Together, Honeywell and Intel have developed a IoT proof of concept (PoC) for the Connected Worker. The Connected Worker can take many forms - factory laborer, mine worker, first responder, firefighters and more. For each environment and worker role, a different selection of sensors may be appropriate to provide the most meaningful IoT-fueled dataset to represent that individual worker asset. As with most IoT solutions, it is critical to avoid being overwhelmed by a steady stream of meaningless data. Rather, it is essential to send select actionable intelligence to the cloud for visualization and customized alert notifications. The downside is that data from individual wearable devices - if viewed independently - can potentially cause false alarms and contribute to inefficiencies in a manpower as a result. Fusing sensor technology with big data processing (hub/gateway), analytics - all in the cloud - is the key to improving local intelligence as well as remote visualization of actionable intelligence.

DownUnder GeoSolutions (DUG) wanted to increase computing performance by 5 to 10 times to improve seismic processing. The solution must build on current architecture software investments without sacrificing existing software and scale computing without scaling IT infrastructure costs.

The vending industry is undergoing a sea change, taking advantage of new technologies to go beyond just delivering snacks to creating a new retail location. Intelligent vending machines can be found in many public locations as well as company facilities, selling different types of goods and services, including even computer accessories, gold bars, tickets, and office supplies. With increasing sophistication, they may also provide time- and location-based data pertaining to sales, inventory, and customer preferences. But at the end of the day, vending machine operators know greater profitability is driven by higher sales and lower operating costs.

Have you ever felt overloaded by too much sensory input? The results can be problematic, even risky if you’re driving at the time. The same holds true for trains, ships, oil rigs, and many other industrial assets. The data processing challenges on these complex machines are growing rapidly as the number of sensors increases; yet so are the opportunities to transform operations by using all the available data effectively. A modern locomotive, for example, has as many as 200 sensors generating more than a billion data points per second.