1,835 research outputs found
Consensus-Based Group Task Assignment with Social Impact in Spatial Crowdsourcing
Abstract With the pervasiveness of GPS-enabled smart devices and increased wireless communication technologies, spatial crowdsourcing (SC) has drawn increasing attention in assigning location-sensitive tasks to moving workers. In real-world scenarios, for the complex tasks, SC is more likely to assign each task to more than one worker, called group task assignment (GTA), for the reason that an individual worker cannot complete the task well by herself. It is a challenging issue to assign worker groups the tasks that they are interested in and willing to perform. In this paper, we propose a novel framework for group task assignment based on worker groups’ preferences, which includes two components: social impact-based preference modeling (SIPM) and preference-aware group task assignment (PGTA). SIPM employs a bipartite graph embedding model and the attention mechanism to learn the social impact-based preferences of different worker groups on different task categories. PGTA utilizes an optimal task assignment algorithm based on the tree decomposition technique to maximize the overall task assignments, in which we give higher priorities to the worker groups showing more interests in the tasks. We further optimize the original framework by proposing strategies to improve the effectiveness of group task assignment, wherein a deep learning method and the group consensus are taken into consideration. Extensive empirical studies verify that the proposed techniques and optimization strategies can settle the problem nicely
Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation
A human computation system can be viewed as a distributed system in which the
processors are humans, called workers. Such systems harness the cognitive power
of a group of workers connected to the Internet to execute relatively simple
tasks, whose solutions, once grouped, solve a problem that systems equipped
with only machines could not solve satisfactorily. Examples of such systems are
Amazon Mechanical Turk and the Zooniverse platform. A human computation
application comprises a group of tasks, each of them can be performed by one
worker. Tasks might have dependencies among each other. In this study, we
propose a theoretical framework to analyze such type of application from a
distributed systems point of view. Our framework is established on three
dimensions that represent different perspectives in which human computation
applications can be approached: quality-of-service requirements, design and
management strategies, and human aspects. By using this framework, we review
human computation in the perspective of programmers seeking to improve the
design of human computation applications and managers seeking to increase the
effectiveness of human computation infrastructures in running such
applications. In doing so, besides integrating and organizing what has been
done in this direction, we also put into perspective the fact that the human
aspects of the workers in such systems introduce new challenges in terms of,
for example, task assignment, dependency management, and fault prevention and
tolerance. We discuss how they are related to distributed systems and other
areas of knowledge.Comment: 3 figures, 1 tabl
Spatial Crowdsourcing Task Allocation Scheme for Massive Data with Spatial Heterogeneity
Spatial crowdsourcing (SC) engages large worker pools for location-based
tasks, attracting growing research interest. However, prior SC task allocation
approaches exhibit limitations in computational efficiency, balanced matching,
and participation incentives. To address these challenges, we propose a
graph-based allocation framework optimized for massive heterogeneous spatial
data. The framework first clusters similar tasks and workers separately to
reduce allocation scale. Next, it constructs novel non-crossing graph
structures to model balanced adjacencies between unevenly distributed tasks and
workers. Based on the graphs, a bidirectional worker-task matching scheme is
designed to produce allocations optimized for mutual interests. Extensive
experiments on real-world datasets analyze the performance under various
parameter settings
On efficient and scalable time-continuous spatial crowdsourcing
The proliferation of advanced mobile terminals opened up a new crowdsourcing
avenue, spatial crowdsourcing, to utilize the crowd potential to perform
real-world tasks. In this work, we study a new type of spatial crowdsourcing,
called time-continuous spatial crowdsourcing (TCSC in short). It supports broad
applications for long-term continuous spatial data acquisition, ranging from
environmental monitoring to traffic surveillance in citizen science and
crowdsourcing projects. However, due to limited budgets and limited
availability of workers in practice, the data collected is often incomplete,
incurring data deficiency problem. To tackle that, in this work, we first
propose an entropy-based quality metric, which captures the joint effects of
incompletion in data acquisition and the imprecision in data interpolation.
Based on that, we investigate quality-aware task assignment methods for both
single- and multi-task scenarios. We show the NP-hardness of the single-task
case, and design polynomial-time algorithms with guaranteed approximation
ratios. We study novel indexing and pruning techniques for further enhancing
the performance in practice. Then, we extend the solution to multi-task
scenarios and devise a parallel framework for speeding up the process of
optimization. We conduct extensive experiments on both real and synthetic
datasets to show the effectiveness of our proposals
Task Allocation among Connected Devices: Requirements, Approaches and Challenges
Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning
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
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
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
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