Crowdsensing, sometimes referred to as mobile crowdsensing, is a technique where a large group of individuals having mobile devices capable of sensing and computing (such as smartphones, tablet computers, wearables) collectively share data and extract information to measure, map, analyze, estimate or infer (predict) any processes of common interest.
[1] Mobile crowdsensing belongs to three main types: environmental (such as monitoring pollution), infrastructure (such as locating potholes), and social (such as tracking exercise data within a community).
[3] Based on the type of involvement from the users, mobile crowdsensing can be classified into two types: Taking advantage of the ubiquitous presence of powerful mobile computing devices (especially smartphones) in recent years, it has become an appealing method to businesses that wish to collect data without making large-scale investments.
Numerous technology companies use this technique to offer services based on the big data collected, some of the most notable examples being Facebook, Google, and Uber.
[3] For instance, videos monitoring an activity (e.g. traffic) may be stored on a user's device for a specific period and are then transmitted to a person or institution capable of taking action.
[8] The data collected through mobile crowdsensing can be sensitive to individuals, revealing personal information such as home and work locations and the routes used when commuting between the two.