Crowdsensing: mobile data and predictive algorithms

In Pakistan, mobile data has helped the authorities predict where an epidemic will break out:

Researchers working for the Pakistani government developed an early epidemic detection system for their region that looked for telltale signs of a serious outbreak in data gathered by government employees searching for dengue larvae and confirmed cases reported from hospitals. If the system’s algorithms spotted an impending outbreak, government employees would then go to the region to clear mosquito breeding grounds and kill larvae. “Getting early epidemic predictions this year helped us to identify outbreaks early,” says Umar Saif, a computer scientist at the Lahore University of Management Sciences, and a recipient of MIT Technology Review’s Innovators Under 35 award in 2011.

When we think about “mobility” and its potential in business and society, we shouldn’t limit ourselves to the desktop and app paradigm.

Some big, disruptive innovations of the next ten years will be about marrying powerful sensors being carried by crowds (our colleagues, our customers) and making sense of what the data can tell us.

The description for this trend, or avenue of potential, would naturally be “crowdsensing” (although I suppose in this year’s trend-speak the headline should scream MOBILE+BIG DATA). There’s a research paper from an IBM team a couple of years ago that appears to have coined the term “crowdsensing”. The abstract is as follows:

An emerging category of devices at the edge of the Internet are consumer centric mobile sensing and computing devices, such as smartphones, music players, and in-vehicle sensing devices. These devices will fuel the evolution of the Internet of Things as they feed sensor data to the Internet at a societal scale. In this paper, we will examine a category of applications that we term mobile crowdsensing, where individuals with sensing and computing devices collectively share information to measure and map phenomena of common interest. We will present a brief overview of existing mobile crowdsensing applications and illustrate various research challenges.


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