Crowdsourcing and the Internet of Things

Machine collaboration in the new era of computing.

As the Internet of Things (IoT) and Machine-to-Machine (M2M) communications connect millions of diverse machines over networks, the goal is to make the combination of those machines greater than the sum of each type and to provide people with greater information and insight as the ecosystem expands.

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Once machines become connected, they can directly collaborate through data to provide a crowd-sourced view of scenarios.

To achieve this level of interconnectivity, businesses that depend on those machines need them to work reliably, securely, and cost-effectively – without human intervention. That’s where an unexpected technology function comes in to help: crowdsourcing.

Crowdsourcing typically conjures up images of people-driven programs, like traffic information gathered from thousands of commuters or weather reports created by people supplying pictures and information from their mobile devices.

However, once machines and sensors become connected, they can directly collaborate through data to provide a more comprehensive crowd-sourced view of a vast number of scenarios.

Certainly, one can imagine scenarios where traction-control sensors from the cars of one business can be mashed up with temperature data from the sensors of another business to crowd-source the detection of black ice which can then alert the truck drivers of yet another business.

The implementation of those scenarios might still be a bit in the future, but already today we are using crowdsourcing across machine types to detect and diagnose issues with the machines and the networks which connect them.

A Powerful Solution

For example, you might have one machine in a particular area that’s having trouble with end-to-end connectivity. If there are other machines around it, Big Data analytics can determine on-the-fly if the issue is specific to that machine, or if there’s a bigger, regional problem.

That’s incredibly powerful. If you look at the machines organically, they can interact like people, such as car drivers or restaurant diners that may be different but share some things in common and connect in a common way. This insight can really save our customers valuable time.

First, you can identify if there is a problem in an automated way. And once you recognize there is a problem you can have big data analytics tell you the likely source of the problem. So rather than taking hours or days to figure it out, you can resolve it in minutes or seconds.

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Rather than taking hours or days to figure a problem out, you can resolve it in minutes or seconds.

That’s a huge difference. Ultimately the result is better quality of service and lower costs for our customers, and when it comes to emergency services, it could actually help save lives.

We at Aeris have already begun deploying crowdsourcing techniques based on Big Data analytics for networked machine monitoring. We’ll soon extend it to our new online, self-service machine-to-machine cellular connectivity marketplace, called Neo.

It’s important to be mindful of the value of different machines and the potential opportunities of interconnecting those machines. Through connectivity and Big Data analytics the value gained from the collaboration will only add to the growing amount of insight made possible in the new era of computing. goldbrown2

This article first appeared on November 21, 2014 on IBM Smarter Planet and was republished with permission.

Drew Johnson is VP of engineering at Aeris Communications

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