The applications of the so-called Precision Agriculture can take on different aspects, which lead us to talking about fog computing. The assumption is that in common there is a stream of information ranging from the field, typically from sensors placed in the ground or other elements that collect environmental data, to some cloud system. This analyzes them in detail, compares them with information collected elsewhere and indicates any actions to be taken to optimize crops.

Even in other applications of the IoT paradigm this ideal model shows a critical point: cloud dependence. It is the cloud that collects and stores information, is the cloud that analyzes them and contextualizes them to give directions. On the other hand, it is not (yet) possible to turn a farm into a small data processing center, and when you imagined the first smart-growing applications it was difficult to think of something other than what we have described.

But technology evolves, and even the peripheral elements of an IoT network have become quite powerful enough to be able to take part in the data processing that was initially thought to pass completely into the cloud. In this way, the concept of cloud computing is conceptually established, which complements cloud computing: there is no central cloud for information management, but a mist of simpler components that, together, can perform various functions Coming to a more agile architecture.

The contrast between the two terms also emphasizes the different proximity of processing to the monitored system: a cloud is high, far away, the fog at the bottom is a nearby cloud that touches the ground.

Better explain with an example. Lets imagine that a farmer distributes a network of sensors in a field to evaluate the presence in the soil of various chemical compounds.

Today, these sensors can have enough memory and intelligence on board to not only detect physical parameters and send them upstream to the cloud. They can, for example, independently compare the data they collect with a standard distribution pattern of chemical compounds in the soil of the area they are located.

In this way sensors can have a decision-making role directly on the site: just record the data when everything is in the norm but send an alert immediately when the data does not match the expected model, perhaps even deriving why the model they were taught.

The conceptual significance of fog computing lies in the fact that it introduces a decentralized, multi-layered, and ideally open, multi-layered modeling model that in the initial design of smart agriculture applications and IoTs generally lacked.

Or rather, to be more precise: it was contemplated for the part of managing and transmitting information but only marginally for their processing. For example, we can think of several smart sensors distributed over a certain geographic area and communicating using the most relevant networks (cellular, Wi-Fi, Sigfox ...), sending data and processing to more intelligent intermediate nodes to the platform Real cloud.

Each of these levels can make elaborations and draw conclusions so that a reaction (even partial but fast) is close to where it is most needed.

Fog and cloud computing are not in opposition but act in synergy, pointing each to specific strength elements. Clearly, fog computing has the advantage of quick reaction but also has the limit of a reduced field of view: peripheral devices can only intervene in the information they have collected.

Instead, the cloud has longer times but the advantage of being able to process information that comes from a huge number of sensors and even from completely different sources. Historical statistics, weather information, other growers data, sophisticated analysis and forecasting algorithms are all elements that can only be found in clouds.

A challenging scenario on which however there is still a lot to do. The Fog Computing concept is not yet clear to potential users, which often consider it a synonym of hedge computing (the main difference lies in multi-layer vision and dynamic resource management of the former compared to the staticity of the second).

Then there is the problem of interoperability, essential when it comes to clouding cloud-focused services along a continuum of various devices.

Companies focusing on cloud computing are working on reference architectures that face this aspect, but there are still many technology jigs that need to be combined with each other.