The obvious way to measure the state of a system is to identify the state variables, find points at which they are exposed so sensors can measure them, and put sensors there. Then pull the sensor data together at a hub. But the obvious way is not necessarily the best way. All those sensors and links make such an approach expensive to install and inherently unreliable.
Another way is to pick a few critical variables that can be sensed remotely and then used to estimate the state of the entire system. This process may be intuitively obvious, or it may involve some serious mathematics and use of a state estimator such as a Kalman filter. One example on the more intuitive side involves security cameras, traffic, parking, and the idea of the smart city.
A typical smart-city scenario might involve lighting management, parking management, traffic control, and security. A traditional IoT approach would put a light sensor on each street lamp, buried proximity sensors in traffic lanes near each intersection and each parking space, and security cameras at strategic locations well above ground level. Each of these sensors would have a wired connection to a local hub, which in turn would have a wireless link to an Internet access point—except for the light sensors, which would use wireless links from the tops of the lamp posts to their hubs.