6,462 research outputs found
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
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
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
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
Participatory sensing as an enabler for self-organisation in future cellular networks
In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells
Trust and obfuscation principles for quality of information in emerging pervasive environments
Non peer reviewedPostprin
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