3,211 research outputs found
A Photo-Based Mobile Crowdsourcing Framework for Event Reporting
Mobile Crowdsourcing (MCS) photo-based is an arising field of interest and a
trending topic in the domain of ubiquitous computing. It has recently drawn
substantial attention of the smart cities and urban computing communities. In
fact, the built-in cameras of mobile devices are becoming the most common way
for visual logging techniques in our daily lives. MCS photo-based frameworks
collect photos in a distributed way in which a large number of contributors
upload photos whenever and wherever it is suitable. This inevitably leads to
evolving picture streams which possibly contain misleading and redundant
information that affects the task result. In order to overcome these issues, we
develop, in this paper, a solution for selecting highly relevant data from an
evolving picture stream and ensuring correct submission. The proposed
photo-based MCS framework for event reporting incorporates (i) a deep learning
model to eliminate false submissions and ensure photos credibility and (ii) an
A-Tree shape data structure model for clustering streaming pictures to reduce
information redundancy and provide maximum event coverage. Simulation results
indicate that the implemented framework can effectively reduce false
submissions and select a subset with high utility coverage with low redundancy
ratio from the streaming data.Comment: Published in 2019 IEEE 62nd International Midwest Symposium on
Circuits and Systems (MWSCAS
A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing
Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing
tasks, traditionally performed by employees or contractors, to a large group of
smart-phone users by means of an open call. With the increasing complexity of
the crowdsourcing applications, requesters find it essential to harness the
power of collaboration among the workers by forming teams of skilled workers
satisfying their complex tasks' requirements. This type of MCS is called
Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on
the aspect of team skills maximization. Other team formation studies on social
networks (SNs) have only focused on social relationship maximization. In this
paper, we present a hybrid approach where requesters are able to hire a team
that, not only has the required expertise, but also is socially connected and
can accomplish tasks collaboratively. Because team formation in CMCS is proven
to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge
about their SN neighbors and asks a designated leader to recruit a suitable
team. The proposed algorithm is inspired from the optimal stopping strategies
and uses the odds-algorithm to compute its output. Experimental results show
that, compared to the benchmark exponential optimal solution, the proposed
approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International
Conference on Microelectronics (ICM
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Crowd control : organizing the crowd at Yelp
This dissertation investigates how businesses are able to align the collective actions of a disconnected crowd with the strategic goals of the organization. I examined this questions within the context of the business review website Yelp through a quantitative analysis of nearly 60,000 business reviews, 17 in-depth qualitative interviews with reviewers, and a two-year ethnography. Interpreting the results of this data within the framework of the collective action space (Bimber, Flanagin, & Stohl, 2012) indicates that Yelp is able to manage the contributions of a relatively small subset of reviewers through the Yelp Elite Squad. Rather than simply motivating more reviews, the Elite Squad encouraged reviewers to interact more personally with other reviewers and accept increased institutional engagement with Yelp. In encouraging members of the crowd to produce online reviews within this context, Yelp was able to use organizational culture as a control strategy for encouraging Elite reviewers to adopt a pre-mediated reviewing approach to their reviews. This increased the frequency of moderate reviews and decreased the frequency of extreme reviews. This behavior ultimately furthers the organizational goals of Yelp, as moderate reviews are considered to be more helpful for reviews of businesses. Finally, implications for crowdsourcing, big data analysis, and theory are discussed
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
Technology for Good: Innovative Use of Technology by Charities
Technology for Good identifies ten technologies being used by charitable organizations in innovative ways. The report briefly introduces each technology and provides examples of how those technologies are being used.Examples are drawn from a broad spectrum of organizations working on widely varied issues around the globe. This makes Technology for Good a unique repository of inspiration for the public and private sectors, funders, and other change makers who support the creation and use of technology for social good
An investigation into the role of crowdsourcing in generating information for flood risk management
Flooding is a major global hazard whose management relies on an accurate understanding of its risks. Crowdsourcing represents a major opportunity for supporting flood risk management as members of the public are highly capable of producing useful flood information. This thesis explores a wide range of issues related to flood crowdsourcing using an interdisciplinary approach. Through an examination of 31 different projects a flood crowdsourcing typology was developed. This identified five key types of flood crowdsourcing: i) Incident Reporting, ii) Media Engagement, iii) Collaborative Mapping, iv) Online Volunteering and v) Passive VGI. These represent a wide range of initiatives with radically different aims, objectives, datasets and relationships with volunteers. Online Volunteering was explored in greater detail using Tomnod as a case study. This is a micro-tasking platform in which volunteers analyse satellite imagery to support disaster response. Volunteer motivations for participating on Tomnod were found to be largely altruistic. Demographics of participants were significant, with retirement, disability or long-term health problems identified as major drivers for participation. Many participants emphasised that effective communication between volunteers and the site owner is strongly linked to their appreciation of the platform. In addition, the feedback on the quality and impact of their contributions was found to be crucial in maintaining interest. Through an examination of their contributions, volunteers were found to be able to ascertain with a higher degree of accuracy, many features in satellite imagery which supervised image classification struggled to identify. This was more pronounced in poorer quality imagery where image classification had a very low accuracy. However, supervised classification was found to be far more systematic and succeeded in identifying impacts in many regions which were missed by volunteers. The efficacy of using crowdsourcing for flood risk management was explored further through the iterative development of a Collaborative Mapping web-platform called Floodcrowd. Through interviews and focus groups, stakeholders from the public and private sector expressed an interest in crowdsourcing as a tool for supporting flood risk management. Types of data which stakeholders are particularly interested in with regards to crowdsourcing differ between organisations. Yet, they typically include flood depths, photos, timeframes of events and historical background information. Through engagement activities, many citizens were found to be able and motivated to share such observations. Yet, motivations were strongly affected by the level of attention their contributions receive from authorities. This presents many opportunities as well as challenges for ensuring that the future of flood crowdsourcing improves flood risk management and does not damage stakeholder relationships with participants
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