Mobile crowdsensing: current state and future challenges

By Raghu Kiran Ganti, Fan Ye, Hui Lei. IEEE Communications Magazine, 2011

1,980 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e032fb2394612e3cf3937245e60ece378a71f858

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In Computer Science Applications

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Citations in the corpus

Structural Health Monitoring Using Wireless Sensor Networks: A Comprehensive Survey (2017, background): “Although MPS has high potential and brings many opportunities as mentioned above, MPS comes with several challenges [105], [106].”
A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “They uncover the potential of the crowd, where individuals with sensing and computing devices collectively share data to measure and map phenomena of common interest [7].”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “, for medical purposes [2] and air quality monitoring [3].”
A Crowdsourcing Framework for On-Device Federated Learning (2019, neutral): “, for indoor localization, traffic monitoring, navigation [3], [4], [5], [6]).”
Resource Management in Fog/Edge Computing (2018, background): “ause the number of edges is not sufﬁcient, and some highly populated areas may result in congestion on edges. To address these limitations, HEC combines mobile edge computing with mobile crowdsensing [146], where smartphones or tablet computers become edge nodes, share sensor data, and analyze the d”
An Amateur Drone Surveillance System Based on the Cognitive Internet of Things (2017, background): “A large crowd of people can contribute surveillance data in the way, for example, mobile crowd sensing [11] where individuals with sensing and computing devices (such as smartphones, in-vehicle sensors) collectively share data and extract information to measure and map phenomena on the presence of a”
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, background): “According to Ganti et al. [2011], traditional crowd-powered sensing spans a wide spectrum regarding the degree of human involvement, where participatory sensing and opportunistic sensing are placing at the two ends.”
Internet of Things and Big Data Analytics for Smart and Connected Communities (2016, background): “, individuals share their exercise data and compare their exercise levels with the rest of the community to improve their daily exercise routines [13].”
How to crowdsource tasks truthfully without sacrificing utility: Online incentive mechanisms with budget constraint (2014, background): “For more details, we refer interested readers to several survey papers [1], [8], [9].”
Incentives for Mobile Crowd Sensing: A Survey (2016, background): “Mobile crowd sensing [3] requires large amounts of participants (e.”
Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art (2019, background): “Accordingly, the big data produced by the integration of massive global-view remote sensing data and local, live, location-based social media data can offer new opportunities for smart-city and smart-environment applications, with the ground reference collection through social sensing [197] and crow”
From participatory sensing to Mobile Crowd Sensing (2014, background): “[10], crowd-powered sensing can span a wide spectrum in terms of user involvement, with participatory and opportunistic sensing at the two ends.”
A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches (2015, background): “According to recent studies in [32] and [33], mobile crowd-sensing systems have been recently used to”
The Internet of Things vision: Key features, applications and open issues (2014, background): “In addition, information derived from the user’s habits could measure the impact of the individual behavior on the urban environment and increase the user’s awareness of each single action on the environment sustainability [127].”