2,708 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
HyTasker:Hybrid Task Allocation in Mobile Crowd Sensing
Task allocation is a major challenge in Mobile Crowd Sensing (MCS). While previous task allocation approaches follow either the opportunistic or participatory mode, this paper proposes to integrate these two complementary modes in a two-phased hybrid framework called HyTasker. In the offline phase, a group of workers (called opportunistic workers ) are selected, and they complete MCS tasks during their daily routines (i.e., opportunistic mode). In the online phase, we assign another set of workers (called participatory workers ) and require them to move specifically to perform tasks that are not completed by the opportunistic workers (i.e., participatory mode). Instead of considering these two phases separately, HyTasker jointly optimizes them with a total incentive budget constraint. In particular, when selecting opportunistic workers in the offline phase of HyTasker, we propose a novel algorithm that simultaneously considers the predicted task assignment for the participatory workers, in which the density and mobility of participatory workers are taken into account. Experiments on two real-world mobility datasets demonstrate that HyTasker outperforms other methods with more completed tasks under the same budget constraint
Competition-Congestion-Aware Stable Worker-Task Matching in Mobile Crowd Sensing
Mobile Crowd Sensing is an emerging sensing paradigm that employs massive number of workers’ mobile devices to realize data collection. Unlike most task allocation mechanisms that aim at optimizing the global system performance, stable matching considers workers are selfish and rational individuals, which has become a hotspot in MCS. However, existing stable matching mechanisms lack deep consideration regarding the effects of workers’ competition phenomena and complex behaviors. To address the above issues, this paper investigates the competition-congestion-aware stable matching problem as a multi-objective optimization task allocation problem considering the competition of workers for tasks. First, a worker decision game based on congestion game theory is designed to assist workers in making decisions, which avoids fierce competition and improves worker satisfaction. On this basis, a stable matching algorithm based on extended deferred acceptance algorithm is designed to make workers and tasks mapping stable, and to construct a shortest task execution route for each worker. Simulation results show that the designed model and algorithm are effective in terms of worker satisfaction and platform benefit. IEE
Location Privacy in Spatial Crowdsourcing
Spatial crowdsourcing (SC) is a new platform that engages individuals in
collecting and analyzing environmental, social and other spatiotemporal
information. With SC, requesters outsource their spatiotemporal tasks to a set
of workers, who will perform the tasks by physically traveling to the tasks'
locations. This chapter identifies privacy threats toward both workers and
requesters during the two main phases of spatial crowdsourcing, tasking and
reporting. Tasking is the process of identifying which tasks should be assigned
to which workers. This process is handled by a spatial crowdsourcing server
(SC-server). The latter phase is reporting, in which workers travel to the
tasks' locations, complete the tasks and upload their reports to the SC-server.
The challenge is to enable effective and efficient tasking as well as reporting
in SC without disclosing the actual locations of workers (at least until they
agree to perform a task) and the tasks themselves (at least to workers who are
not assigned to those tasks). This chapter aims to provide an overview of the
state-of-the-art in protecting users' location privacy in spatial
crowdsourcing. We provide a comparative study of a diverse set of solutions in
terms of task publishing modes (push vs. pull), problem focuses (tasking and
reporting), threats (server, requester and worker), and underlying technical
approaches (from pseudonymity, cloaking, and perturbation to exchange-based and
encryption-based techniques). The strengths and drawbacks of the techniques are
highlighted, leading to a discussion of open problems and future work
Collaborative Route Planning of UAVs, Workers and Cars for Crowdsensing in Disaster Response
Efficiently obtaining the up-to-date information in the disaster-stricken
area is the key to successful disaster response. Unmanned aerial vehicles
(UAVs), workers and cars can collaborate to accomplish sensing tasks, such as
data collection, in disaster-stricken areas. In this paper, we explicitly
address the route planning for a group of agents, including UAVs, workers, and
cars, with the goal of maximizing the task completion rate. We propose
MANF-RL-RP, a heterogeneous multi-agent route planning algorithm that
incorporates several efficient designs, including global-local dual information
processing and a tailored model structure for heterogeneous multi-agent
systems. Global-local dual information processing encompasses the extraction
and dissemination of spatial features from global information, as well as the
partitioning and filtering of local information from individual agents.
Regarding the construction of the model structure for heterogeneous
multi-agent, we perform the following work. We design the same data structure
to represent the states of different agents, prove the Markovian property of
the decision-making process of agents to simplify the model structure, and also
design a reasonable reward function to train the model. Finally, we conducted
detailed experiments based on the rich simulation data. In comparison to the
baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has
exhibited a significant improvement in terms of task completion rate
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Future internet enablers for VGI applications
This paper presents the authors experiences with the development of mobile Volunteered Geographic Information (VGI) applications in the context of the ENVIROFI project and Future Internet Public Private Partnership (FI-PPP) FP7 research programme.FI-PPP has an ambitious goal of developing a set of Generic FI Enablers (GEs) - software and hardware tools that will simplify development of thematic future internet applications. Our role in the programme was to provide requirements and assess the usability of the GEs from the point of view of the environmental usage area, In addition, we specified and developed three proof of concept implementations of environmental FI applications, and a set of specific environmental enablers (SEs) complementing the functionality offered by GEs. Rather than trying to rebuild the whole infrastructure of the Environmental Information Space (EIS), we concentrated on two aspects: (1) how to assure the existing and future EIS services and applications can be integrated and reused in FI context; and (2) how to profit from the GEs in future environmental applications.This paper concentrates on the GEs and SEs which were used in two of the ENVIROFI pilots which are representative for the emerging class of Volunteered Geographic Information (VGI) use-cases: one of them is pertinent to biodiversity and another to influence of weather and airborne pollution on users’ wellbeing. In VGI applications, the EIS and SensorWeb overlap with the Social web and potentially huge amounts of information from mobile citizens needs to be assessed and fused with the observations from official sources. On the whole, the authors are confident that the FI-PPP programme will greatly influence the EIS, but the paper also warns of the shortcomings in the current GE implementations and provides recommendations for further developments
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