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240 results on '"Ye-Qiong Song"'

1. IEEE Access Special Section Editorial: Toward Smart Cities With IoT Based on Crowdsensing

Author

Kun Wang, Zhibo Wang, Ye-Qiong Song, Dejun Yang, Shibo He, and Wei Wang

Subjects
Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract

The proliferation of the Internet of Things (IoT) has paved the way for the future of smart cities. The large volume of data over the IoT can enable decision-making for various applications such as smart transportation, smart parking, and smart lighting. The key to the success of smart cities is data collection and aggregation over the IoT. Recently, crowdsensing has become a new data collection paradigm over the IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with microphones on smartphones. Despite the advantages of crowdsensing and the IoT, there are many challenges to utilize crowdsensing over the IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users.

Published
2021
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2. Impact of Link Unreliability and Asymmetry on the Quality of Connectivity in Large-scale Sensor Networks

Author

Youxian Sun, Zhi Wang, René Schott, Ye-Qiong Song, and Yanjun Li

Subjects
sensor networks, connectivity, link model, node isolation probability, giant component, critical node density, Chemical technology, TP1-1185
Abstract

Connectivity is a fundamental issue in research on wireless sensor networks. However, unreliable and asymmetric links have a great impact on the global quality of connectivity (QoC). By assuming the deployment of nodes a homogeneous Poisson point process and eliminating the border effect, this paper derives an explicit expression of node non-isolation probability as the upper bound of one-connectivity, based on an analytical link model which incorporates important parameters such as path loss exponent, shadowing variance of channel, modulation, encoding method etc. The derivation has built a bridge over the local link property and the global network connectivity, which makes it clear to see how various parameter impact the QoC. Numerical results obtained further confirm the analysis and can be used as reference for practical design and simulation of wireless ad hoc and sensor networks. Besides, we find giant component size a good relaxed measure of connectivity in some applications that do not require full connectivity.

Published
2008
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48. IEEE Access Special Section Editorial: Toward Smart Cities With IoT Based on Crowdsensing

Author

Dejun Yang, Zhibo Wang, Kun Wang, Ye-Qiong Song, Wei Wang, Shibo He, University of California (UC), Zhejiang University, SIMulating and Building IOT (SIMBIOT), Department of Networks, Systems and Services (LORIA - NSS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), Colorado School of Mines, State Key Laboratory of Industrial Control Technology, and Amazon Inc.

Subjects
Data collection, General Computer Science, business.industry, Computer science, Reliability (computer networking), General Engineering, Crowdsourcing, Computer security, computer.software_genre, TK1-9971, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], Crowdsensing, Leverage (negotiation), Key (cryptography), General Materials Science, Electrical engineering. Electronics. Nuclear engineering, Smart lighting, business, Internet of Things, computer
Abstract

International audience; The proliferation of the Internet of Things (IoT) has paved the way for the future of smart cities. The large volume of data over the IoT can enable decision-making for various applications such as smart transportation, smart parking, and smart lighting. The key to the success of smart cities is data collection and aggregation over the IoT. Recently, crowdsensing has become a new data collection paradigm over the IoT, which can realize large-scale and fine-grained data collection with low cost for various applications. For example, we can leverage the power of the crowd to build a real-time noise map with microphones on smartphones. Despite the advantages of crowdsensing and the IoT, there are many challenges to utilize crowdsensing over the IoT for smart cities, such as how to allocate tasks to appropriate users to provide high-quality sensing data, how to incentivize users to participate in crowdsourcing, how to detect the reliability of the crowdsourced data, and how to protect the privacy of users.

Published
2021
Full Text
View/download PDF

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