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Is Waggle the Next Big Thing in Plant Data Management?

Manufacturers would be wise to keep an eye on a Chicago demonstration project of the Waggle sensor data platform and so-called Array of Things project. Supported by a $3.1 million grant from the National Science Foundation, the city of Chicago, in partnership with University of Chicago and the Argonne National Laboratory, will be deploying sensing systems that will work like fitness apps for the urban environment.

Around the city, 500 Array of Things nodes will measure and monitor conditions related to problems like traffic, air quality and flooding. Sensors will collect highly granular and localized data on temperature, barometric pressure, light, vibration, carbon monoxide, nitrogen dioxide, sulfur dioxide, ozone, ambient sound intensity, pedestrian and vehicle traffic and surface temperature— but it won’t collect private data from people. These sensors can even extract data from images collected by cameras.


Using the Waggle platform, the smart sensors can be programmed to not only collect the data, but to respond to it. The sensors will also process data before sending it, to speed everything up. All the data is uploaded as often as twice a minute and ends up in the cloud, where systems and apps will rapidly make sense of it. Because expertise in programming such systems is not abundant, Waggle is built on a framework that is meant to provide researchers with plug-and-play retrieval of secure data.


How will Waggle impact manufacturing?


Waggle’s roots are actually closer to manufacturing than to urban environments. The project began in response to heat and temperature fluctuations in the laboratory’s supercomputer machine rooms. The Waggle team found that simple devices on the machine room’s computer racks could collect and send data on airflow and temperature, and they could develop new approaches to aggregating and utilizing it.


In a manufacturing plant, warehouse, or shipping facility, there are hundreds if not thousands of machines and micro-locations where control would be improved with better data collection and management systems. While there have long been sensors and plant data systems in use in manufacturing, cost, data standardization, incompatible programming languages and systems, and hard-to-find skillsets limit large-scale effectiveness.


Waggle’s not ready for prime time yet, but scientists will soon be able to get Waggle “kits” from Argonne for modification and use for their projects. In this period of research, manufacturers could help guide Waggle’s direction into the technologies they need most. With encouragement and support from industry, manufacturing research centers could use Waggle in working on machine and plant sensor data systems. Schneider Electric, which produces automation and control devices, is already involved in other Internet of Things (IoT) projects and has provided input into the project. For other companies producing manufacturing plant infrastructure and systems it’s a competitive opportunity not to be missed. In addition, as end-users of those systems, manufacturers should be asking their partners about Waggle projects they may be working on. To prepare for the availability of such systems, manufacturers should also be sure that their CTOs and lead engineers are aware of Waggle and are monitoring its development. If it achieves its aims, they should develop skillsets in their technical teams.


Sensors and programming processes have come a long way since ladder logic and the first Programmable Logic Controllers (PLCs) came on the scene. The Waggle platform and Array of Things sensor technology have the potential to make manufacturing data systems as usable as a smart phone’s weather, traffic maps, messaging and fitness apps we use every day.


Karen Wilhelm has worked in the manufacturing industry for 25 years, and blogs at Lean Reflections, which has been named as one of the top ten lean blogs on the web.

    Some opinions expressed in this article may be those of a contributing author and not necessarily Gray.

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