Human Computation and Convergence

Humans are the most effective integrators and producers of information, directly and through the use of information-processing inventions. As these inventions become increasingly sophisticated, the substantive role of humans in processing information will tend toward capabilities that derive from our most complex cognitive processes, e.g., abstraction, creativity, and applied world knowledge. Through the advancement of human computation - methods that leverage the respective strengths of humans and machines in distributed information-processing systems - formerly discrete processes will combine synergistically into increasingly integrated and complex information processing systems. These new, collective systems will exhibit an unprecedented degree of predictive accuracy in modeling physical and techno-social processes, and may ultimately coalesce into a single unified predictive organism, with the capacity to address societies most wicked problems and achieve planetary homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added references to page 1 and 3, and corrected typ

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

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

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)

A multi-site study on walkability, data sharing and privacy perception using mobile sensing data gathered from the mk-sense platform

Walking is a fundamental part of a physically active lifestyle, it is one of everyday activities that positively impacts health and wellbeing. In this paper we describe the challenges and experiences of conducting a sensing campaign in the wild. We make use of mk-sense; a software platform to facilitate the deployment of collaborative sensing campaigns. We elaborate on two cross-cultural studies conducted in four different countries (Mexico, Turkey, Spain, and Switzerland) with a total of 77 participants. We present a detailed description of the data collected from one of the studies aimed at measuring walkability around three different university campuses. The analysis of the data shows that walkability can be assessed using information from the sensors in the smartphones and results from surveys answered by participants. In addition, we analyze issues about data sharing and privacy awareness

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN

Crowdsourcing and the folksonomy of emergency response: the construction of a mediated subject

This article explores the role of digital platforms in the involvement of citizens in disaster response, relying on an analysis of metadata and of the structure of classification. It adopts the analytical apparatus of Cultural-Historical Activity Theory (Vygotsky, Leontiev, Engeström) and the notion of governmentality (Foucault) in order to conduct a critical comparative analysis of how crowdsourcing platforms construct the relationship between citizens and disasters. The article identifies three regimes of classification (informing, alerting and engagement) and explores the structures of classification for mobilization of citizens’ resources. The notion of governmentality allows us to identify the struggle around the structure of classification as a struggle between the institutional actors interested in controlling citizens’ resources and those actors who are interested in citizen engagement and the synergy between independent and institutional actors as a part of the disaster response. The article suggests the notion of the folksonomy of activity, identifying situations where citizens are able to participate in the definition of their relationships with disaster through participating in classification. It also discusses the visibility of classification and the generativity of classification as a part of citizen–disaster (subject–object) relationships

An Empirical Analysis of System-generated Data in Location-based Crowdsourcing

This paper develops a research model explaining how task location and incentives affect the take up and, for those tasks that are processed, the time to start. For an empirical analysis, we use the system-generated data of all 1860 location-based crowdsourcing tasks in Berlin available on the Streetspotr platform within one year. The results indicate that while the population density of the task location does not influence the probability that some crowdworker will eventually process the task, a task located in a more densely-populated area tends to be taken up more quickly. Moreover, the take-up probability is expected to increase as the monetary and non-monetary incentives are raised. However, both increasing the monetary incentives and lowering the non-monetary incentives tends to shorten the time to start. This suggests that high non-monetary incentives with which unattractive tasks are endowed do not entice the crowdworkers to quickly set about processing these tasks

A Service Based Architecture for Multidisciplinary IoT Experiments with Crowdsourced Resources

Research on emerging networking paradigms, such as Mobile Crowdsensing Systems, requires new types of experiments to be conducted and an increasing spectrum of devices to be supported by experimenting facilities. In this work, we present a service based architecture for IoT testbeds which (a) exposes the operations of a testbed as services by following the Testbed as a Service (TBaaS) paradigm; (b) enables diverse facilities to be federated in a scalable and standardized way and (c) enables the seamless integration of crowdsourced resources (e.g. smartphones and wearables) and their abstraction as regular IoT resources. The architecture enables an experimenter to access a diverse set of resources and orchestrate experiments via a common interface by hiding the underlying heterogeneity and complexity. This way, the field of IoT experimentation with real resources is further promoted and broadened to also address researchers from other fields and discipline