763 research outputs found
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
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
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
Mobile crowdsensing for road sustainability: exploitability of publicly-sourced data
ABSTRACTThis paper examines the opportunities and the economic benefits of exploiting publicly-sourced datasets of road surface quality. Crowdsourcing and crowdsensing initiatives channel the parti..
MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications
Mobile smartphones along with embedded sensors have become an efficient
enabler for various mobile applications including opportunistic sensing. The
hi-tech advances in smartphones are opening up a world of possibilities. This
paper proposes a mobile collaborative platform called MOSDEN that enables and
supports opportunistic sensing at run time. MOSDEN captures and shares sensor
data across multiple apps, smartphones and users. MOSDEN supports the emerging
trend of separating sensors from application-specific processing, storing and
sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing
the efforts in developing novel opportunistic sensing applications. MOSDEN has
been implemented on Android-based smartphones and tablets. Experimental
evaluations validate the scalability and energy efficiency of MOSDEN and its
suitability towards real world applications. The results of evaluation and
lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing,
2014. arXiv admin note: substantial text overlap with arXiv:1310.405
Integrating IoT-Sensing and Crowdsensing with Privacy: Privacy-Preserving Hybrid Sensing for Smart Cities
Data sensing and gathering is an essential task for various
information-driven services in smart cities. On the one hand, Internet of
Things (IoT) sensors can be deployed at certain fixed locations to capture data
reliably but suffer from limited sensing coverage. On the other hand, data can
also be gathered dynamically through crowdsensing contributed by voluntary
users but suffer from its unreliability and the lack of incentives for users'
contributions. In this paper, we explore an integrated paradigm called "hybrid
sensing" that harnesses both IoT-sensing and crowdsensing in a complementary
manner. In hybrid sensing, users are incentivized to provide sensing data not
covered by IoT sensors and provide crowdsourced feedback to assist in
calibrating IoT-sensing. Their contributions will be rewarded with credits that
can be redeemed to retrieve synthesized information from the hybrid system. In
this paper, we develop a hybrid sensing system that supports explicit user
privacy -- IoT sensors are obscured physically to prevent capturing private
user data, and users interact with a crowdsensing server via a
privacy-preserving protocol to preserve their anonymity. A key application of
our system is smart parking, by which users can inquire and find the available
parking spaces in outdoor parking lots. We implemented our hybrid sensing
system for smart parking and conducted extensive empirical evaluations.
Finally, our hybrid sensing system can be potentially applied to other
information-driven services in smart cities.Comment: To appear in ACM Transactions on Internet of Thing
Mobile Crowdsensing Services for Tinnitus Assessment and Patient Feedback
Assessment of chronic disorders requires new ways of data collection compared to the traditional pen & paper based approaches. For example, tinnitus, the phantom sensation of sound, is a highly prevalent disorder that is difficult to treat; i.e., available treatments are only effective for patient subgroups. In most individuals with tinnitus, loudness and annoyance of tinnitus varies over time. Currently, established assessment methods of tinnitus neither systematically assess this moment-to-moment variability nor environmental factors having an effect on tinnitus loudness and distress. However, information of individual fluctuations and the effect of envi-ronmental factors on the tinnitus might represent important information for tinnitus subtyping and for individualized treat-ment. In this context, a promising approach for collecting ecological valid longitudinal datasets at rather low costs is mobile crowdsensing. In the TrackYourTinnitus project, we developed an advanced mobile crowdsensing platform to reveal more detailed information about the course of tinnitus over time. In this paper, the patient mobile feedback service as a particular component of the platform is presented. It was developed to provide patients with aggregated information about the variation of their tinnitus over time. This mobile feedback service shall help a patient to demystify the tinnitus and to get better control of it, which should facilitate coping with this chronic health condition. As the basic principles and design of this mobile services are also applicable to other chronic disorders, promising perspectives for disorder management and clinical research arise
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
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