspectives for the implementation of these kinds of solutions in our daily lives

    Evaluating Sensor Data in the Context of Mobile Crowdsensing

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    With the recent rise of the Internet of Things the prevalence of mobile sensors in our daily life experienced a huge surge. Mobile crowdsensing (MCS) is a new emerging paradigm that realizes the utility and ubiquity of smartphones and more precisely their incorporated smart sensors. By using the mobile phones and data of ordinary citizens, many problems have to be solved when designing an MCS-application. What data is needed in order to obtain the wanted results? Should the calculations be executed locally or on a server? How can the quality of data be improved? How can the data best be evaluated? These problems are addressed by the design of a streamlined approach of how to create an MCS-application while having all these problems in mind. In order to design this approach, an exhaustive literature research on existing MCS-applications was done and to validate this approach a new application was designed with its help. The procedure of designing and implementing this application went smoothly and thus shows the applicability of the approach

    Practical server-side WiFi-based indoor localization: Addressing cardinality & outlier challenges for improved occupancy estimation

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    National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ
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