7 Edge Computing Uses You Should Know
Thanks to the Internet of Things, there has been a significant rise in the volume of data that is being processed. This has further resulted in the need to deploy edge computing devices.
In simple terms, edge computing is the processing of data closer to the source where the data is stored or processed, particularly where there is an IoT device. Edge computing can easily be processed using sensors, routers, or gateways that are connected to the devices to communicate data. Alternately, these devices are connected through a set of small servers that are installed on-premise in a small cluster.
Edge Computing Examples
This section will go through various edge computing and edge device examples.
To ensure that the vehicles function seamlessly, these autonomous vehicles need to collect and process their data around multiple parameters such as direction, speed, location, traffic congestion, and many more. What’s more critical is that these parameters need to be processed in real-time. This requires every vehicle to operate as an edge device with strong computing capability. Edge computing devices can easily capture the data through the sensors on board these vehicles’ combined onboard cameras and transmit the data in milliseconds within the inbuilt edge computing device to process it super fast.
Edge computing can easily be applied in areas such as lane-departure warnings and even in the case of self-parking applications. The more we see an increase in the ability of the vehicle to interact with the environment, the more will be a need for a faster and more responsive network.
Another use case within this segment is electric vehicles. Electric vehicles can be a computing example where the technology can be used for predictive maintenance. Even in charging stations, edge computing can be used to capture data with different parameters to help them gain valuable insights into the functioning of charging stations.
Traffic Management Systems
Another area of an edge computing example is traffic management systems. If we look at the most sophisticated application, the New York City traffic management system is where we see the use case of edge computing devices. It can perform functions such as adjusting the timing of traffic signals and opening and closing extra traffic lanes on an ad-hoc basis. Here if we notice, through edge computing, traffic is managed intelligently and on a real-time basis to avoid any sort of congestion.
Public Transit Systems
In the case of public transit systems, edge computing devices can be installed in public transport vehicles such as buses and passenger rail systems. Through these devices, data processing happens only for vehicles in transit. For instance, when a vehicle is in transit, it can send out different types of data on the speed, safety updates from the driver, vehicle condition while in transit, and so on.
Clean Energy and Sustainable Technology
Today everyone is talking about clean energy and sustainable technology. This is a growing segment. Every city and smart grid system within can adopt edge computing devices to monitor critical elements around buildings, such as efficiency in heating, lighting, and clean energy.
Smart lighting systems use edge computing to control the optimized use of lights in cities by controlling consumption and ensuring public safety.
The health care sector constantly collects patient data and patient records from various devices such as sensors, monitors, wearables, and other medical devices. This helps health care professionals to gain timely information on patient conditions.
Another example of an edge computing device is in the area of security, particularly worker safety. Data from devices such as onsite cameras, safety devices, and sensors. This ensures that there is no unauthorized access to the site and monitors the safety policies followed by employees.
Agriculture and Farming
Agriculture and farming is also an area where we can see a significant use case of edge computing. Using the concept of precision agriculture technology, farmers can leverage edge computing here and monitor various parameters such as the soil’s temperature, humidity, and level of nutrients in the soil. This can further yield in taking decisions on the watering quantity and level of fertilizers to be applied for farming.
Edge computing is not just limited to farming land but also can be further extended to greenhouses, where sensors are installed to capture various data inputs.
What Is Driving the Growth of Edge Computing?
There is a multitude of reasons for edge computing gaining popularity. While the primary reason is the large number of edge computing devices being connected, there are three other factors that are driving the growth of edge computing.
- Reliability: Having edge computing reduces the situation of network congestion, which usually arises, causing interruptions in used cases such as point-of-sale-systems
- Bandwidth: The cost of relaying large quantum of data and the limitation of physical bandwidth make edge computing far more viable
- Latency: Certain kinds of applications cannot operate with even near-zero latency. In such a situation, it is impossible to fetch the data from a cloud server. The data needs to be stored close to the device, and edge computing can deliver this capability.
Most of the data gathered by IoT devices are pulse data, also called standard ‘heartbeat’ data. This kind of data does not require to be stored in a cloud data center. Having stored this filtered-out data at the network edge closer to the proximity of the device, edge computing can easily bring down the volume of data that needs to be sent to the server. This causes a tremendous reduction in latency and saves bandwidth.
It has been observed that the number of used cases of edge computing devices has slowly been rising. This is primarily due to the large number of smart devices that are able to perform different types of data processing on the edge. Furthermore, machine learning and artificial intelligence capabilities further enhance edge computing devices’ capability.
Edge Computing Practices to Follow
From a best practices point of view, here are four practices associated with edge computing devices and edge computing technology.
- Management: There should be a clear awareness of what edge computing is, the support needed to implement this technology, and the automation around this.
- Business Case: Once the awareness is established, then businesses should prepare a business case where every aspect of deploying edge computing is taken into consideration – such as the risk involved, security components, and even the benefits
- Compatibility: Once the edge compatibility aspect is defined, then it should be easy for the organization to operate on standard cloud and on-premise server
- Knowing the edge technology: Every edge technology is different because it is completely dependent on the used case. The hardware device selection is critical and must be in sync with the edge infrastructure.
Though it sounds to be highly advanced with some complex infrastructure requirements, in reality, edge computing is very simple and easy to deploy. The above-mentioned used cases give an idea of the already prevalent examples of edge computing devices. We are looking at the future where edge computing will be extended to segments like cloud gaming, smart homes (already implemented in pockets), and many more.