A survey of mobile phone sensing

By N. Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, Andrew T. Campbell. IEEE Communications Magazine, 2010

2,034 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/3bddd8fdff241b3380cc2cf065e2097619a9fe9b

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In Computer Science Applications

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Citations in the corpus

Internet of Things: Architectures, Protocols, and Applications (2017, background): “The smartphone itself has many types of sensors embedded in it [16] such as the location sensor (GPS), movement”
The rise of low-cost sensing for managing air pollution in cities. (2015, background): “(Lane et al. 2010; Mead et al. 2013), but more sophisticated corrections are required for”
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels (2017, background): “Examples of noisy datasets are medical (Raviv & Intrator, 1996), human-labeled (Paolacci et al., 2010), and sensor (Lane et al., 2010) data.”
Mobile crowdsensing: current state and future challenges (2011, positive): “…measured accurately only when many individuals provide speed and air quality information from their daily commutes, which are then aggregated spatio-temporally to determine congestion and pol-Device Community sensing is also popularly called participatory sensing [1] or opportunistic sensing [2].”
DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing (2016, neutral): “ious signal estimation and classification applications [23].”
The Smartphone Psychology Manifesto (2012, neutral): “Smartphones manufacturers do offer substantial support for app developers in the form of software development kits (“devkits”; Lane et al., 2010), but those app developers are typically expert in Java, Objective-C, C#, or other common app programming languages.”
Incentives for Mobile Crowd Sensing: A Survey (2016, background): “have enabled and stimulated the development of mobile sensing technologies [1]–[5], among which mobile crowd sens-”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, background): “The ubiquity of cell phones, along with rapid advancement in mobile technology, has made them increasingly effective sensors of our daily whereabouts (Lane et al., 2010).”
Behavioral Intervention Technologies: Evidence review and recommendations for future research (2010, positive): “work around patients' reluctance to log information [46].”
Heterogeneity in Mobile Cloud Computing: Taxonomy and Open Challenges (2014, positive): “• Context-Processing: Contemporary mobile devices are capable of gathering extensive context and social information, specifically available cloud resources, network bandwidth, weather conditions, and users’ voice and gestures from their surrounding environment [51], [52].”
Context Aware Computing for The Internet of Things: A Survey (2013, positive): “This technique is widely used in mobile phone sensing [150] and”
A Survey on Internet of Things: Architecture, Enabling Technologies, Security and Privacy, and Applications (2017, background): “Based on different data processing steps, privacy-preserving mechanisms can be divided into three categories: 1) privacy preservation in data collection [65]; 2) privacy preservation in data aggregation; and 3) privacy preservation in data mining and analytics [20], [134].”
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”