6,878 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
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
QUOIN: Incentive Mechanisms for Crowd Sensing Networks
Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications
Challenges in Complex Systems Science
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda
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