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Scaling IoT to meet enterprise needs

Enterprises are increasingly complementing their cloud-based IoT solutions with edge computing to accelerate the pace of data analysis and make better decisions, faster.

Just a few years ago, many expected all the Internet of Things (IoT) to move to the cloud—and much of the consumer-connected IoT indeed lives there—but one of the key basics of designing and building enterprise-scale IoT solutions is to make a balanced use of edge and cloud computing. Most IoT solutions now require a mix of cloud and edge computing. Compared to cloud-only solutions, blended solutions that incorporate edge can alleviate latency, increase scalability, and enhance access to information so that better, faster decisions can be made, and enterprises can become more agile as a result.

That being said, complexity introduced by edge computing should justify the objectives at hand, which include scale, speed, and resiliency. A choice that goes too far in one direction typically introduces substantial operational complexities and expenses. Ultimately, the enterprise should take into consideration a full range of factors that reflect its own particular objectives in designing and building an IoT solution in the first place.

In this article, we discuss when and how enterprises can optimally make use of both the edge and the cloud in their IoT solutions. We explain the roles edge and cloud computing play, why the edge may be needed, and how to approach selecting a solution. We also explain some of the complexities with edge computing and provide some use cases.

The cloud explosion and the latency challenge: Enter edge computing

We have experienced a veritable explosion of cloud adoption in the past decade—the IT functionality of many modern companies exists exclusively, or in large part, in the cloud. Among the many benefits of the cloud infrastructure are cost effectiveness, scale, self-service automation, interoperability with traditional back-office systems, and centralized functionality.

At the same time, the amount of sensor-generated data has grown strongly too, and this trend is expected to continue in the years ahead. Because data can become essentially valueless after it is generated, often within milliseconds, the speed at which organizations can convert data into insight and then into action is generally considered mission critical. Therefore, having the smallest possible latency between data generation and the decision or action can be critical to preserve an organization’s agility. However, as the speed of data transmission is inviolably bounded by the speed of light, it is only by reducing the distance that data must travel that the latency challenge can be mitigated or avoided altogether. In a cloud-only world the data ends up traveling hundreds or even thousands of miles, so where latency is critical to a solution, edge computing can become key.

According to one estimate, as much as 55 percent of IoT data could soon be processed near the source, either on the device or through edge computing. Indeed, scale plays a big role in this likely shift—growing data demands will likely put the focus on latency, and decreased latency could dramatically improve the response time, thereby saving both time and money.

Continuing reading more by Deloitte's Ken Carroll and Mahesh Chandramouli here.

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IoT in Transportation and Logistics

It is said that there are 100,000 freighters on the seas and that 90% of everything you have has come via container ship. The first time I saw Hong Kong Harbour from my swank room at the JW Marriott what struck me most was the number of container ships. As I scanned the waters I counted several dozen of the floating giants and imagined everything onboard was coming out of China and going somewhere on the planet.

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Photo Credit: Andrew Smith via Flickr

Once at its destination port a gantry crane unloads the containers and places them either on a truck or a rail car. Then the goods are sent off to their respective warehouses, where another delivery vehicle most likely takes it to another vendor or supplier where it might eventually end up in your garage. Just thinking about this one aspect of transportation and logistics is mind boggling. And it’s perfect for IoT.

The fine folks at Deloitte University Press have written a wonderful overview on IoT considerations for the shipping and logistics industries. Entitled, “Shipping smarter: IoT opportunities in transport and logistics,” the report highlights that while companies in transport and logistics (T&L) have always been data-driven, with specific applications like real-time tracking of shipments, warehouse-capacity optimization, predictive asset maintenance, route optimization, improved last-mile delivery, and more, they still have a huge opportunity ahead of them in IoT.

The increasing number of connected devices, embedded sensors, and analytics technologies will only increase the data and accelerate. This will lead to more efficient use of transport infrastructure, better engagement with customers, and more informed decision making. The report has four recommendations for T&L and IoT, but what I found most thought provoking was their framework that captures the series and sequence of activities by which organizations create value from information: the Information Value Loop (see below).

You can find the full report here. Further reading on the subject is listed after the graphic.



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Further Reading



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