Crowdsensing

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. In short, this means crowdsourcing of sensor data from mobile devices. 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. In short, this means crowdsourcing of sensor data from mobile devices. Devices equipped with various sensors have become ubiquitous. Most smartphones can sense ambient light, noise (through the microphone), location (through the GPS), movement (through the accelerometer), and more. These sensors can collect vast quantities of data that are useful in a variety of ways. For example, GPS and accelerometer data can be used to locate potholes in cities, and microphones can be used with GPS to map noise pollution. The term 'mobile crowdsensing' was coined by Raghu Ganti, Fan Ye, and Hui Lei in 2011. 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). Current crowdsensing applications operate based on the assumption that all users voluntarily submit the sensing data leading to extensive user participation. It can also indicate the way mobile device users form microcrowds based on a specific crowdsensing activity.