289 research outputs found
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
Task bundling in worker‐centric mobile crowdsensing
Most existing research about task allocation in mobile crowdsensing mainly focus on requester-centric mobile crowdsensing (RCMCS), where the requester assigns tasks to workers to maximize his/her benefits. A worker in RCMCS might suffer benefit damage because the tasks assigned to him/her may not maximize his/her benefit. Contrarily, worker-centric mobile crowdsensing (WCMCS), where workers autonomously select tasks to accomplish to maximize their benefits, does not receive enough attention. The workers in WCMCS can maximize their benefits, but the requester in WCMCS will suffer benefit damage (cannot maximize the number of expected completed tasks). It is hard to maximize the number of expected completed tasks in WCMCS, because some tasks may be selected by no workers, while others may be selected by many workers. In this paper, we apply task bundling to address this issue, and we formulate a novel task bundling problem in WCMCS with the objective of maximizing the number of expected completed tasks. To solve this problem, we design an algorithm named LocTrajBundling which bundles tasks based on the location of tasks and the trajectories of workers. Experimental results show that, compared with other algorithms, our algorithm can achieve a better performance in maximizing the number of expected completed tasks
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
Decentralized Online Learning in Task Assignment Games for Mobile Crowdsensing
The problem of coordinated data collection is studied for a mobile
crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially
publishes sensing tasks to the available mobile units (MUs) that signal their
willingness to participate in a task by sending sensing offers back to the
MCSP. From the received offers, the MCSP decides the task assignment. A stable
task assignment must address two challenges: the MCSP's and MUs' conflicting
goals, and the uncertainty about the MUs' required efforts and preferences. To
overcome these challenges a novel decentralized approach combining matching
theory and online learning, called collision-avoidance multi-armed bandit with
strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem
is modeled as a matching game considering the MCSP's and MUs' individual goals
while the MUs learn their efforts online. Our innovative "free-sensing"
mechanism significantly improves the MU's learning process while reducing
collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the
loss of learning, is analytically shown to be bounded by a sublinear function,
ensuring the convergence to a stable optimal solution. Simulation results show
that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to
state-of-the-art methods while reducing the average task completion time by at
least 16%
An Efficient Collaboration and Incentive Mechanism for Internet-of-Vehicles (IoVs) with Secured Information Exchange Based on Blockchains
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordWith the rapid development of Internet-of-Things
(IoT), mobile crowdsensing, i.e., outsourcing sensing tasks to
mobile devices or vehicles, has been proposed to address the
problem of data collection in the scenarios such as smart city.
Despite its benefits for a wide range of applications, mobile
crowdsensing lacks an efficient incentive mechanism, restricting
the development of IoT applications, especially for Internet-ofVehicles (IoV) – a typical example of IoT applications; this
is because vehicles are usually reluctant to participate these
sensing tasks. Moreover, in practice some sensing tasks may
arrive suddenly (called an emergent task) in the IoV environment,
but the resources of a single vehicle may be insufficient to
handle, and thus multi-vehicles collaboration is required. In
this case, the incentive mechanisms for the participation of
multiple vehicles and the task scheduling for their collaborations
are collectively needed. To address this important problem, we
firstly propose a new model for the scenario of two vehicles
collaboration, considering the situation of emergent appearance
of a task. In this model, for a general sensing task, we propose
a bidding mechanism to better encourage vehicles to contribute
their resources, and the tasks for those vehicles are scheduled
accordingly. Secondly, for an emergent task, a novel time-window
based method is devised to manage the tasks among vehicles
and to incent the vehicles to participate. Finally, we develop
a blockchain framework to achieve the secured information
exchange through smart contract for the proposed models in
IoV.National Key Research and Development Program of ChinaNational Natural Science Foundation of China (NSFC)Purple Mountain Laboratory: Networking, Communications and SecurityAcademician Expert Workstation of Bitvalue Technology (Hunan) Company Limite
- …