1,466 research outputs found
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
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
Mobile crowd sensing architectural frameworks: A comprehensive survey
Mobile Crowd Sensing has emerged as a new sensing paradigm, efficiently exploiting human intelligence and mobility in conjunction with advanced capabilities and proliferation of mobile devices. In order for MCS applications to reach their full potentials, a number of research challenges should be sufficiently addressed. The aim of this paper is to survey representative mobile crowd sensing applications and frameworks proposed in related research literature, analyze their distinct features and discuss on their relative merits and weaknesses, highlighting also potential solutions, in order to take a step closer to the definition of a unified MCS architectural framework
Privacy Management and Optimal Pricing in People-Centric Sensing
With the emerging sensing technologies such as mobile crowdsensing and
Internet of Things (IoT), people-centric data can be efficiently collected and
used for analytics and optimization purposes. This data is typically required
to develop and render people-centric services. In this paper, we address the
privacy implication, optimal pricing, and bundling of people-centric services.
We first define the inverse correlation between the service quality and privacy
level from data analytics perspectives. We then present the profit maximization
models of selling standalone, complementary, and substitute services.
Specifically, the closed-form solutions of the optimal privacy level and
subscription fee are derived to maximize the gross profit of service providers.
For interrelated people-centric services, we show that cooperation by service
bundling of complementary services is profitable compared to the separate sales
but detrimental for substitutes. We also show that the market value of a
service bundle is correlated with the degree of contingency between the
interrelated services. Finally, we incorporate the profit sharing models from
game theory for dividing the bundling profit among the cooperative service
providers.Comment: 16 page
- …