Mobile Phone Sensing Systems: A Survey

By Wazir Zada Khan, Yang Xiang, M. Aalsalem, Quratulain Arshad. IEEE Communications Surveys and Tutorials, 2013

589 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/e07620cf680612dbe8d8fa5f0b78df3ae2c12706

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In Computer Science Applications

In Computer Vision and Pattern Recognition

In Transportation

Citations in the corpus

Systematic review of smartphone-based passive sensing for health and wellbeing (2017, background): “This review was unique in focusing on mobile phone systems, because of the advantages described above, but consequently did not incorporate the broader literature on passive sensing using wearable devices such as activity trackers [75] or data collection from social networks [17,18].”
A Survey of Online Activity Recognition Using Mobile Phones (2015, background): “It is being applied for many applications, including health monitoring, monitoring road and trafﬁc conditions, commerce, environmental monitoring and recognizing human behavior [27].”
A Survey on Wireless Body Area Networks: Technologies and Design Challenges (2014, background): “Surveys on mobile phone sensing and human activity recognition using wearable sensors are given in [17] and [18] respectively.”
Incentives for Mobile Crowd Sensing: A Survey (2016, background): “We refer interested readers to a thorough survey of such mobile sensing systems in [2].”
Internet of Things: Architectures, Protocols, and Applications (2017, background): “With the increasing popularity of smartphones among people, researchers are showing interest in building smart IoT solutions using smartphones because of the embedded sensors [16, 26].”
$\textsf{LoPub}$ : High-Dimensional Crowdsourced Data Publication With Local Differential Privacy (2016, background): “With the development of various integrated sensors and crowd sensing systems [19], crowdsourced information from all aspects can be collected and analyzed to better produce rich knowledge about the group, which can benefit everyone in the crowdsourced system [20].”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “under the common umbrella of MCS [7], [9], [36], [37].”
Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors (2016, background): “Activity recognition using body-worn motion sensors in general [19] and especially using smartphone sensors has been studied in recent years [1,2], and it is still being studied extensively [1,2,19–21].”