The IoT generates a vast amount of big data, and this, in turn, puts a huge strain on Internet Infrastructure. As a result, this forces companies to find solutions to minimise the pressure and solve their problem of transferring large amounts of data. Cloud computing has entered the mainstream of information technology, providing scalability in the delivery of enterprise applications and Software as a Service (SaaS). Companies are now migrating their information operations to the cloud. Many cloud providers can allow for your data to be either transferred via your traditional internet connection or a dedicated direct link. The benefit of a direct link into the cloud will ensure that your data is uncontended and that the traffic is not crossing the internet and the Quality of Service can be controlled. As the IoT proliferates, businesses face a growing need to analyse data from sources at the edge of a network, whether they are mobile phones, gateways or IoT sensors. Cloud computing has a disadvantage here: It can’t process data quickly enough for modern business applications.

Cloud computing and the IoT both serve to increase efficiency in everyday tasks, and both have a complementary relationship. The IoT generates massive amounts of data, and cloud computing provides a pathway for this data to travel. Many cloud providers charge on a pay per use model, which means that you only pay for the computer resources that you use and not more. Economies of scale is another way in which cloud providers can benefit smaller IoT start-ups and reduce overall costs to IoT companies. Another benefit of cloud computing for the IoT is that cloud Computing enables better collaboration, which is essential for developers today. By allowing developers to store and access data remotely, developers can access data immediately and work on projects without delay. Finally, by storing data in the cloud, this enables IoT companies to change direction quickly and allocate resources in different areas. Big data has emerged in the past couple of years, and with such emergence, the cloud has become the architecture of choice. Most companies find it feasible to access the massive quantities of IoT big data via the cloud.

The IoT owes its explosive growth to the connection of physical things and operational technologies to analytics and machine learning applications, which can help glean insights from device-generated data and enable devices to make “smart” decisions without human intervention. Currently, such resources are mostly being provided by cloud service providers, where the computation and storage capacity exists. However, despite its power, the cloud model is not applicable to environments where operations are time-critical, or internet connectivity is poor. It is especially true in scenarios such as telemedicine and patient care, where milliseconds can have fatal consequences. The same can be said about vehicle-to-vehicle communications, where the prevention of collisions and accidents can’t afford the latency caused by the round-trip to the cloud server.

Moreover, having every device connected to the cloud and sending raw data over the internet can have privacy, security and legal implications, especially when dealing with sensitive data that is subject to separate regulations in different countries. IoT nodes are closer to the action, but for the moment, they do not have the computing and storage resources to perform analytics and machine learning tasks. Cloud servers, on the other hand, have the horsepower, but are too far away to process data and respond in time.

The fog/edge layer is the perfect junction where there are enough compute, storage and networking resources to mimic cloud capabilities at the edge and support the local ingestion of data and the quick turnaround of results. Main benefits of fog/edge computing are the following:

• increased network capacity: fog computing uses much less bandwidth, which means it doesn’t cause bottlenecks and other similar occupancies. Less data movement on the network frees up network capacity, which then can be used for other things.
• real-time operation: fog computing has much higher experience than any different cloud computing architecture we know today. Since all data analysis is being done at the spot, it represents a true real-time concept, which means it is a perfect match for the needs of IoT concepts.
• data security: collected data is more secure when it doesn’t travel. It also makes information storing much more straightforward because it stays in its country of origin. Sending data abroad might violate specific laws.

Figure 1 pictorially depicts the cloud edge fog computing scenario. The current trend shows that fog computing will continue to grow in usage and importance as the IoT expands and conquers new grounds. With inexpensive, low-power processing and storage becoming more available, we can expect computation to move even closer to the edge and become ingrained in the same devices that are generating the data, creating even greater possibilities for inter-device intelligence and interactions. Sensors that only log data might one day become a thing of the past.

Fog/edge computing has the potential to revolutionise the IoT in the next several years. It seems evident that while cloud is a perfect match for the IoT, we have other scenarios and IoT technologies that demand low-latency ingestion and immediate processing of data where fog computing is the answer. Fog/edge computing improves efficiency and reduces the amount of data that needs to be sent to the cloud for processing. But it’s here to complement the cloud, not replace it. The cloud will continue to have a pertinent role in the IoT cycle. In fact, with fog computing shouldering the burden of short-term analytics at the edge, cloud resources will be freed to take on the more cumbersome tasks, especially where the analysis of historical data and large datasets is concerned. Insights obtained by the cloud can help update and tweak policies and functionality at the fog layer. To sum up, it is the combination of fog and cloud computing that will accelerate the adoption of the IoT, especially for the enterprise.