A massive stand storm has originated and is expected to sweep the nearby regions of about 20 miles radius in just a couple of hours. There are massive vessels, aka container ships, lined up for the unloading and loading of large containers carrying cargo from across the world. When the stand storm reaches the seaport, it may not be possible to operate any of the cranes and devices. The worst part is that the storm may cut off the cloud connectivity for the next couple of days causing further delay in operations. It would be a loss of business for the seaport as well as the ship operators. This happens at least once every quarter and the seaport is looking for a viable solution to overcome such scenarios in the future.

A large hospital in a city with over 8 million people, receives almost 5 accident cases daily resulting in 1 or more deaths. They are setting up an automated digital response system in the emergency response department that can perform quick diagnosis of the victim, run pathological tests, and come up with different lines of treatment for the victim. These emergency response systems have very little time to respond to the victim’s situation and they cannot wait for remote processing or access of data.

A multi-storied building suffered an electrical short-circuit resulting in a massive fire. More than 20 fire engines were pressed into action to contain the fire and reduce the loss of lives and assets. The fire engines are all fitted with smart devices that can quickly scan through the building for the presence of people and pets, and contain the fire in those specific locations first. They also extend the rescue ladders to the floors from where people are present. The mixture, pressure, and quantity of the fire extinguisher material are controlled according to the situation of the fire. There is little time for the fire engines to contain the situation and they need to keep readjusting their tactics every few minutes accordingly.

These are all stories of edge computing-based applications that are already being implemented in different parts of the world in different capacities and modes. The seaport scenario is already in use in many locations, while the hospitals are doing test runs with lab dummies. The fire engines have borrowed the idea from similar pilots in the oil and energy industry. The space research industry is the other key industry very inclined towards edge computing processing data at the source.

Is Edge Computing the same as IoT?

Edge computing is complementary to IoT. IoT devices are the source of data. They gather and collect data from their host machines and applications, and transfer the data back to a cloud server for processing the data. IoT devices do not spend time or resources filtering, cleansing, or processing the data. The best examples of IoT are following:

• Smart devices are used for home automation solutions, such as refrigerators, air-conditioners, geysers, and TV.
• Wearable devices used for measuring health routines.
• Field monitoring solutions in agriculture and other large industries.

On the other hand, edge computing devices can collect and process the data at the source. They can work in disconnected environments for a sustainable period and keep serving their users with processed information and insights. They create new ways to improve the operational efficiency of smart devices, reduce the latency of data processing, and ensure higher availability even in case of network disruptions.

Is Edge Computing right for my project?

When adopting edge computing for your solutions, you should consider the following aspects:

• Content Size: There is a trade-off in handling large content using IoT and Edge devices. If the content size is large, it is better to cleanse the data before it is uploaded to the cloud for further processing. For example, when an automated gate operations system captures multiple images of moving vehicles, it is bound to capture images that are blurred or foggy. Such images need not be uploaded to the cloud for further processing. These can be filtered off at the device itself.
  
• Latency: When the data frequency or data points are high, network latency in transferring the data plays an important role. A slow network can cause a loss of data from the source itself. For example, liquid cargo is unloaded underwater at locations away hundreds of feet away from the seaport. With the help of edge computing, the loading can be controlled and monitored at the source without having to pass through the network frequently.
  
• Data Security: Data transferred over the network always suffers from threats of cyber attacks and privacy compromise. More data is processed locally on the device and less data is transferred over the network, thus reducing the attack spectrum. It is very effective in preventing the DDoS attacks that cloud servers are vulnerable to.
  
• Reliability: The edge devices are capable of working in isolated environments without network connections and provide higher reliability even in case of power or network outages. This is especially useful for industries that work in areas susceptible to natural disasters like cyclones, fires, or earthquakes, such as oil extraction units, underground mining, high-sea weather monitors, and deep forest monitors.
  
• Training data models: The edge devices are extremely useful in building AI/ML applications that need to capture data and quickly learn from the data to build intelligent patterns. It is extremely useful in learning road techniques for the adoption of driver-less cars and flying techniques of different makes and models of commercial and military planes.

With many organizations already making advances in the area of Edge computing, this year a significant number of new industries will be adopting the valuable paradigm of Edge computing in locations like forests, seaports, manned toll gates, and on the highways to allow more people to work from home than ever before.