This chapter was an introduction to Decision Science and IoT. You learned about the basics of IoT and how it evolved and understood the differences between ambiguous names such as M2M, IIoT, IoE, and others. We studied the logical architecture of the IoT ecosystem by considering IoE and learned how People, Processes, Data, and Things together form the IoT ecosystem. We also discussed decision science and understood more about defining a problem based on the current life stage as Muddy, Fuzzy, or Clear, based on its type as Impactful and Frequent, and finally based on its nature as Descriptive, Inquisitive, Predictive, or Prescriptive. We also studied that problem solving in decision science requires an interdisciplinary approach using a combination of math, business, technology, and so on. Finally, we also studied about the problem solving framework using a generic example of a hydroelectric power plant.

In the next chapter, you will learn in depth about the IoT problem universe and use a concrete example to understand the problem and design the blueprint for the problem using the problem solving framework.