408 research outputs found
Optimum Statistical Estimation with Strategic Data Sources
We propose an optimum mechanism for providing monetary incentives to the data
sources of a statistical estimator such as linear regression, so that high
quality data is provided at low cost, in the sense that the sum of payments and
estimation error is minimized. The mechanism applies to a broad range of
estimators, including linear and polynomial regression, kernel regression, and,
under some additional assumptions, ridge regression. It also generalizes to
several objectives, including minimizing estimation error subject to budget
constraints. Besides our concrete results for regression problems, we
contribute a mechanism design framework through which to design and analyze
statistical estimators whose examples are supplied by workers with cost for
labeling said examples
Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints
We study a problem of optimal information gathering from multiple data
providers that need to be incentivized to provide accurate information. This
problem arises in many real world applications that rely on crowdsourced data
sets, but where the process of obtaining data is costly. A notable example of
such a scenario is crowd sensing. To this end, we formulate the problem of
optimal information gathering as maximization of a submodular function under a
budget constraint, where the budget represents the total expected payment to
data providers. Contrary to the existing approaches, we base our payments on
incentives for accuracy and truthfulness, in particular, {\em peer-prediction}
methods that score each of the selected data providers against its best peer,
while ensuring that the minimum expected payment is above a given threshold. We
first show that the problem at hand is hard to approximate within a constant
factor that is not dependent on the properties of the payment function.
However, for given topological and analytical properties of the instance, we
construct two greedy algorithms, respectively called PPCGreedy and
PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the
optimal solution. Finally, we evaluate our methods using a realistic crowd
sensing testbed.Comment: Longer version of AAAI'18 pape
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 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
Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data
To overcome the limited coverage in traditional wireless sensor networks,
\emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To
achieve longer battery lives of user devices and incentive human involvement,
this paper presents a novel approach that seamlessly integrates MCS with
wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS),
for supporting crowd sensing with energy consumption and offering rewards as
incentives. The optimization problem is formulated to simultaneously maximize
the data utility and minimize the energy consumption for service operator, by
jointly controlling wireless-power allocation at the \emph{access point} (AP)
as well as sensing-data size, compression ratio, and sensor-transmission
duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the
optimal power allocation policy is shown to have a \emph{threshold}-based
structure with respect to a defined \emph{crowd-sensing priority} function for
each MS. Given fixed sensing-data utilities, the compression policy achieves
the optimal compression ratio. Extensive simulations are also presented to
verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206
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