Opportunities in mobile crowd sensing

By Huadong Ma, Dong Zhao, Peiyan Yuan. 2014

467 citations recorded by the source corpus.

Subfields: Computer Science Applications, Electrical and Electronic Engineering, Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Bin Guo
2. Xingshe Zhou
3. Zhiwen Yu

In Electrical and Electronic Engineering

1. Billy Pik Lik Lau
2. U-Xuan Tan
3. Yuren Zhou
4. Meng Zhang
5. Naveed Ul L. Hassan
6. Marakkalage Sumudu Hasala
7. Chau Yuen
8. Pascal Bouvry

In Transportation

1. Chau Yuen
2. Billy Pik Lik Lau
3. Bin Guo
4. Zhu Wang
5. Zhiwen Yu
6. U-Xuan Tan

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “As shown in [225], crowdsensing is one of the most cost-efficient method as personal mobile devices such as smart-phones.”
• Mobile Crowd Sensing and Computing (2015, background): “…many mobile sensing devices cannot be guaranteed to be connected all the time due to poor network coverage (e.g., poor signal strength due to interference or no signal in a rural area), energy constraint (e.g., low battery), or user preference (e.g., phone shut off in a meeting) [Ma et al. 2014].”
• A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, methodology): “procedure to meet a pre-defined objective function [37].”
• Federated Learning in Smart City Sensing: Challenges and Opportunities (2020, unknown): “…gravity sensors, acceleration sensors, and so on) and the ubiquitous sensing network make ordinary users able to participate in sensing activities that helps collect the surrounding environmental conditions provides hardware infrastructure support for ubiquitous depth sensing and computing [46].”