Computational Analysis of Urban Places Using Mobile Crowdsensing

In cities, urban places provide a socio-cultural habitat for people to counterbalance the daily grind of urban life, an environment away from home and work. Places provide an environment for people to communicate, share perspectives, and in the process form new social connections. Due to the active role of places to the social fabric of city life, it is important to understand how people perceive and experience places. One fundamental construct that relates place and experience is ambiance, i.e., the impressions we ubiquitously form when we go out. Young people are key actors of urban life, specially at night, and as such play an equal role in co-creating and appropriating the urban space. Understanding how places and their youth inhabitants interact at night is a relevant urban issue. Until recently, our ability to assess the visual and perceptual qualities of urban spaces and to study the dynamics surrounding youth experiences in those spaces have been limited partly due to the lack of quantitative data. However, the growth of computational methods and tools including sensor-rich mobile devices, social multimedia platforms, and crowdsourcing tools have opened ways to measure urban perception at scale, and to deepen our understanding of nightlife as experienced by young people. In this thesis, as a first contribution, we present the design, implementation and computational analysis of four mobile crowdsensing studies involving youth populations from various countries to understand and infer phenomena related to urban places and people. We gathered a variety of explicit and implicit crowdsourced data including mobile sensor data and logs, survey responses, and multimedia content (images and videos) from hundreds of crowdworkers and thousands of users of mobile social networks. Second, we showed how crowdsensed images can be used for the computational characterization and analysis of urban perception in indoor and outdoor places. For both place types, urban perception impressions were elicited for several physical and psychological constructs using online crowdsourcing. Using low-level and deep learning features extracted from images, we automatically inferred crowdsourced judgments of indoor ambiance with a maximum R2 of 0.53 and outdoor perception with a maximum R2 of 0.49. Third, we demonstrated the feasibility to collect rich contextual data to study the physical mobility, activities, ambiance context, and social patterns of youth nightlife behavior. Fourth, using supervised machine learning techniques, we automatically classified drinking behavior of young people in an urban, real nightlife setting. Using features extracted from mobile sensor data and application logs, we obtained an overall accuracy of 76.7%. While this thesis contributes towards understanding urban perception and youth nightlife patterns in specific contexts, our research also contributes towards the computational understanding of urban places at scale with high spatial and temporal resolution, using a combination of mobile crowdsensing, social media, machine learning, multimedia analysis, and online crowdsourcing

Delivering IoT Services in Smart Cities and Environmental Monitoring through Collective Awareness, Mobile Crowdsensing and Open Data

The Internet of Things (IoT) is the paradigm that allows us to interact with the real world by means of networking-enabled devices and convert physical phenomena into valuable digital knowledge. Such a rapidly evolving field leveraged the explosion of a number of technologies, standards and platforms. Consequently, different IoT ecosystems behave as closed islands and do not interoperate with each other, thus the potential of the number of connected objects in the world is far from being totally unleashed. Typically, research efforts in tackling such challenge tend to propose a new IoT platforms or standards, however, such solutions find obstacles in keeping up the pace at which the field is evolving. Our work is different, in that it originates from the following observation: in use cases that depend on common phenomena such as Smart Cities or environmental monitoring a lot of useful data for applications is already in place somewhere or devices capable of collecting such data are already deployed. For such scenarios, we propose and study the use of Collective Awareness Paradigms (CAP), which offload data collection to a crowd of participants. We bring three main contributions: we study the feasibility of using Open Data coming from heterogeneous sources, focusing particularly on crowdsourced and user-contributed data that has the drawback of being incomplete and we then propose a State-of-the-Art algorith that automatically classifies raw crowdsourced sensor data; we design a data collection framework that uses Mobile Crowdsensing (MCS) and puts the participants and the stakeholders in a coordinated interaction together with a distributed data collection algorithm that prevents the users from collecting too much or too less data; (3) we design a Service Oriented Architecture that constitutes a unique interface to the raw data collected through CAPs through their aggregation into ad-hoc services, moreover, we provide a prototype implementation

A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd

Mobile CrowdSensing (MCS), through employing considerable workers to sense and collect data in a participatory manner, has been recognized as a promising paradigm for building many large-scale applications in a cost-effective way, such as combating COVID-19. The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies assume that the qualities of workers are known in advance, or the platform knows the qualities of workers once it receives their collected data. In reality, to reduce their costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform. So, it is very hard for the platform to evaluate the authenticity of the received data. In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem, and design an UCB-based algorithm to separate the exploration and exploitation, considering the Sensing Rates (SRs) of recruited workers as the gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We prove that our SCMABA achieves truthfulness and individual rationality. Additionally, we exhibit outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.Comment: 18 pages, 14 figure

Evaluating Sensor Data in the Context of Mobile Crowdsensing

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

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

Multi-dimensional urban sensing in sparse mobile crowdsensing

International audienceSparse mobile crowdsensing (MCS) is a promising paradigm for the large-scale urban sensing, which allows us to collect data from only a few areas (cell selection) and infer the data of other areas (data inference). It can significantly reduce the sensing cost while ensuring high data quality. Recently, large urban sensing systems often require multiple types of sensing data (e.g., publish two tasks on temperature and humidity respectively) to form a multi-dimensional urban sensing map. These multiple types of sensing data hold some inherent correlations, which can be leveraged to further reduce the sensing cost and improve the accuracy of the inferred results. In this paper, we study the multi-dimensional urban sensing in sparse MCS to jointly address the data inference and cell selection for multi-task scenarios. We exploit the intra-and inter-task correlations in data inference to deduce the data of the unsensed cells through the multi-task compressive sensing and then learn and select the most effective cell, task pairs by using reinforcement learning. To effectively capture the intra-and inter-task correlations in cell selection, we design a network structure with multiple branches, where branches extract the intra-task correlations for each task, respectively, and then catenates the results from all branches to capture the inter-task correlations among the multiple tasks. In addition, we present a two-stage online framework for reinforcement learning in practical use, including training and running phases. The extensive experiments have been conducted on two real-world urban sensing datasets, each with two types of sensing data, which verify the effectiveness of our proposed algorithms on multi-dimensional urban sensing and achieve better performances than the state-of-the-art mechanisms