4,788 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
Living Innovation Laboratory Model Design and Implementation
Living Innovation Laboratory (LIL) is an open and recyclable way for
multidisciplinary researchers to remote control resources and co-develop user
centered projects. In the past few years, there were several papers about LIL
published and trying to discuss and define the model and architecture of LIL.
People all acknowledge about the three characteristics of LIL: user centered,
co-creation, and context aware, which make it distinguished from test platform
and other innovation approaches. Its existing model consists of five phases:
initialization, preparation, formation, development, and evaluation.
Goal Net is a goal-oriented methodology to formularize a progress. In this
thesis, Goal Net is adopted to subtract a detailed and systemic methodology for
LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps.
Big data, crowd sourcing, crowd funding and crowd testing take place in
suitable steps to realize UUI, MCC and PCA throughout the innovation process in
LIL 2.0. It would become a guideline for any company or organization to develop
a project in the form of an LIL 2.0 project.
To prove the feasibility of LIL Goal Net Model, it was applied to two real
cases. One project is a Kinect game and the other one is an Internet product.
They were both transformed to LIL 2.0 successfully, based on LIL goal net based
methodology. The two projects were evaluated by phenomenography, which was a
qualitative research method to study human experiences and their relations in
hope of finding the better way to improve human experiences. Through
phenomenographic study, the positive evaluation results showed that the new
generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf
Research allocation in mobile volunteer computing system: Taxonomy, challenges and future work
The rise of mobile devices and the Internet of Things has generated vast data which require efficient processing methods. Volunteer Computing (VC) is a distributed network that utilises idle resources from diverse devices for task completion. VC offers a cost-effective and scalable solution for computation resources. Mobile Volunteer Computing (MVC) capitalises on the abundance of mobile devices as participants. However, managing a large number of participants in the network presents a challenge in scheduling resources. Various resource allocation algorithms and MVC platforms have been developed, but there is a lack of survey papers summarising these systems and algorithms. This paper aims to bridge the gap by delivering a comprehensive survey of MVC, including related technologies, MVC architecture, and major finding in taxonomy of resource allocation in MVC
Research on user recruitment algorithms based on user trajectory prediction with sparse mobile crowd sensing
Sparse mobile crowd sensing saves perception cost by recruiting a small number of users to perceive data from a small number of sub-regions, and then inferring data from the remaining sub-regions. The data collected by different people on their respective trajectories have different values, and we can select participants who can collect high-value data based on their trajectory predictions. In this paper, we study two aspects of user trajectory prediction and user recruitment. First, we propose an STGCN-GRU user trajectory prediction algorithm, which uses the STGCN algorithm to extract features related to temporal and spatial information from the trajectory map, and then inputs the feature sequences into GRU for trajectory prediction, and this algorithm improves the accuracy of user trajectory prediction. Second, an ADQN (action DQN) user recruitment algorithm is proposed.The ADQN algorithm improves the objective function in DQN on the idea of reinforcement learning. The action with the maximum input value is found from the Q network, and then the output value of the objective function of the corresponding action Q network is found. This reduces the overestimation problem that occurs in Q networks and improves the accuracy of user recruitment. The experimental results show that the evaluation metrics FDE and ADE of the STGCN-GRU algorithm proposed in this paper are better than other representative algorithms. And the experiments on two real datasets verify the effectiveness of the ADQN user selection algorithm, which can effectively improve the accuracy of data inference under budget constraints
- …