55 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
A Stackelberg Game Approach Towards Socially-Aware Incentive Mechanisms for Mobile Crowdsensing (Online report)
Mobile crowdsensing has shown a great potential to address large-scale data
sensing problems by allocating sensing tasks to pervasive mobile users. The
mobile users will participate in a crowdsensing platform if they can receive
satisfactory reward. In this paper, to effectively and efficiently recruit
sufficient number of mobile users, i.e., participants, we investigate an
optimal incentive mechanism of a crowdsensing service provider. We apply a
two-stage Stackelberg game to analyze the participation level of the mobile
users and the optimal incentive mechanism of the crowdsensing service provider
using backward induction. In order to motivate the participants, the incentive
is designed by taking into account the social network effects from the
underlying mobile social domain. For example, in a crowdsensing-based road
traffic information sharing application, a user can get a better and accurate
traffic report if more users join and share their road information. We derive
the analytical expressions for the discriminatory incentive as well as the
uniform incentive mechanisms. To fit into practical scenarios, we further
formulate a Bayesian Stackelberg game with incomplete information to analyze
the interaction between the crowdsensing service provider and mobile users,
where the social structure information (the social network effects) is
uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium
are validated by identifying the best response strategies of the mobile users.
Numerical results corroborate the fact that the network effects tremendously
stimulate higher mobile participation level and greater revenue of the
crowdsensing service provider. In addition, the social structure information
helps the crowdsensing service provider to achieve greater revenue gain.Comment: Submitted for possible journal publication. arXiv admin note: text
overlap with arXiv:1711.0105
A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd
Mobile CrowdSensing (MCS), through employing considerable workers to sense
and collect data in a participatory manner, has been recognized as a promising
paradigm for building many large-scale applications in a cost-effective way,
such as combating COVID-19. The recruitment of trustworthy and high-quality
workers is an important research issue for MCS. Previous studies assume that
the qualities of workers are known in advance, or the platform knows the
qualities of workers once it receives their collected data. In reality, to
reduce their costs and thus maximize revenue, many strategic workers do not
perform their sensing tasks honestly and report fake data to the platform. So,
it is very hard for the platform to evaluate the authenticity of the received
data. In this paper, an incentive mechanism named Semi-supervision based
Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve
the recruitment problem of multiple unknown and strategic workers in MCS.
First, we model the worker recruitment as a multi-armed bandit reverse auction
problem, and design an UCB-based algorithm to separate the exploration and
exploitation, considering the Sensing Rates (SRs) of recruited workers as the
gain of the bandit. Next, a Semi-supervised Sensing Rate Learning (SSRL)
approach is proposed to quickly and accurately obtain the workers' SRs, which
consists of two phases, supervision and self-supervision. Last, SCMABA is
designed organically combining the SRs acquisition mechanism with multi-armed
bandit reverse auction, where supervised SR learning is used in the
exploration, and the self-supervised one is used in the exploitation. We prove
that our SCMABA achieves truthfulness and individual rationality. Additionally,
we exhibit outstanding performances of the SCMABA mechanism through in-depth
simulations of real-world data traces.Comment: 18 pages, 14 figure
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns.
Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas
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