An in-depth look at IoT’s reliance on the latest technologies like Edge computing & Fog computing and how they are revolutionizing data processing.

It is safe to say that data collection is becoming the centre of technology in the present and the future, and as a result, there is a certain explosion of data. In fact, a study shows that there will be over 75 million active IoT devices by 2025. With such a massive influx of data from each device, it is almost impossible to rely on centralized cloud platforms to store them, which is where Edge computing and Fog computing come in to place. They are the most plausible solutions available currently and widely used by almost all data collection sources. RootQuotient understands the importance of both as we work on products that involve large amounts of data collection and we specialize in both Edge and Fog computing.

What are these and how do they work? What is the primary difference between them? Is it possible to prefer one over the other?

Focusing on Network Latency

First of all, they share similar objectives in terms of their attempt to collect, analyze and process data from the assets more effectively than the traditional cloud computing methods. Cloud computing has always faced the issue of taking a long time and involving an elaborate process to analyze data. Edge and Fog came as an extension of the cloud to resolve these issues. Some of the objectives include:

  • To reduce the amount of data sent to the cloud
  • To reduce the risk of network and internet latency
  • To increase the system response time in applications

This clearly indicates the primary reason why Edge and Fog computing are better than cloud computing.

But where do they differ? – The basic difference between Edge and Fog computing is the exact place where computing power is placed.

Edge computing

Edge, as the name suggests, brings processing close or at the “edge” of a data source, and it does not require a centralized system like a cloud to store the data. This eliminates the time and distance required to send it to the cloud, thereby making the process faster and easier for data transport. It pushes the data directly into the devices.

A classic example of edge computing is Predictive Maintenance. It enables IoT sensors to scrutinize and react to machine health in real-time. The data is captured by the edge computing technology and then sent to centralized cloud computing for further analysis. Read more on Industrial IoT(IIoT).

Fog computing

Several users utilize fog as a jumping-off point to use Edge computing. Fog assists in computing, storage and networking services between the end sources and cloud centres. It also indicates the extension of computing to the edge of the network. Fog computing accesses the local computer resources rather than remote resources, resulting in decreasing network latency.

For instance, if a device constantly updates the machine temperature, Fog makes sure only the necessary data like change in temperature reaches the cloud system, filtering the unnecessary data that may not be used for further analysis or studies.

Multiple-step Process vs Simplified Process

Another major difference between Edge and Fog is that Fog takes multiple steps to complete its process while Edge computing simplifies the communication chain and reduces potential failure points. However, it has been understood that Fog computing reduces up to 98% of the number of packets transmitted with 97% , hence tamping down the bandwidth usage by a great margin.

Succeeding with Edge and Fog Computing

With Edge and Fog computing carrying many advantages over the cloud, the patterns of success while using these two are as follows:

  • Any device with an IoT process can be targeted and the small amounts of data collected and processed through Edge. The data collected will manage up to 90% of the IoT based process and can help in identifying any noticeable changes in machine health.
  • Any device requiring large data collection can be dealt with using Fog. An IoT device that sends continuous data to be collected, and analyzed can be processed with Fog as the “mediator” to filter the necessary information to be forwarded to the cloud but also take in all the data that is sent.
  • Utilize more involved processing at a central saver, such as machine learning systems and deep data analysis. This will assist in identifying where the processing requires more data storage and implement tactical processing that does not require much horsepower at the edge.
  • Security has never been an issue with Edge computing. It maintains the data discreetly and is found to be better than the cloud at safety and security as the data isn’t transferred. When there is relatively smaller data involved, it is better to use Edge over Cloud or Fog.
  • When there are multiple devices involved, since Fog uses LAN, it acts as the best bet to process and analyze data. This understanding of when and where to place Edge and Fog results in the most effective use of IoT technology and long-standing error-free processes.

The above-mentioned ideas have been arrived at with thorough experience by RootQuotient while working on developing various applications over the years. We have understood the importance of differentiating Edge and Fog precisely and implementing them exactly where they are required for the best outcomes.

The primary challenge with Fog computing is security as it collects large amounts of data from multiple sources. With any technology, updates and problem solving become a part of the future. With the involvement of secure platforms like cloud and edge, it is only a matter of time that the security issue is sorted.

Network latency stood in the way of IoT achieving its full evolution and maturity. Placing Edge and Fog platforms, expanding the capacity of IoT systems and leveraging their performance, leads to more use cases for IoT in general.

Microsoft’s Satya Nadella has spoken of how Edge computing is the future when utilized in tandem with the cloud. All industries, big and small, are shifting to IoT to improve production efficiency and this transformation is not complete without Edge and Fog computing. They extend beyond IoT and are set to revolutionize technology in general. A couple of years ago, Edge and Fog were considered the future of cloud-based systems and we are already there. It is not long before we see them take over technology completely.