896 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
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
The recent proliferation of increasingly capable mobile devices has given
rise to mobile crowd sensing (MCS) systems that outsource the collection of
sensory data to a crowd of participating workers that carry various mobile
devices. Aware of the paramount importance of effectively incentivizing
participation in such systems, the research community has proposed a wide
variety of incentive mechanisms. However, different from most of these existing
mechanisms which assume the existence of only one data requester, we consider
MCS systems with multiple data requesters, which are actually more common in
practice. Specifically, our incentive mechanism is based on double auction, and
is able to stimulate the participation of both data requesters and workers. In
real practice, the incentive mechanism is typically not an isolated module, but
interacts with the data aggregation mechanism that aggregates workers' data.
For this reason, we propose CENTURION, a novel integrated framework for
multi-requester MCS systems, consisting of the aforementioned incentive and
data aggregation mechanism. CENTURION's incentive mechanism satisfies
truthfulness, individual rationality, computational efficiency, as well as
guaranteeing non-negative social welfare, and its data aggregation mechanism
generates highly accurate aggregated results. The desirable properties of
CENTURION are validated through both theoretical analysis and extensive
simulations
A Game-Theoretic Model Motivated by the DARPA Network Challenge
In this paper we propose a game-theoretic model to analyze events similar to
the 2009 \emph{DARPA Network Challenge}, which was organized by the Defense
Advanced Research Projects Agency (DARPA) for exploring the roles that the
Internet and social networks play in incentivizing wide-area collaborations.
The challenge was to form a group that would be the first to find the locations
of ten moored weather balloons across the United States. We consider a model in
which people (who can form groups) are located in some topology with a
fixed coverage volume around each person's geographical location. We consider
various topologies where the players can be located such as the Euclidean
-dimension space and the vertices of a graph. A balloon is placed in the
space and a group wins if it is the first one to report the location of the
balloon. A larger team has a higher probability of finding the balloon, but we
assume that the prize money is divided equally among the team members. Hence
there is a competing tension to keep teams as small as possible.
\emph{Risk aversion} is the reluctance of a person to accept a bargain with
an uncertain payoff rather than another bargain with a more certain, but
possibly lower, expected payoff. In our model we consider the \emph{isoelastic}
utility function derived from the Arrow-Pratt measure of relative risk
aversion. The main aim is to analyze the structures of the groups in Nash
equilibria for our model. For the -dimensional Euclidean space ()
and the class of bounded degree regular graphs we show that in any Nash
Equilibrium the \emph{richest} group (having maximum expected utility per
person) covers a constant fraction of the total volume
A Socially-Aware Incentive Mechanism for Mobile Crowdsensing Service Market
Mobile Crowdsensing has shown a great potential to address large-scale
problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs
will participate in a Crowdsensing platform if they can receive satisfactory
reward. In this paper, in order to effectively and efficiently recruit
sufficient MUs, i.e., participants, we investigate an optimal reward mechanism
of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and
participating as a two-stage game, and analyze the MUs' participation level and
the CSP's optimal reward mechanism using backward induction. At the same time,
the reward is designed taking the underlying social network effects amid the
mobile social network into account, for motivating the participants. Namely,
one MU will obtain additional benefits from information contributed or shared
by local neighbours in social networks. We derive the analytical expressions
for the discriminatory reward as well as uniform reward with complete
information, and approximations of reward incentive with incomplete
information. Performance evaluation reveals that the network effects
tremendously stimulate higher mobile participation level and greater revenue of
the CSP. In addition, the discriminatory reward enables the CSP to extract
greater surplus from this Crowdsensing service market.Comment: 7 pages, accepted by IEEE Globecom'1
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
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