The Internet of Thing’s dirty little secret is the cost of deployment. For example, adding a low-cost motion sensor and radio to a traffic light to count passing vehicles before it leaves the factory is easy and inexpensive. The incremental cost of deployment is near zero, especially if low-power wide-area network (LPWAN) coverage or other low-cost communications coverage is available. But retrofitting the traffic light with sensors and radios will cost the municipality a public works truck roll, a crew, an electrician and a police traffic detail. Retrofits are expensive.
Retrofitting the world for IoT is a data science and a sensor engineering task to study the IoT problem and find the simplest way to acquire the data. The question for the data scientist is what minimum data resolution will provide the desired result: How many sensors per unit of area and how many readings per time interval are necessary to solve the problem? The engineer must trade off sensor costs, radio costs and network availability, and power consumption versus available power sources.