From participatory sensing to Mobile Crowd Sensing

By Bin Guo, Zhiwen Yu, Xingshe Zhou, Daqing Zhang. 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS), 2014

402 citations recorded by the source corpus.

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.

Subfields: Computer Science Applications, Ocean Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/1143439d5267227639092104fd1d7da9e27a870a

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. Dusist Niyato
3. Xingshe Zhou
4. Zhiwen Yu

In Ocean Engineering

1. Luca Foschini

In Transportation

1. Bin Guo
2. Dusist Niyato
3. Zhu Wang
4. Zhiwen Yu

Citations in the corpus

• A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, methodology): “define Mobile Crowdsensing in [10] by specifying the required features for a system to be defined as a MCS system.”
• Data Collection and Wireless Communication in Internet of Things (IoT) Using Economic Analysis and Pricing Models: A Survey (2016, background): “Similar to the participatory sensing, a crowdsensing network stimulates ordinary citizens to contribute data which is sensed or generated from their mobile devices [109].”
• Opportunities in mobile crowd sensing (2014, background): “At present, little work focuses on the sensing opportunities provided by human mobility, which have important impacts on the sensing quality of many MCS applications (e.g., urban environment monitoring applications).”
• Mobile Crowd Sensing and Computing (2015, methodology): “A typical example is the Social Contact Manager, which is a system that leverages a combination of the data collected from the web and smartphones to enable intelligent social contact information management and associative search [Guo et al. 2014].”